Get Paid with Manny Medina

AI-Native Go-To-Market Platform Replacing Sales Tech Stack, Sales Enablement & CRM

Manny Medina — Thu, 23 Apr 2026 15:30:00 +0000

In this episode 47 of the Get Paid podcast, host Manny Medina is joined by Jason Eubanks, Co-founder and CEO of Aurasell AI, to tell the story of how a restless operator turned into a founder taking on the biggest dragon in go-to-market tech: the CRM.

What You’ll Learn:

Why the 22-tool sales stack is broken
Why Jason’s sellers were only spending 28% of their time with customers
How to land wall-to-wall adoption
Why founders must raise large seed rounds to build true platforms

Jason Eubanks is Co-founder and CEO of Aurasell AI, an AI-native go-to-market platform revolutionizing how sales teams operate. With over 20 years of experience as a sales leader at industry-leading companies, including BMC, Twilio, Cisco Meraki, and Harness, Jason brings deep operational expertise to solving critical productivity challenges in sales.

Episode Resources:

Jason Eubanks on LinkedIn
Aurasell AI Website
Manny Medina on LinkedIn
Paid Website

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The Future of B2B Sales Is AI Agents, Not SaaS (Here’s What Comes Next) with Doug Landis

Manny Medina — Thu, 16 Apr 2026 15:30:00 +0000

In this episode 46 of the Get Paid podcast, host Manny Medina is joined by Doug Landis, Co-founder and CRO at StoryPath.ai, to unpack what’s really changing in B2B as SaaS gives way to agentic services.

What You’ll Learn:

Why storytelling is a teachable skill
How to transition from seat-based to outcome-based pricing
Why the “crisis of sameness” is destroying traditional go-to-market strategies
Why demoing your product early in the sales process masks discovery failures and kills deal momentum
The storytelling skill stack required to compete in agentic services

Doug Landis is Co-founder and CRO at StoryPath.ai, an AI-first platform designed to empower enterprise sales teams through narrative-driven selling. With over seven and a half years of experience in venture capital at Emergence Capital and a deep background in sales enablement and go-to-market strategy, Doug brings a unique perspective on how storytelling serves as the ultimate competitive differentiator in an increasingly commoditized market.

Episode Resources:

Doug Landis on LinkedIn
StoryPath.ai Website
Manny Medina on LinkedIn
Paid Website

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Why Clay Cut Its Own Revenue to Prove a Point About SaaS Pricing | Karan Parekh | Get Paid with Manny Medina

Manny Medina — Thu, 09 Apr 2026 15:30:00 +0000

Clay’s pricing change sent shockwaves through the go-to-market tech world. The company voluntarily separated data credits from workflow credits, published its internal memo for anyone to read, and openly acknowledged the move would cost them revenue in the short term.

It was a bet that aligning price to value would reshape how their customers and, eventually, the entire industry think about SaaS pricing.

In this episode of the Get Paid podcast, host Manny Medina sits down with Karan Parekh, Head of Finance at Clay, and one of the architects behind that decision, to walk us through the full arc of how it happened.

Why Clay Changed Its Pricing While Winning

A couple of years ago, Clay was primarily a data business. Customer enriched profiles across 150 aggregated vendors, exported the results, and moved on. Data credits made sense.

But the product evolved. The real value shifted to orchestration, qualifying inbound leads in real-time, routing prospects before they finished submitting a form, and automating research at enterprise scale. Customers started asking a reasonable question: why is data getting more expensive if what I’m actually paying for is the workflow on top of it?

“Why should data become twice as expensive if Clay got five times better?”

Clay spent a year talking to over a hundred customers and agency partners, benchmarking against orchestration platforms, and testing pricing models before making the split. They cut data costs by more than half and introduced a separate credit for platform activity, the action that actually creates business outcomes.

The short-term bet was explicit: revenue would decline. The long-term thesis was that if customers could find five or ten things to do inside Clay instead of one, the math would overwhelmingly favor the new model.

“The way we lose is if people come into Clay and just buy data. The way we win is if they find ten things to do.”

Two Credits, Not One

The team debated collapsing everything into a single credit type. Customers found it confusing. If a credit represents data, why is it also paying for orchestration? Splitting into data credits and action credits created transparency. Data credits function like a wallet with generous rollover. Action credits function like a capacity ceiling, refreshing monthly.

“Even if it can drive a little bit more buying uncertainty because now you have two beaters to think about, you now have way more transparency on what Clay is charging you, where we make money, and where you are saving.”

Selling Usage-Based Pricing to Enterprise CFOs

A year ago, Clay was mostly PLG. Today, the business is approaching an even split between PLG and enterprise. Enterprise buyers want predictability, and Karan’s team delivers it through tight scoping: defining the use case, approximating credit consumption, and giving the buyer a concrete number.

The first use case lands, works better than expected, and the expansion motion becomes consultative: hackathons, on-site sessions, introductions to other customers.

“People get promoted when they use Clay.”

The Cloud Pricing Analogy

Karan sees AI-era SaaS pricing converging toward cloud infrastructure economics. Storage is a low-margin commodity. You cover costs, but that’s not where you build a business. The value layer sits on top.

Today, most AI companies price tokens because they have to cover input costs. But tokens don’t represent value. Some workflows consume a few tokens and generate massive outcomes. Others burn through compute and produce nothing differentiated. The industry will eventually need two vectors: one for covering fixed costs and one for capturing value created.

“You eventually have to price the value you're bringing to a customer, not what it costs you to serve that product.”

System of Action, Not System of Record

Clay’s ambition isn’t to replace the CRM. Clay wants to be the system of action. Wherever your data lives, Clay pulls it in and helps you do something exceptional with it. Karan argues that Clay actually makes CRM data stickier by keeping it fresh and useful, rather than threatening the platforms that store it.

“Owning the data ourselves doesn't make the platform more powerful.”

The Printing Press and the Future of Go-to-Market

Karan pushes back on the narrative that AI will flatten go-to-market into pure automation. Before the printing press, the hard part was manufacturing books. Once manufacturing became trivial, the hard part shifted to having something worth saying.

AI will do the same to go-to-market: volume will explode, but the things that stand out will be driven by genuine creative insight with an increasingly short half-life.

“Your job will always be on the efficient frontier of what's coming next.”

Companies Mentioned

Clay
Salesforce
Snowflake
OpenAI
Anthropic
Gong

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The Pricing Shift That’s Breaking SaaS | Get Paid with Manny Medina

Manny Medina — Thu, 02 Apr 2026 15:30:00 +0000

As companies introduce credit systems tied to compute and AI functionality, customers push back, questioning why they should pay more for what feels like an extension of existing products.

In this episode of the Get Paid podcast, host Manny Medina and Rob Litterst from PricingSaaS speak to real operators in the field to unpack one of the toughest challenges in modern SaaS: transitioning from seat-based pricing to AI-driven, usage-based models.

They dive into the psychology behind pricing resistance, the risks of poor transparency, and a practical two-step strategy to introduce credits without breaking trust or losing revenue.

The Pricing Shift That’s Breaking SaaS

SaaS pricing used to be simple: charge per seat, scale with users, grow predictably.

That model is breaking.

As AI features become embedded into products, companies are being forced into a new reality: usage-based pricing tied to compute, tokens, or credits. And customers are not happy about it.

“We're already giving you hundreds of thousands to use the software as it is. Why should we pay more for this?”

That question sits at the center of the transition. And most companies don’t have a good answer.

Why Customers Push Back

From the customer’s perspective, the frustration is rational.

They were sold a product. They’re already paying a significant amount. Now, suddenly, core functionality is being repackaged as an add-on.

However, the resistance goes deeper than just price.

There are three hidden concerns:

Loss of predictability: Usage-based pricing is harder to forecast.
Lack of transparency: Unclear what credits actually deliver.
Perceived double-charging: Paying again for something that feels included.

“Credits create this potential limited liability or consumption. It's hard to forecast.”

This becomes a trust problem.

The Real Issue: Change Management

Most companies approach this transition as a pricing update.

That’s the mistake.

This is fundamentally a change management problem. You’re not just changing how you charge; you’re changing how customers understand value.

“The main one is the change management at the point of the customer.”

Customers need time to:

Understand the new model.
Experience the value.
Reframe what they’re paying for.

Without that, every pricing conversation turns into friction.

The Two-Step Transition Strategy

Instead of forcing customers into a new model overnight, the smarter approach is gradual.

Step 1: Introduce Credits Without Charging for Them

Bundle a set number of credits into the existing plan.

Position it as:

A reward for loyalty
Early access to innovation
A way to experience new value

“It’s gonna be included in your seat. We normally charge for this, but for you…”

This removes risk from the customer side while creating exposure to the new model.

Step 2: Monetize After Value Is Proven

Once customers have used the feature and seen results, the conversation changes.

Now it’s no longer, “Why should I pay more?”

It becomes, “How much is this worth to me?”

“And once the three months expire, then we're gonna talk about pricing and packaging that makes sense for your business.”

This flips the dynamic from resistance to negotiation.

Why Transparency Is Non-Negotiable

One of the biggest failures in usage-based pricing is opacity.

Customers don’t understand:

What a credit actually does.
How usage translates into value.
Why costs vary.

“The majority of the solutions out there being sold on tokens or credits or any variable usage have very little transparency as to what you're getting from those tokens and credits and usage.”

If users feel like they’re being charged for something invisible, trust erodes fast.

The companies that win will:

Clearly map credits to outcomes.
Show real-time usage.
Tie pricing directly to value delivered.

The Real Question

The challenge isn’t, “How do we charge more?”

It’s, “How do we help customers understand why this is worth more?”

Because in the end, pricing is about perceived value, trust, and timing.

And if you get those right, the transition doesn’t feel like a price increase.

It feels like an upgrade.

Companies Mentioned

OpenAI
Anthropic
GitHub
Monday.com
ServiceNow
Amazon Web Services
Snowflake
Twilio
SendGrid
Stripe
Datadog
Vercel
Replit

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He Built AI Agents Before Anyone Knew What to Call Them | Flo Crivello | Get Paid with Manny Medina

Manny Medina — Thu, 26 Mar 2026 15:30:00 +0000

In this episode of the Get Paid with Manny Medina, Flo Crivello, Founder and CEO of Lindy, shares the full origin story of one of the most ambitious AI products being built today: a proactive executive assistant that manages your inbox, prepares you for every meeting, and takes action on your behalf before you even ask.

Flo traces the journey from a 2022 meeting recorder to a no-code agent builder to the AI chief of staff executives are now replacing their human assistants with. Along the way, he opens up about losing talent through pivots, the dangerous middle ground between product and platform, and the model release that made him realize the guardrails he’d built were now the ceiling holding Lindy back.

Starting Before the Language Existed

Flo began working on AI agents in mid-2022. GPT-4 didn’t exist yet. LangChain didn’t exist. The word ‘agents’ wasn’t part of the industry vocabulary.

The insight came from experimenting with the GPT-3 API while building a meeting recorder. The team realized the model wasn’t just good at generating language; it could take actions.

“The GDP is not made of copywriters. It’s made of work.”

While the rest of the market rushed to build AI writing tools, Flo was quietly trying to build software that could actually do things.

The Leash Was the Product

Early agentic AI was far too unreliable to ship as a freeform system. Hallucination rates were unworkable. Function calling didn’t exist. The team resorted to having models write raw code to hit APIs, a fragile, error-prone approach.

Lindy’s solution was a structured canvas, similar to Zapier, where humans defined every step in a workflow and the agent filled in the blanks. It was rigid. But it worked.

“We called it keeping the agent on a leash. It buys you reliability, but it takes away flexibility.”

The model proved itself quickly. Within weeks of building on Rails, Lindy was automating complex workflows for a prominent VC firm that had doubted it was possible. Shortly after, a YouTube creator named MattVidPro discovered the product, and the inbound exploded, with most companies spending heavily to manufacture, and arrived for free.

The Platform Trap

As the agent builder grew, a harder problem emerged. Lindy had drifted into the space between a product and a platform, too opinionated for developers, too technical for end users.

Flo’s analogy is sharp: telling someone you’ve built the world’s easiest way to make their own cheeseburger at home doesn’t land, because people who want a cheeseburger want McDonald's.

“Don't be in the middle. Pick a lane.”

The realization forced a real decision: go hard toward developers and compete in the infrastructure space, or go hard toward end users and build something genuinely magical for people who are too busy to configure anything.

Lindy chose the latter. That choice sent them back to the original vision.

The Moment the Leash Became a Ceiling

Throughout all the pivots, Flo had been running Lindy as his own personal AI assistant, swapping in each new model as it was released and watching the experience slowly improve. When Claude 3.5 arrived, something changed.

End-to-end agents, fully autonomous loops with no human-defined steps, had always been the weakest part of the product. Suddenly, they worked. The structured workflows that had made Lindy reliable were now limiting what the agent could do on its own.

“Take me off the leash. I know what to do. I can do so much more for you.”

Lindy rebuilt around the vision Flo had been carrying since 2022.

What Lindy Actually Is

Lindy connects to your email and calendar, learns your context continuously, and acts before you ask. There’s no setup wizard. No flow to configure. It starts delivering value within minutes of connecting your accounts.

During a recent engineering interview, a candidate mentioned a referral. By the time the meeting ended, Lindy had found the LinkedIn profile, drafted a personalized outreach email, and sent Flo a text asking if he wanted it sent.

“I did not have to change much before sending it.”

The breakthrough surface turned out to be iMessage. What started as a feature became the core of the product, a text interface that lets busy executives give and receive information in the same seconds they’re glancing at their phone between back-to-back meetings.

“All I check is this. I check my phone.”

Where the Opportunity Is

For founders building now, Flo’s advice centers on a constituency most people are still underestimating: agents themselves.

Agents are becoming buyers. They need compute, billing infrastructure, memory, and tooling designed for non-human operators. The founders building that layer, not for developers, not for end users, but specifically for agents, are sitting on a wide-open opportunity.

“There are going to be infinitely more agents in the future. Focus on that constituency.”

Companies Mentioned

Lindy
Uber
Rippling
Figma
Intercom
Claude
ChatGPT
LangChain
E2B
Stripe
Superhuman
Copy.ai
Jasper

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The Pricing Change That Increased Revenue 60% | Sofiia Shvets | Get Paid with Manny Medina

Manny Medina — Thu, 19 Mar 2026 15:30:00 +0000

In this episode of the Get Paid podcast, Sofiia Shvets, CEO and Co-founder of Claid, explains why her company replaced traditional SaaS pricing with a credit-based model.

Sofiia shares the challenges of migrating customers, the benefits of usage-based monetization for AI products, and how the switch ultimately increased revenue per account by 50-60%.

The conversation also explores AI infrastructure costs, enterprise adoption challenges, and the future of agent-driven generative media.

When Subscription Pricing Breaks

Claid originally launched with a simple SaaS subscription model where users paid a monthly fee for access to generative media tools.

However, as the product expanded from three tools to more than twenty, pricing complexity exploded.

Different tools had different computational costs. Some customers only needed one feature. Enterprise clients needed flexibility to expand usage quickly.

“It was a monstrosity of the process.”

Hard limits across tiers made the product harder to use and harder to sell.

Why Credits Simplified Everything

Claid replaced subscription tiers with a credit-based pricing system.

Each operation now consumes credits based on its cost and complexity.

For enterprise buyers, this made pricing dramatically easier to understand. Customers could forecast usage, run experiments, and expand their workflows without renegotiating contracts.

“Here's the whole list of operations. They cost two, three, or five credits. The price of the credit is, I don't know, $1 or whatever. $5. Super easy. Easy math.”

Credits also allowed Claid to launch new tools without changing pricing plans.

The Short-Term Pain of Changing Pricing

Migrating to credits was not painless.

Existing customers initially complained about the introduction of limits where none existed before.

“We got a bunch of complaints from users.”

Some churn followed, but far less than expected.

New users, however, adapted immediately because credit pricing had already become common across AI tools.

The Result: 50–60% Revenue Growth per Account

Within three months of the transition, Claid saw a major shift.

Revenue per account increased by 50-60%.

The reason: the old pricing model had been limiting usage.

Customers could now scale their usage freely and purchase additional credit bundles when needed.

“It got more expensive, but it was more fair use.”

The new model aligned pricing with real product usage.

The Hidden Complexity of AI Monetization

Unlike traditional SaaS products, AI tools have real-time infrastructure costs.

Every request consumes GPU resources, and processing times vary depending on load and model complexity.

Claid operates a hybrid system:

Some models are hosted internally, others run through external APIs.

This makes margin forecasting far more complex than traditional SaaS.

“It’s hard to calculate, especially when you host models yourself.”

Advice to AI Founders

Sofiia’s biggest advice to founders building AI products:

Don’t obsess over getting monetization perfect on day one.

First, validate that users are willing to pay.

“Try to sell it in any way.”

Pricing can always evolve once product value is proven.

Companies Mentioned

Claid
Google
Black Forest Labs
Amazon
eBay
OLX
Lovable

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How AI Agents Are Saving Millions in Industrial Operations | Somya Kapoor

Manny Medina — Thu, 12 Mar 2026 15:30:00 +0000

The Real Challenge of AI Agents

The industry often focuses on building AI agents. But building them is the easy part.

What enterprises actually struggle with is adoption.

Agents need to integrate with real workflows. They need to be able to handle edge cases and operate within strict operational constraints. In industrial environments, an incorrect output isn’t just inconvenient; it can disrupt operations or create risk.

That’s why the future is far different from building general-purpose agents. It’s deploying domain-specific digital workers that understand business processes.

Digital Workers and Real ROI

The promise of AI agents becomes powerful when they connect directly to operational workflows.

In manufacturing and industrial supply chains, routine decisions like inventory replenishment or purchase order generation consume thousands of hours every year.

By deploying AI-powered digital workers into these processes, companies can automate operational decisions. While keeping humans in control of the overall system.

In one case, an oil and gas company implemented a digital worker to manage inventory replenishment and supplier orders.

The result? 90,000 hours saved annually, which is equivalent to $3 million in cost savings.

The End of RPA

For years, companies have relied on Robotic Process Automation (RPA) to automate repetitive workflows. But RPA was built for rigid systems.

It required developers, constant maintenance, and long deployment cycles.

Agentic AI changes that equation. Instead of automating individual steps, agents can reason across systems, adapt to context, and make decisions based on real-time data.

As Somya explains, the future of enterprise AI adoption is autonomous digital workers.

How Founders Should Think About AI Startups

For AI founders entering enterprise markets, Somya offers a simple rule: Don’t try to replace the entire system.

Instead, identify one business process where AI can deliver measurable value quickly. Land with that workflow.

Prove ROI. Then expand.

In industrial companies, especially, adoption happens when leaders can show clear operational improvements within a quarter. Once that value is proven, organizations are eager to deploy more industrial AI automation across other processes.

Why Industrial AI Is Different

Deploying AI agents in industrial environments is fundamentally different from deploying them in traditional SaaS workflows.

In support or marketing systems, an AI output that is “mostly correct” can still be useful. Speed often matters more than perfect accuracy. But industrial systems operate under a different standard.

When an agent creates a purchase order, schedules maintenance, or assigns work to a technician, the output must be deterministic and reliable. A wrong output doesn’t just create inconvenience; it can delay operations, introduce safety risks, or disrupt critical infrastructure.

That’s why enterprise AI in industrial environments requires structured workflows, strong guardrails, and deep domain expertise.

The Rise of Outcome-Based Pricing

Traditional SaaS pricing was built around seats. More users meant more revenue.

But AI agents change that equation.

When a digital worker processes purchase orders, manages inventory replenishment, or automates work orders, the value it creates isn’t tied to how many people log into the system.

It’s tied to what work gets done.

That’s why Somya is increasingly seeing enterprise deals structured around business outcomes like the number of orders processed, workflows completed, or operational hours saved.

This model aligns pricing directly with the ROI the technology creates. And for enterprise buyers, it makes the decision easier.

Companies Mentioned

IFS
SAP
ServiceNow
TheLoops
Microsoft
IndustrialX

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AI Native Companies Don’t Sell Seats - They Steal Users | Jacco Van Der Kooij

Manny Medina — Fri, 06 Mar 2026 10:19:00 +0000

If you’re building or leading a SaaS company, this conversation explores how the AI SaaS playbook is changing traditional go-to-market models. Jacco, Founder of Winning by Design, explains why product-led growth, real-time systems, and growth loops vs. funnels are becoming the dominant approach to B2B growth in the AI era.

Together, Jacco and Manny discuss why the traditional SaaS funnel is losing effectiveness and how modern companies are building self-reinforcing growth engines instead. They explore the SaaS expansion strategy, the shift from logo acquisition to user adoption, and how AI-native organizations stack growth loops to maintain momentum.

“There are no seat sales. That doesn’t exist. There’s user sales, and users have usage.”

The Death of Logo Worship

For years, SaaS companies optimized for winning logos.

Get the company. Hang it on the wall. Put it on the deck.

AI-native companies think differently. They optimize for users.

“Logos don’t bring in new logos. Users bring in new users.”

Jacco argues that disruption doesn’t start with logo churn. It starts with user migration. AI-native tools land with users first. Once usage consolidates, the logo follows.

Funnels vs. Growth Loops

Traditional SaaS growth is an open-loop system. It requires constant external input: more leads, more SDRs, and more spend.

AI-native growth is closed-loop. When growth creates more growth, the system compounds.

Jacco explains that nearly every fast-growing company today operates on at least one growth loop:

User-based loops
Expansion loops
Community loops
Viral loops

If you have to pay for every dollar of growth, you will eventually hit a ceiling.

The S-Curve and Stacking Growth

All growth follows an S-curve:

Slow start
Exponential phase
Plateau

Every S-curve eventually becomes a bell curve unless you stack another one.

That’s why companies like Dropbox and DocuSign continuously launch adjacent products. When individual-user growth saturates (often around 200k–300k users in B2B), churn dampens the exponential curve.

To sustain momentum, companies must stack new growth loops on top of old ones.

Velocity Is the Real Shift

What’s different now isn’t growth theory, but speed.

AI-native companies operate at a different clock speed. If win rates in Spain jump from 18% to 26%, a traditional SaaS company might respond next quarter. An AI-native company reacts in days.

The difference lies in infrastructure, real-time systems, automated feedback loops, and humans designing systems rather than manually steering every move.

“When things go this fast, humans are no longer the control stick. We design the system and let it run.”

The 50,000 User Thought Experiment

Consider a $100M SaaS company with:

$30k ACV
3,000 customers
50,000 users

If just 1% of users per month generate an opportunity signal, that’s 500 quality signals per month. 6,000 per year. Instead of sending SDRs to cold outbound lists, Jacco proposes redeploying them toward users:

Build advocacy
Create community
Enable propagation

The growth engine is already inside the customer base. You just need to activate it.

AI Is Not Stealing Logos. It’s Stealing Users.

The biggest misconception in SaaS today is that competition happens at the logo level.

It doesn’t.

AI-native tools enter at the user layer. They win individual adoption. They observe usage patterns in real time. They iterate faster. Then the logo shifts.

If you’re SaaS-native, you are not doomed. But you must:

Focus on the users, not seats
Understand your dominant growth loop
Upgrade to real-time infrastructure
Design propagation into the product

Because in the AI era, velocity compresses the S-curve. And the companies that achieve irreversible time dominance don’t just win first. They become almost impossible to catch.

Companies Mentioned

Winning by Design
Canva
Dropbox
DocuSign
Superhuman
Grammarly
Starlink
SpaceX
Boston Dynamics
HubSpot
Cursor

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S2E39: Stay, Go, or Slow: The Scaling Signals Most Founders Ignore | Mark Roberge

Thu, 26 Feb 2026 02:00:00 +0000

In part 2 of this episode, Mark Roberge, Co-founder of Stage 2 Capital, former CRO of HubSpot, Harvard Business School professor, investor, and author of The Science of Scaling, joins Paid’s Manny Medina to break down the real mechanics of scaling in an AI-fueled market.

They delve into execution, exploring how to decide when to accelerate, when to hold, and when to slam on the brakes. Mark also shares the Stay / Go / Slow model, founder versus VC misalignment, AI bubble dynamics, business model innovation, and why retention should be slide one in every board deck.

“I have a beautiful hack for you called the stay or go or slow model.”

The Anti-Annual Plan

Mark challenges one of the most sacred startup rituals: the annual plan.

“It’s so stupid that we build these annual plans when you're a $2,000,000 business and we abide by them like they’re scripture.”

Instead of blindly chasing a 2-to-20 growth promise, Mark proposes a quarterly decision framework agreed upon in advance with the board. After each quarter, you evaluate three signals:

Demand generation for health
Conversion performance
Leading indicator of retention

If all three are green, you accelerate.

If any are yellow, you hold the pace.

If any are red, you stop pouring gas and fix the system.

Most Founders Are Scaling at the Wrong Pace

According to Mark, roughly:

45% are going too slow
45% are going too fast
Only 10% are at the right pace.

Going too slow means the window closes. Going too fast means burn outpaces signal.

“If you burned a billion dollars this year and you’re not OpenAI, that’s probably too much.”

But burning a dollar is too little. The real skill is calibrating scale risk to context. A high-moat airport software company does not scale like Cursor or Day AI. Winner-take-most markets require aggression. Most-driven markets reward discipline.

New Logos Should Not Be Slide One

In today’s AI wave, Mark sees a dangerous pattern:

Pilot revenue labeled as ARR.
Experimental deployments treated as durable revenue.
Boards are obsessed with new logos.

“The first slide in your board deck should be your leading indicator of retention.”

Customer success must be a first-class citizen metric. Not logo count. Not headline ARR. Retention-leading indicators signal real value creation. Everything else is noise.

Are We in an AI Bubble?

Mark’s answer: yes. Signs of a classic bubble include:

Extreme valuation multiples
Extraordinary burn ratios
Overcapitalized first movers
‘Vibe revenue’ that looks sticky until renewals hit

On first mover advantage, Mark cites the broader pattern: fast followers win more often than first movers. The first mover wins roughly 35% of the time. The fast follower wins closer to 65%.

“I think the last two-year cohort will see the highest failure rate in startup history.”

At the same time, the breakout winners could define a generation.

Founder vs. VC Incentives

VCs have 20 bets. Founders have one.

Investors optimize for outliers. Founders optimize for life-changing outcomes. Some investors would rather see a company fail fast than grow steadily at 60% for six years and sell for $700M.

That tension fuels overscaling and unnecessary risk.

Business Model Risk Is the Startup’s Advantage

AI is forcing a rethink of monetization.

Per-seat pricing made sense in traditional SaaS. AI automates work. It compresses seats. The safe play is per-module. The bold play may be consumption or outcomes-based pricing.

Startups have an advantage: they can take business model risk. Incumbents can’t. Sales compensation plans, revenue expectations, and public market pressures trap incumbents in legacy structures.

Today’s Value Prop Won’t Win Tomorrow

One of the most strategic insights of the episode: the product printing money today will likely not be the long-term moat.

Mark references Amazon’s early focus on books as a wedge. Design big. Start small. Print money in phase one while building infrastructure for phase two.

If you build the future too early, the market isn’t ready. If you only optimize for what sells today, you lose the long game.

Companies Mentioned

HubSpot
OpenAI
Amazon
Slack
Notion
Cursor
Day AI
Siebel
ServiceNow
Harvard Business School

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S2E38: AI Hasn’t Changed: How to Scale Without Blowing Up

Thu, 19 Feb 2026 00:30:00 +0000

In this episode, Paid’s Manny Medina sits down with Mark Roberge, Co-founder of Stage 2 Capital, former CRO of HubSpot, Harvard Business School professor, investor, and author of The Science of Scaling, to break down what actually changes in go-to-market during an AI boom, and what absolutely does not.

Mark argues that while AI is accelerating workflows, it has not rewritten the laws of scaling. Human decision-making hasn’t changed. Retention still matters. Unit economics still matter. Hiring 17 reps overnight is still dangerous.

They go deep on product-market fit and why it’s not a feeling, the Stay / Go / Slow scaling model, founder vs. VC misalignment, systems of action vs. systems of record, and why the current AI cohort may see the highest failure rate in startup history.

“If you burned a billion dollars this year and you’re not OpenAI, that’s probably too much.”

First Principles Still Win

One of Mark’s central points is simple: AI does not change behavioral science.

“AI is not going to change the behavioral science of how humans make decisions.”

Much of AI 1.0 has been workflow streamlining, not workflow reinvention. The fundamentals of buyer psychology, sales process design, and value creation still apply.

What does change is leverage. Mark believes the first major unlock in go-to-market AI is increasing selling time.

“If you increase selling time from 25% to 75%, you triple X productivity right there.”

Product-Market Fit Is Not a Feeling

Most founders say they’re ready to scale when they ‘feel’ product-market fit. Mark rejects that entirely.

“Product-market fit is when you create customer value consistently.”

The metric? Retention. Specifically, net dollar retention is north of 100%. In early stages, you can’t wait a year to measure retention, so Mark pushes founders to define a leading indicator:

What usage behavior in month one predicts long-term retention?

Slack used 2,000 team messages.
HubSpot used three features adopted.
Notion used weekly engagement.

If 80% of new customers hit that indicator, you have product market fit. If not, scaling is premature.

The Stay / Go / Slow Model

Instead of locking into rigid annual plans, Mark proposes a quarterly decision framework. After each quarter, you evaluate:

Demand generation for health
Conversion and quota attainment
Leading indicator of retention

If all are green, go faster.

If some are yellow, stay the course.

If any are red, slow down and fit it.

“It's so stupid that we build these annual plans when you're a $2,000,000 business and we abide by them like they're scripture.”

New Logos Should Not Be Slide One

In today’s AI cohort, Mark sees a dangerous pattern.

Boards ask for new logos, founders report ARR growth, then pilot revenue gets labeled as ARR.

But value creation lags.

“The first slide in your board deck should be your leading indicator of retention.”

Customer success should be a first-class citizen metric.

Founder vs. VC Incentives

VCs have 20 bets. Founders have one.

Some investors would rather see a company fail fast than ‘skimp along’ at 6-% growth.

But a founder who builds a durable $700M exit instead of chasing a trillion-dollar dream may protect life-changing outcomes.

This tension fuels overscaling.

Are We in an AI Bubble?

Mark’s answer: yes.

Signs of a classic bubble:

Extreme valuation multiples
Extraordinary burn ratios
Experimental deployments counted as durable revenue
First movers overcapitalized

“I think the last two-year cohort will see the highest failure rate in startup history.”

At the same time, the winners may be generational.

Today’s Value Prop Won’t Win Tomorrow

The most strategic insight of the episode: The product printing money today is unlikely to be the long-term moat.

Mark references Amazon’s early book focus as a wedge.

You design big, start small, and build the infrastructure for what the market will want in five years.

If you build the future too early, the market isn’t ready. If you only optimize for what sells today, you lose the long game.

Companies Mentioned

HubSpot
Salesforce
Workday
ZoomInfo
OpenAI
Microsoft
Amazon
Slack
Notion
Cursor
Day AI
Harvard Business School
Boston Consulting Group

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Get Paid with Manny Medina is handcrafted by our friends over at: fame.so

S2E37: How ElevenLabs Scaled from 20 to 500: Building Growth Systems in a Crowded AI Market | Luke Harries

Wed, 11 Feb 2026 22:00:00 +0000

In this episode, Paid’s Manny Medina sits down with Luke Harries, Head of Growth at ElevenLabs, to break down how one of the fastest-growing AI companies in the world thinks about growth when everything is crowded, noisy, and competitive. Luke joined ElevenLabs when there were about 20 people. Today, it’s approaching 500, and pushing hard from best-in-class model to full-stack product company.

Luke and Manny go deep on why traditional marketing channels are the wrong mental model, how ElevenLabs treats launches as systems, and why video quietly became their highest-leverage growth input. They also unpack hiring mistakes, why onboarding should feel like a video game, and which classic startup roles are being squeezed hardest by AI-native teams.

“The overall approach we take at ElevenAds is to try and build growth systems. And so these are, like, treating each channel as its own system and really optimizing it.”

Growth Is a System, Not a Channel

At the core of Luke’s thinking is a framework that replaces vague channels with concrete systems.

“When we break down what actually is a growth system, there’s three main parts. There’s the actual system itself, so that’s the people, the checklist, the automations, the code. There’s your model for the system and the analytics, and then there’s the goal.”

Instead of asking how to incrementally improve performance, ElevenLabs sets aggressive output targets and works backward.

“Let’s say we’re only getting two meetings booked per month from webinars. We’re like, how do we go from two to 200?”

The work then becomes identifying and scaling the inputs that make that output possible.

“How do we max out every single one of those inputs?”

Why B2B Growth Feels Slower Than Self-Serve

Luke contrasts his background in self-serve growth with the realities of B2B.

“I come from much more of a self-serve high-volume game where everything can be an AB test. Everything now with B2B is much more like, Okay, over a quarter, we spend this amount of money, we do a shot this way, how did it work?”

In B2B, learning cycles are longer, and bets are bigger.

“Use your intuition, learn from what worked in the past, put your foot down hard, and give it your best shot.”

Why Video Became the Highest-Leverage Growth Hire

One of Luke's earliest and most unconventional growth hires at ElevenLabs was a motion designer. The reason? Leverage.

“We realized the biggest lever for these launches is just really good engaging videos.”

As an audio-first company, ElevenLabs couldn’t rely on text alone.

“You really need to show audio through video. You can’t just rely on text.”

After experimenting with agencies and contractors, the overhead became obvious.

“Contractors and agencies, there’s so much overhead. They need to learn the style, the brand.”

Bringing motion design in-house turned launches into a repeatable system instead of a scramble.

Treat Case Studies As Launches

Luke explains why most companies underutilize their strongest proof.

“Lots of companies, maybe you’d like to create a case study, but you don’t do that push in the launch.”

When ElevenLabs published a case study with Revolut, they treated it like a full launch moment, and it showed.

Hiring, Onboarding, and When It Doesn’t Work Out

The biggest lever for improving hiring outcomes is onboarding. At ElevenLabs, onboarding is designed to build momentum fast.

“We try to do onboarding where it’s kind of like a video game where you, like, start small tasks which build up, which have an impact.”

Early, direct feedback is non-negotiable.

“I try early on to give concrete feedback because then not only do you help shape the person to the company and get the best output, but also you’re building that muscle together if I'm going to give you feedback.”

From Model Company to Product Ecosystem

Luke describes ElevenLabs’ evolution in two phases.

“The first is the zero to one billion ElevenLabs.”

The next phase is depth and surface area: orchestration, integrations, and enterprise readiness.

“Then there’s the whole orchestration, then you do integrations with Salesforce, with HubSpot.”

Voice orchestration itself is technically complex.

“There’s speech to text, text to speech, and the hand taking, and that has to happen in microseconds.”

Final Advice

Luke’s advice for marketers and operators trying to stay relevant? You can just do things. Proof of work matters more than credentials. And for anyone hesitating:

“Jump headfirst. Don’t think too much about it.”

Companies Mentioned

ElevenLabs
Revolut
PostHog
Salesforce
HubSpot
Google Gemini
Anthropic
Lovable
Retool
Semrush
Deutsche Telekom
PagBank
Meta
Resend
Supabase
Stripe
Anthropic
Y Combinator
OpenAI

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Get Paid with Manny Medina is handcrafted by our friends over at: fame.so

S2E36: Meet the Man Who Left Siri to Build the Future of Voice AI

Thu, 05 Feb 2026 16:00:00 +0000

Nikola has been building voice AI since it was basically a lab experiment.

Cambridge → a startup that got acquired by Apple → then straight into the deep end with PolyAI: enterprise voice agents for customer support, CX, and the brutal reality of contact centers.

In this episode, Nikola and Manny go right at the parts people avoid:

why voice is still the dominant interface, why contact centers are structurally broken (attrition, no-shows, “hiring as strategy”), and why the BPO model collapses the second you try to automate it.

Then the real fight: monetisation.

They unpack why CCaaS incumbents move slowly, why “outcome-based” is trickier than it sounds in enterprise, and how PolyAI actually prices today — consumption vs outcome vs license, plus what happens when buyers demand predictability.

If you’re building agentic CX, selling into enterprise, or trying to price AI without nuking your margins — this one is for you.

🟢 Links & resources Get Paid.ai

→ Follow Manny Medina, Founder/CEO of Paid.ai on LinkedIn -

→ Paid.ai on LinkedIn

→ Subscribe to AgentTalk (Substack)

🟢 Listen on other platforms:

→ Substack

→ Apple Podcasts

→ Spotify

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Get Paid with Manny Medina is handcrafted by our friends over at: fame.so

S2E35: The Pricing Mistake 90% of AI Founders Make

Wed, 28 Jan 2026 21:00:00 +0000

💵Monetise your AI software with credits — start free on Paid!

SaaS pricing has lived in a fantasy world.

For 20 years, software got to charge per seat, print margins, and ignore what the real economy has dealt with forever: variable cost, capacity, and brutal competition.

This week on Get Paid, Manny Medina brings on Dimi, a pricing strategist from Simon-Kucher, to drag AI pricing back to reality. Hotels, airlines, retail, transportation… industries where pricing is a weapon, not a spreadsheet.

They break down what SaaS leaders are missing as agents show up: your cost is now variable, your “seat” metric can actually decline, and cost-plus pricing is a trap that forces you into a race to be the cheapest.

Dimi explains why usage pricing often incentives the wrong behaviour, why per-minute pricing is fundamentally broken for voice agents, and why outcome pricing is the right north star but way harder than people admit. He also gets into the uncomfortable truth founders avoid: the best B2B revenue is already “personalised pricing” and we just call it discounting.

If you’re building agents, trying to protect margins, or figuring out what to charge when your product has real variable cost, this episode will change how you think.

🟢 Links & resources Get Paid.ai

→ Follow Manny Medina, Founder/CEO of Paid.ai on LinkedIn -

→ Paid.ai on LinkedIn

→ Subscribe to AgentTalk (Substack)

🟢 Listen on other platforms:

→ Substack

→ Apple Podcasts

→ Spotify

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Get Paid with Manny Medina is handcrafted by our friends over at: fame.so

S2E34: AI Pricing Masterclass with HubSpot Founder Dharmesh Shah

Wed, 21 Jan 2026 00:00:00 +0000

💵Monetise your AI software with credits — start free on Paid.ai.

Dharmesh Shah didn’t just build a SaaS company — he helped shape the modern SaaS era.

As co-founder of HubSpot, he took the company from zero to public and played a central role in defining how software is built, sold, and priced. Now his focus is on what breaks when AI agents stop assisting humans and start doing the work themselves.

In this episode of Get Paid, Dharmesh joins Manny Medina for a direct conversation about the next platform shift — and why it’s closer to the dawn of the internet than another product cycle. Manny is the host of Get Paid and the founder/CEO of Paid.ai, focused on how AI companies price, package, and monetise agent-driven products.

They unpack why reasoning models changed everything, why “AI will kill SaaS” is the wrong question, and why the real disruption is the abstraction layer moving up. From the limits of vibe coding to why focus still beats building everything yourself, the conversation goes straight at the uncomfortable decisions founders are avoiding.

Dharmesh also shares how HubSpot is making the transition for real: throwing out the roadmap, resetting parts of the culture, and running HubSpot Next like a startup inside the company to build agent-native businesses that don’t fit neatly into the core org.

On monetisation, he’s blunt: why seats still matter, why credits are inevitable, why outcome-based pricing isn’t always the right answer — and what happens to software economics when agents replace work instead of enabling it.

If you’re building AI products, rethinking pricing, or wondering whether your SaaS model survives an agent-first world, this episode will challenge your assumptions.

🟢 Links & resources

Try Paid.ai

Follow Manny Medina, Founder/CEO of Paid.ai on LinkedIn.

Paid.ai on LinkedIn.

Subscribe to AgentTalk (Substack)

🟢 Listen on other platforms:

Apple Podcasts

Spotify

Youtube

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Get Paid with Manny Medina is handcrafted by our friends over at: fame.so

S2E33: Stop charging for access. Start charging for results.

Fri, 16 Jan 2026 00:00:00 +0000

Dan Griggs has seen multiple platform shifts up close. From taking Sitecore through a private equity transformation to leading finance at Intercom, he’s spent his career navigating what happens when business models break and how to rebuild them.

In this episode of Get Paid, Dan joins Manny to unpack one of the biggest shifts in software right now: the move from seat-based SaaS pricing to outcome-based pricing driven by AI agents.

Dan walks through Intercom’s decision to launch Fin, the AI agent for customer service, and why charging per seat stopped making sense once software started doing the work itself. He explains how Intercom became one of the first companies to price by outcomes, why they landed on 99 cents per resolution, and what that shift meant for margins, sales incentives, procurement conversations, and internal operations.

The conversation goes deep on the real economics of AI agents: how resolution rates affect margins, why simplicity beats precision in pricing, and what breaks when companies try to apply the old SaaS playbook to agent-driven systems. Dan also shares how Intercom brought sales, support, and customer success along for the ride, and what founders and CFOs consistently underestimate when making this transition.

If you’re building AI agents, thinking about outcome-based pricing, or trying to understand how software economics change when AI does the work, this episode is for you.

If you like this podcast, please subscribe on YouTube, Spotify, Apple Podcasts, or wherever you get your fix.

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Get Paid with Manny Medina is handcrafted by our friends over at: fame.so

S2E32: You're going to get usurped by an Al company

Fri, 09 Jan 2026 14:00:00 +0000

Maria Colacurcio built Smartsheet into a Pacific Northwest giant. Now, as CEO of Syndio, she's betting the company on a pivot from SaaS to agentic AI. And she's not looking back.

In this episode, Maria walks Manny through the launch of Syndi, an AI pay expert that transforms how companies make compensation decisions. Instead of annual pay equity audits that send CFOs scrambling for remediation budgets, Syndi embeds fairness as a constraint in every offer, every promotion, every pay decision. The result: companies save millions in payroll waste while actually improving equity outcomes.

Maria is candid about what "burning the boats" actually looks like. She discusses the internal communication challenges of asking employees to commoditize their own product, why some attrition is inevitable during a pivot, and how she keeps her board aligned when engagement scores are dropping and Blind is lighting up. Her solution to board prep? A custom GPT loaded with her deck, messaging, and each board member's particular interests, so meetings become strategic conversations rather than rabbit holes.

The episode also covers TD SYNNEX's early adoption of agent-powered HR, why the EU Pay Transparency Directive will force multinationals to explain their pay decisions, and how Syndi tracks the network effect of compensation choices over time. Revealing, for example, that 28% of "ex-OpenAI premium" hires leave as non-regrettable attrition. For any CEO wondering whether to bolt AI onto existing systems or rebuild from the ground up, Maria's answer is clear: your faster, more early-stage competitors will quickly overcome you if you don't go native.

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Get Paid with Manny Medina is handcrafted by our friends over at: fame.so

S2E31: Behind the code: meet our engineers

Fri, 19 Dec 2025 00:00:00 +0000

This episode of Get Paid is a little different.

Instead of bringing on a guest, Manny sits down with the engineering team at Paid to talk about what it’s actually like to build an AI agent company in year one. No slides, no polished narratives. Just the people building the product and deciding what actually ships.

The team talks through what they’re working on day to day: how Paid handles billing and cost management for agents, why the SDK is designed to stay flexible, and how features like credits, Blocks, and self serve have evolved based on how customers are actually using the product. They share examples of where customers pushed the system in ways they didn’t expect, and what that forced them to rethink.

They dig into what breaks once agents leave demos and hit production, and how they handle accuracy, evals, and human handoffs.

The episode looks ahead to what’s coming next, what the team is excited to build, and what they think matters most as AI agents move from experiments to systems companies rely on.

If you’re building with AI agents, thinking about adopting them, or just curious what’s happening behind the code, this episode gives a clear view of how a team is approaching it in real time.

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Get Paid with Manny Medina is handcrafted by our friends over at: fame.so

S2E30: Building a company is a team sport | Ariel Harmoko (Artifact AI)

Fri, 12 Dec 2025 14:00:00 +0000

Ariel Harmoko went from three-time national racing champion to Formula 3 driver to Cambridge's youngest ML researcher, all before turning 18. On this episode of Get Paid, he tells Manny how he built Artifact AI, an accounting automation platform that's taking on a $600B industry with a two-person team and a radically different approach to vertical AI.

Ariel breaks down exactly how he landed his first customers: cold-calling accounting firms he found on the Xero marketplace, armed with nothing but a Figma prototype. He explains why Artifact positions itself against offshore BPO providers rather than software competitors. And why that lets them charge $30K-$200K contracts instead of SaaS prices. The conversation gets tactical on pricing, go-to-market, and why accuracy matters more than speed when you're building AI that handles other people's money.

The episode closes with a sharp take on where the industry is headed. Ariel argues the billable hour is dying, the Big Four are vulnerable, and the next generation of accounting giants will be AI-first firms that never hired bookkeepers in the first place. His advice to founders: build your own evaluation infrastructure, don't outsource your accuracy, and don't be afraid to charge what a human employee would cost.

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Get Paid with Manny Medina is handcrafted by our friends over at: fame.so

S2E29: I set up a series A inside my growth stage company | Matthew Scullion (Matillion)

Fri, 05 Dec 2025 14:00:00 +0000

With over $100 million in recurring revenue, data productivity company Matillion was a thriving, established enterprise. Yet CEO Matthew Scullion spotted a fundamental threat on the horizon. In this episode of the GetPaid podcast, Matthew tells host Manny Medina why he pivoted Matillion’s focus, preemptively, before the rising tide of AI disrupted both their data engineer user base, and the product itself.

Matillion’s response is Maia: an AI‑powered “agentic data engineering team.”

To build Maia, Scullion assembled the “Maia A‑Team,” a small, multi‑functional startup within the larger organisation. Modelled after a Series A company, the A‑Team favoured agility and short feedback loops over the rhythms of a growth‑stage business.

This approach helped quickly prove the concept, learn new go‑to‑market motions, and validate the product through a lighthouse programme with key customers. In this episode, Scullion shares the conviction that came from seeing the technology work. The Maia pivot required rethinking the company’s core assumptions and structure, but ultimately delivered validation at speed.

Commercially, Maia shifts Matillion from selling incremental tools to practitioners, to delivering greater enterprise value to executive buyers such as CDOs and CIOs. That unlocks larger budget pools often reserved for BPO, consulting, and human capital. Scullion also explains how zero‑dollar contracts helped Matillion partner with customers early, securing critical validation and public success stories ahead of launch.

Matthew’s story is a clear reminder to turn the “fear” of disruption into a focused strategy. As AI and agentic solutions reshape how business gets done, Matillion’s journey offers practical lessons for leaders on the edge of inevitable change.

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Get Paid with Manny Medina is handcrafted by our friends over at: fame.so

S2E28: SaaS needs a strong AI component | Dave Kellogg (Balderton)

Fri, 28 Nov 2025 14:30:00 +0000

In this episode of Get Paid, we speak to Dave Kellogg about the future of the software industry, with a specific focus on the shift from traditional SaaS models to AI and outcome-based pricing. Kellogg, a seasoned CEO, founder, Executive in Residence at Balderton Capital, and CFO Whisperer, with a background of leadership at Host Analytics (now Planful), MarkLogic, Salesforce.com, and Business Objects, shares his expert insights on the three laws of pricing. He emphasizes that while delivering higher value justifies raising prices, the maximum price is always capped by customer perception of value and the competitive landscape. Together, we explore the inherent challenges and opportunities of implementing outcome-based pricing, particularly the need for predictability, and how creative deal structures, such as including rollover credits and multi-year contracts, can reduce customer friction and increase adoption.

We also talk about the operational implications of this transformation, with particular focus on the complex issue of sales commission structures in an outcome-based environment. Kellogg evaluates the balance between incentivizing initial sales and aligning variable compensation with actual value delivery, suggesting a model that pays part upfront and connects the rest to successful customer outcomes over time. We also explore the changing financial metrics for AI-native companies, where rising computation costs might temporarily reduce gross margins. Kellogg challenges the common focus on margin percentage by highlighting that dollar-value gross profit is the key measure of business value and predicting that efficiency improvements in AI models will eventually lead to healthier gross margins.

Finally, we discuss the major shift in talent acquisition and go-to-market strategies. We analyze the rise of the "Forward Deployed Engineer" (FDE) as a key role in product development, especially for early-stage, AI-driven companies where the product is still being heavily refined based on real-world use cases - a necessity in a platform-focused world. Kellogg recommends that new AI startups focus on hiring a small, elite team of AI-native engineers and seek go-to-market hires with proven experience in navigating competitive, greenfield markets. He often favors those who excel in aggressive, "uncomfortable" sales environments over those who are comfortable only with established market leadership.

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Get Paid with Manny Medina is handcrafted by our friends over at: fame.so

S2E27: The LLM inflection point | Nicolas Sharp (Attio)

Fri, 21 Nov 2025 14:00:00 +0000

Manny Medina is joined by a guest who is crushing it in the world of sales technology: Nicolas Sharp, Co-founder and CEO of Attio, the AI-Native CRM. Attio's rapid growth with over 5,000 paying customers is a masterclass in strategic pivoting and catching a generational technology wave. Nicolas shares Attio's powerful growth story, which began with the difficult decision to abandon a niche CRM built for investors. They realized this strategy was a "trap" and chose to "completely start again" to capitalize on the massive technology shifts created by No-Code tools and the rise of LLMs—an inflection point that rendered old software playbooks (like the ones used by Salesforce and HubSpot) obsolete.

Attio's core thesis is that the legacy CRM industry presented a "false dichotomy" between powerful, complex systems (Salesforce) and simple, limited ones (HubSpot). Attio solves this by arming the "builders" of the modern go-to-market world—the hyper-growth, PLG-focused companies—with a CRM that scales power without sacrificing usability. Their vision is built on the belief that the traditional, linear sales process is dead. Today’s non-linear buyer’s journey requires a new, AI-native approach: Attio captures high-resolution data from every customer interaction to enable deep automation, eliminating the manual data entry that has plagued sales teams for decades.

This forward-looking strategy is evident in their new Marketplace where the biggest bet is on extensibility. Instead of getting bogged down in the "death march of long-tail features," Attio is empowering customers to configure and customize their CRM using AI-generated JavaScript code. This allows them to quickly meet highly specific business needs that are beyond any single vendor's roadmap. Nicolas also provides insight into the operational strategy of running a global competitor from London, leveraging a hybrid executive team split between the UK and the US. Their revenue model ensures growth scales with customer success by balancing an accessible seat-based entry price with consumption-based credits that unlock advanced automation.

This conversation is an essential listen for anyone who believes the old playbooks are broken and wants to understand the future of go-to-market, underscoring:

The death of the rigid, legacy sales pipeline model in the face of non-linear buying journeys.

The necessity of high-resolution data and AI-generated code to enable ultimate CRM automation and configuration.

Why a hybrid revenue model that combines seat-based and consumption-based pricing is essential for scaling with modern, efficient customers.

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REPLAY - Pat Grady (Sequoia) - What actually works in AI startups

Fri, 14 Nov 2025 14:30:00 +0000

In a recent deep dive with Sequoia Capital partner Pat Grady, he shared surprising insights about what separates winning AI companies from the rest, and challenged conventional wisdom about AI moats, pricing models, and what investors truly value.

Our favorite takeaways:

* Building AI companies is just building a company. It’s 95% the same and people problems still dominate

* Trust is the critical design pattern most AI companies miss. Users need to see how you arrive at your results

* Most AI products achieve 80% functionality quickly, but the final 20% takes 5-10x longer and is what builds actual trust.

* The greatest moat in AI isn't data or tech - it's founders with relentless execution.

Pat also added some extra wisdom that we appreciate:

* The "data flywheel" appears in 100% of AI pitches but only 1% of companies actually demonstrate it works - Pat demands evidence, not theory

* AI pricing will standardize around outcome-based models with huge variation - the most successful companies think about both "input" (work done) and "output" (value created)

* For investors, negative gross margins are acceptable in early AI companies because token costs are dropping 99% and multi-tenancy is becoming more accepted

* Domain-specific AI products that build real trust can carve out defensible positions against foundation model providers in vertical markets

* The most successful AI companies avoid "vibe revenue" (temporary excitement) by focusing on engagement and retention using consumer internet metrics even for B2B products

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit agenttalk.substack.com

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S2E26: The $2.7B Agent Tax Crisis | Paid x Commenda Study

Fri, 07 Nov 2025 13:00:00 +0000

Most AI founders are building in a tax gray zone they don’t even realize exists. In this episode, Paid’s Manny Medina and Arnon Shimoni sit down with Commenda’s Spencer Schneier and Sam Suechting to unpack their landmark study on AI agent taxability, which is the first of its kind to test how U.S. states classify AI agents under sales and use tax law.

The takeaway: how you structure your AI business can be the difference between being taxable in 22 states or just 4.

“Productivity has shifted from labor to capital. Tax systems haven’t.”

Paid’s research with Commenda reveals a $2.7 billion hole forming in state and federal revenues as AI agents replace taxable workers but escape existing tax categories. Two companies offering nearly identical automation services can face totally different tax bills, depending on whether they’re classified as software or managed services. The group explores what that means for founders, policymakers, and the future of work.

They trace the historical parallels: it took 25 years for e-commerce to become taxable and warn that governments will face the same lag with AI.

“It’s not a loophole,” Arnon Shimoni says. “It’s a blind spot.”

The conversation moves fast: how outcome-based pricing supports tax exemption, why Private Letter Rulings (PLRs) are the new shield for AI companies, and what founders can do right now to structure their revenue legally and defensibly.

This is the episode for anyone building AI products that do work. Because the difference between “software” and “service” isn’t just semantics but it’s your tax bill, your margins, and the next $2.7 billion question.

Read the full report: https://paid.ai/blog/ai-agents/the-2-7-billion-agent-tax-crisis-first-ever-study

Companies Mentioned

Paid
Commenda Technologies
Amazon
Wayfair
Nvidia
OpenAI

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S2E25: We had 90 days to ship or shut down | Eric Simons (Bolt)

Fri, 31 Oct 2025 13:00:00 +0000

Eric Simons spent seven years building StackBlitz (now Bolt.new), a cloud IDE that millions of developers loved but almost nobody would pay for. After raising $22 million from Insight Partners in 2022, the company spent a year building enterprise features for customers who seemed excited. By launch in 2023, those customers had disappeared. The 2021 buying mania had created false demand everywhere.

"Everyone was buying everything. By the time we delivered it, we turned around and they just weren't even there. They were not interested. So there was false demand effectively."

With 18 months of runway and no clear path forward, Eric faced the hardest moment of his career: layoffs, followed by one final 90-day bet on a product called Bolt.

The 90-Day deadline saved them

Eric laid off seven or eight people and told the remaining 15-person team they had 90 days to ship Bolt before the next board meeting. They'd gotten a sneak peek at upcoming Anthropic models that solved problems they'd hit earlier. If Bolt didn't work, they'd start winding down the company.

"We barely got it online in 90 days. We only made it because we had no choice."

They launched with a single tweet on October 3rd, 2024. Day one: $60K ARR. Day two: $80K. Week one: $1 million. Month two: $4 million to $20 million ARR.

"I've been doing startups for 15 years. I've never seen anything like it. Neither had anyone else I talked to."

The customer nobody expected

Bolt thought they were building for developers. Within weeks, they realized their paying customers were product managers, designers, and non-technical founders at companies.

"PMs' jobs have been to write JIRA tickets, assign them to developers, and hope they actually implement it. Now they write the same spec, hit enter in Bolt, and it's done in 60 seconds instead of six business days."

Bolt isn't turning PMs into programmers. It's making them exponentially better at their actual job. Companies ship in one-tenth the time because engineers review AI-generated code instead of building trivial UI changes from scratch.

The Twitter hackathon that cost $100K and brought in millions

A tweet changed everything: "If I was Bolt's CEO, I'd throw the world's largest hackathon." Eric replied asking about prize pool size. Within two hours, they had $1 million committed from sponsors. Bolt kicked in roughly $100K.

"130,000 people signed up. Everyone got free domains through Entry. Everyone used Supabase. ROI was extremely positive. It was probably our best marketing event ever."

Why Windsurf had to sell

When Windsurf sold to Cognition, Eric wasn't surprised. Cursor was doing 5-10X Windsurf's revenue. At that scale, the gap becomes impossible to close.

"Once the flywheel starts like that, it's just harder. If you're going up against a competitor with substantially more distribution, crossing that chasm isn't realistically possible. So you find someone with even MORE distribution than your competitor."

The pain tolerance that built Bolt

Eric's early career included living in an AOL office building and eating at frat houses. He compares it to Navy SEAL training: constant discomfort that builds tolerance for the startup grind.

"A lot of what they train you to do is to be wet and cold all the time. They don't get discouraged by that stuff because it's normal. That's your only move as an entrepreneur: turn over all the stones and be intellectually honest about whether you see a path."

Companies Mentioned:

StackBlitz
Anthropic Claude
Cursor
Windsurf
Cognition
Devin
OpenAI ChatGPT
Figma
Wix
Replit
Lovable
Microsoft
GitHub
Gartner
Insight Partners
Paddle
Supabase

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S2E24: NetSuite is where ERPs go to die. We're building the escape hatch | Jonathan Sanders (Light)

Fri, 24 Oct 2025 09:30:00 +0000

We invited Jonathan Sanders, CEO and founder of Light, fresh off raising their $30M A round and serving some of the fastest-growing companies in the world, to share why ERP migrations are universally hated, how his company turns 18-month implementations into one-day onboardings, and why finance teams are becoming technical writers without even realizing it.

Jonathan and Manny explore the death of the traditional ERP category and the birth of smart finance platforms. From working with OpenAI months before ChatGPT launched to discovering the "student assistant" mental model that shaped Light's entire product philosophy, this episode maps out the future where finance teams configure systems by writing policies in plain English instead of paying consultants $200K to write scripts nobody understands.

"Think of it as a student assistant that works for you 24/7. The clearer you are in your instructions, the better a job it will do."

The Migration Pain That Created Light

Jonathan lived through two nightmare ERP migrations at Pleo and Juni. Each took 12-18 months, cost over $200K, and required constant consultant babysitting. At Outreach, Manny's board literally told leadership not to migrate because it would be a massive waste of time with zero ROI.

"It's like watching paint dry and getting f****. There's nothing enjoyable about it."

When Jonathan decided to build a better way, investors told him it would take 10 years and $100 million. He built it in 2 years instead.

The OpenAI Insight That Changed Everything

Before ChatGPT launched, Light was working directly with OpenAI's VP of Product:

"He said think of it as a student assistant. It won't always get it right, but the clearer your instructions, the better the job it'll do."

Finance teams write 2-4 page documents in plain English explaining their accounting. The AI uses those policies to classify transactions and handle exceptions. No consultants. No scripts. Just instructions you'd give any new hire.

The $200K Implementation Racket Is Over

Implementation consultants were brutally honest with Jonathan:

"If I can't sell a six-month project, I need to sell a project every week just to make payroll. Your interests are completely misaligned with your core client."

Light eliminates this entirely. No implementation fees. No consultant dependency. By design, they can't build the traditional channel ecosystem because if they depend on partners for go-to-market, they can't build self-serve products.

The New Finance Skill: Writing, Not Coding

"The ability to clearly articulate business processes and codify them into words. That's a new skillset coming out across all applications."

Finance teams are becoming policy writers. They document workflows in plain English. The AI executes it. This replaces sending instructions to BPO teams in India or calling consultants to modify scripts.

Software Should Defragment Itself

"Nobody goes and deactivates unused tax codes. But if it does it for you, the system becomes leaner."

Light is building self-healing software that automatically identifies unused accounts and optimizes itself. Like disk defragmentation, but for your entire finance operation.

How Light Hunts for Customers

Light hunts for trigger events on LinkedIn: New CFO hired, scaling globally, hit 300-400 employees, funding announcements. The fastest deal? Saturday inquiry, signed Wednesday.

"Nobody migrates ERPs for fun. They only do it when they have no choice."

Companies Mentioned

Light
NetSuite
SAP
Oracle
Microsoft
Salesforce
Workday
HiBob
Deel
Rippling
Xero
QuickBooks
Chargebee
Zuora
HubSpot
OpenAI
Pleo
Juni
Outreach
Accenture

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S2E23: Building Paid: $31M Raised on Confidence, Not Fear (Manny Medina, Raj Dosanjh)

Fri, 17 Oct 2025 10:00:00 +0000

Manny and Raj sit down to celebrate closing Paid's $21M seed round just 10 months after starting the company in an Airbnb with four people. This is your inside look yet at what it takes to build a company at AI speed. They unpack why Manny took a high valuation he's confident he can deliver on, how they closed the round with a 60% working demo, and the fuckton of arguing that almost tore the cofounders apart.

The valuation question

"I can sell 100 million of anything. So I'm not scared of 100 million. If the evaluation was 300 or like 250, I would be super scared. 100, I can sell 50, I can sell 100 of whatever. I'm not scared of that number."

Manny explains why they took a valuation between $100-200M when everyone warned them it was too high. Sales is a game of confidence. He didn't have the confidence for higher, but he's not scared of this number. They got the term sheet when their product was half demo, not more. The key insight is knowing which bar you can actually clear.

From fear to winning

The shift from Outreach to Paid represents a fundamental mindset change. At Outreach, survival mode drove every decision. At Paid, with $31M in the bank ($10M pre-seed + $21M seed) for 11 people, they have time to design the future they want to build. Raj shares how his last startup died because he avoided fighting. Now he's learned that hard conversations with experienced cofounders create purer relationships. Manny reveals the marriage lesson that changed everything: give each other grace, walk away when heated, and come back to the same problem with fresh perspective and no assumptions. Be a goldfish. Have a shorter memory on the shit that bothers you.

The Palantir playbook

Raj brings forward deployment to Paid. Every single one of the engineers has been deployed to customers and nobody died. The traditional setup has customer research, product managers talking to customers, feeding devs tickets. That's incredibly low bandwidth. An engineer being there, talking to customers, contains everything in their head with zero information loss. Big secret: engineers can talk to people. They can figure this out. They don't need to be locked in a cupboard.

Why Winning Is The Only Metric

Raj doesn't consider the time somebody spends building something as precious. He considers winning precious. Getting a customer precious. The time you take to iterate is just reps. At Outreach, they considered developer time the most valuable thing. In reality, winning is the most valuable thing. This creates different behavior. When team member Atta volunteered to redeploy customers from three months ago because the technology is significantly better now, Manny knew they had something special. No public company does that. Salesforce, ServiceNow, Workday give you code to get you over the line and you're stuck with it. They don't give a fuck about you. Paid operates from duty and pride, like a doctor with a Hippocratic oath. We found new science and we're going to give it to you whether you like it or not.

The UK vs US Mindset

Raj from Coventry gets asked by school friends: why are you doing this when you had such a good job? Manny nails the difference between US and UK entrepreneurship. In the US, people celebrate the ability to sell your way into financial independence and doing whatever you want with your life. Why is getting a job the bar for a university student? How about starting something? Making money? Financial independence? The UK has a class system and a culture where it's not cool to be successful. You want to be like everyone else, not upper class.

But Raj sees Paid as a mechanism to get Britain building again. Not cars, that ship sailed to China. But in a world where you can talk to an AI and it builds something, if you have a real problem, you have the opportunity to sell it. The positive vision of AI versus the dystopian version that robs everyone of agency.

Between Seed and Series A Is Everything

"Whatever you did in the between seed and A is what you're locking in. That's a time of defining how straight the tree is gonna grow. If you ended up crooked, just to put points on the board, and you try to grow crooked by putting more points on the board, you screwed it."

Not quid pro quo deals, everyone does that. It's about sales efficiency, technical debt, operational debt, culture in general. Are you devoting enough time to getting it right? Culture is like being a gardener. Making sure the soil is ready, pulling weeds, giving plants water and sun so they grow. Eventually you step back from fighting and make sure your garden is flourishing.

That's leadership.

Companies Mentioned

Paid
Outreach
Palantir
Salesforce
ServiceNow
Workday
Replit

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S2E22: "You're Not Buying Software, You're Hiring a $1K Employee" (Amjad Masad, Replit)

Fri, 10 Oct 2025 13:00:00 +0000

We invited Amjad Masad, CEO and founder of Replit, fresh off raising $250M at a $3B valuation and hitting $150M ARR, to share why point solution model is dying, how his own HR person built their org chart software in 3 days (killing the need for expensive vendors), and why consumers still don't understand they're hiring employees, not buying software.

Amjad and Manny explore the resurgence of the small entrepreneur enabled by AI agents. From the YouTuber doing Upwork arbitrage with Agent 3 (45 minutes, $25, full app delivered) to the VC CFO building software for other VC CFOs, this episode maps out the future where domain experts build hyper-specific micro-SaaS instead of Silicon Valley creating billion-dollar point solutions. Amjad also gets refreshingly honest about VCs leaking his numbers twice, why management fees attract short-term thinkers, and how Replit does seasonal intensity with 12/12/7 sprints during launches but sustainable hours the rest of the year.

"Agent 3 is an employee, it's not a piece of software. So instead of paying a hundred thousand dollars for a worker, you pay a thousand bucks."

The Pricing Model That Confuses Everyone

Amjad reveals why Replit's token-based pricing creates sticker shock for consumers but makes perfect sense to businesses. A doctor saved $99,700 building an app quoted at $100K. Someone in government built an education app quoted at $10M for a few hundred dollars.

"The difficult part is how people think about value here. When people come to Replit, they come with a mindset of I'm buying a piece of software. In reality, you're hiring a piece of software."

This mental model gap between technology capability and consumer expectations is the fundamental challenge every AI company faces right now.

How Agent 3 Actually Works Under the Hood

Most people don't know this, but Agent 3 isn't a single agent. Amjad pulls back the curtain on the multi-agent architecture that makes it the most advanced system in market:

"Agent 3 is actually a team of agents. There's an agent that is called the architect that continuously monitors the architecture of the system and gives feedback to the main agent. You can see the architect saying like, I don't like this is not very secure. And then the other agent kind of arguing back with that. It was like, it's fine for now."

Users can watch these agents debate in real-time. Some love it because it modernizes their codebase. Others hate it because they're spending money while the agent refactors instead of shipping.

The 3-Day Death of Generic SaaS

Amjad shares what happened inside Replit that proves point solutions are doomed:

"One person on our very small HR team didn't find the exact org chart software that they want to use from vendors. And it was like costing tens of thousands of dollars. And she built it over three days in Replit. And that's what we use right now."

When a single HR person can build exactly what they need in 3 days instead of paying $50K/year for something that almost works, every org chart SaaS company should be terrified.

The Silicon Valley Point Solution is Dead

Amjad's most controversial prediction challenges the entire VC-backed scaling model:

"I think the idea of Silicon Valley companies being able to create these point solutions and scale some to billions of dollars of revenue, I think that's going away."

The future belongs to domain experts building hyper-specific software. A CFO at a VC firm built software specifically for other VC CFOs. Someone who worked HR in hotels can now build HR software for hotels in Istanbul.

"If you have domain knowledge in your head, you can put that into a piece of software and sell it. Maybe that's like a $10 million business, but it is not like a think that's going to IPO."

From Upwork Arbitrage to Micro-SaaS Millions

Amjad maps out the new entrepreneurship ladder enabled by agents:

"We're seeing a YouTuber, name is Nick Conley, after Agent 3 came out, went to Upwork, copy pasted an entire job, put it into Replit at 45 minutes, $25, and he had a full app. Now he could kind of go back to Upwork, sell it back for a couple thousand dollars."

That's level one arbitrage. Level two is building defensible micro-SaaS using your domain knowledge. Level three is making small businesses radically more efficient without hiring.

The Societal Vision: Fewer Billionaires, More Millionaires

Amjad sees a fundamentally better economic structure emerging:

"I think there's less billionaires and a lot more millionaires. I think that might be a good thing. The conglomeration of the world where things are centralizing into a few big companies that are government sanctioned monopolies, I think created a lot of societal ills."

More small businesses, more freedom over where and how people work, more people getting rich on smaller scales.

Why VCs Keep Leaking His Numbers

Replit's revenue numbers leaked twice. Both times from VCs. Amjad explains the systemic problem:

"I think the VC market has become so lucrative, especially lucrative on the management fee side. So you can be a shitty VC and make a lot of money. And so you have a lot of short-term thinkers that are going in and I don't know what's their incentive to be leaking these things. Maybe they're trying to carry a favor with a journalist."

Silicon Valley's trust culture is eroding because the incentive structure changed.

The Seasonal Intensity Model That Actually Works

Amjad rejects both 996 and anti-hustle culture, offering a third way:

"I think we've always taken the view of seasonality. Like I think even in early human history, you have the season in which you're like planting the crop, the season when you're harvesting. Especially around the major releases, we ended up doing a lot more than 996. We ended up doing 12/12/7. But for the most part, it's a lot more sustainable."

During Agent 3's launch, the team went all-in. The rest of the year, people pick their schedules, go home for dinner, and work flexibly.

Which SaaS Companies Will Survive

Amjad draws a clear line between winners and losers in the AI transition:

"Those systems, I think have very lasting advantages just because this is where all the data is. There's network effects, there's developer mind share. They need to start thinking of themselves as like more of an open system, like MCPs and being able to be a platform where other agents can sit on top of."

System of record players like Salesforce survive only if they open up. Point solutions die as companies build custom. Closing APIs (like Salesforce just did with Slack) is the fatal mistake.

The Call to Action

"Jump head first, like don't think too much about it. I think the future really benefits those people who are more action oriented."

Companies Mentioned

Replit
Cursor
Devin
Lovable
Bolt
Retool
Zapier
Make
Figma
OpenAI
Salesforce
Workday
Upwork
Klarna

See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Get Paid with Manny Medina is handcrafted by our friends over at: fame.so

S2E21: The $200K legal bills that made me automate law firms (Nick Holhzherr, GitLaw)

Fri, 03 Oct 2025 10:45:00 +0000

We invited Nick Holzherr, founder of GitLaw and former contestant on The Apprentice, to dive deep into the legal industry's impending transformation. Nick shares his journey from paying hundreds of thousands in legal fees for what he discovered were essentially template documents, to building a platform that automates 90% of legal work. He breaks down the shocking economics of law firms where partners charge $2000/hour while paying juniors $100/hour to fill out standardized forms, and explains why this model is about to collapse entirely.

Nick and Manny explore the broader implications of AI displacing entire professions. From the thousands of qualified junior lawyers who can't find work right now (but politicians don't see it yet), to the mind-bending tax revenue crisis coming when AI agents replace human workers, this episode reveals the systemic changes already happening beneath the surface. Nick also pulls back the curtain on his Apprentice experience, sharing the psychological manipulation tactics used by reality TV producers, and offers his "barbell strategy" for legal spending: automate the basics, hire only the absolute best commercial lawyers for complex deals.

> "The social contract is broken now. The people at the bottom no longer get this shot of being the lazy fat ones on the top."

The $200K Legal Template Revelation

Nick exposes how law firms charge enterprise rates for junior work using standardized templates from Practical Law and LexisNexis. While partners enjoy social events, juniors work 80+ hour weeks filling out templates that clients could complete themselves.

> "90% of the bill is actually operational stuff that's not a really smart lawyer. It's someone much more junior, and they're getting charged at $500-1000/hour."

This broken economic model—where partners capture massive margins while juniors do the grunt work—is about to collapse as AI eliminates the need for junior lawyers entirely.

Why Most Founders Are Overpaying for Legal

The failing pattern Nick sees repeatedly: founders pay premium rates for template work that could be automated. His solution challenges conventional legal spending:

> "Go tippy top or prompt. That's it. Barbell strategy."

His argument: either automate the basic work or hire only the absolute best commercial lawyers who can negotiate deal terms worth hundreds of thousands. Skip everything in between.

The Reality TV Psychological Manipulation Playbook

Nick pulls back the curtain on The Apprentice's systematic psychological warfare tactics:

> "They put ideas into your mind. They tell you 'Did you notice Nick was giving you an evil look? He thinks you're rubbish.'"

The show uses sleep deprivation, 24/7 surveillance, isolation from family, and planted conflicts to break down contestants mentally. Despite the manipulation, Nick would do it again for the business exposure.

The AI Unemployment Crisis Politicians Can't See

Nick reveals the hidden crisis already happening in the legal profession:

> "Hundreds of applicants from great universities. They haven't got jobs."

When GitLaw posts lawyer positions, they're overwhelmed with desperate qualified candidates. But politicians don't see the displacement because "we're all in a bubble."

The Coming Tax Revenue Disaster

The conversation takes a speculative turn into the fiscal crisis coming from AI displacement. When AI agents replace human workers, states lose massive tax revenue but gain unemployment costs with no funding for safety nets.

> "Europe has kind of got a model for that. You can tax the agents really high to pay for everyone on universal basic income."

Europe's existing social safety net infrastructure gives them a massive advantage in the AI transition compared to America's lack of UBI systems.

Companies Mentioned

GitLaw
Practical Law
LexisNexis
Harvey AI
Lawhive
Legora
Granola
ElevenLabs

See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Get Paid with Manny Medina is handcrafted by our friends over at: fame.so

S2E20: Price Before Product: The AI Monetization Playbook (Madhavan Ramanujam, 49Palms)

Fri, 26 Sep 2025 13:00:00 +0000

Madhavan Ramanujam stepped into the AI pricing conversation with a contrarian view: everyone is doing it wrong. As author of "Monetizing Innovation" and founding partner of 49 Palms Ventures, he's advised 250+ companies on pricing strategy. Now he's seeing founders leave millions on the table by using outdated SaaS playbooks for AI products.

> "Your moat is monetization and GTM. Can you get products in the hands of many people? Can you make it stick? And will they pay for the value?"

His message is clear: the two-year coding head start you're banking on is worthless. In the AI era, your competitive advantage isn't technology - it's how you price and distribute it.

The $50K to $500K Pricing Revelation

Madhavan shares a story that captures everything wrong with AI pricing today. A founder came to him charging $50K for an AI agent that was creating tens of millions in value. The founder thought it was "reasonable" and helped close deals faster.

"Put an option on the table. A $50,000 plus 10% of the outcomes that I generate or a $500,000 fixed fee."

This simple choice changed everything. The conversation shifted from price to value measurement. Customers chose the $500K option and negotiated down to $400K - a 10X increase with the same sales velocity.

"Which investor does not want that, right? That you could actually 10X your price and have the same sales velocity. Why wouldn't you do that?"

Why most AI companies are pricing wrong

The failing pattern Madhavan sees repeatedly:

Companies tie pricing to costs rather than value. Founders add 20% margin to token costs and call it pricing. As costs fall, so does revenue, even though customer value remains constant.

> "You just tied yourself to like a destiny that your pricing is going to keep coming down."

The labor budget opportunity. AI agents tap into labor budgets that are 10X larger than IT budgets, yet founders still price like SaaS:

> "200k to hire a salesperson and you charge $5,000 for a seat for a year. I mean like that doesn't make sense, right?"

Underestimating AI's value capture potential. Traditional SaaS captures 10% of value created. AI with high autonomy and clear attribution can capture 25-50%:

> "There is increased autonomy and there is increased attribution. And you can justify that."

The Human + AI Pricing Formula

Madhavan's framework for pricing AI that replaces human labor challenges conventional thinking:

> "If your AI can operate as the best salesperson, and is available 24/7, why wouldn't you think about it that way?"

His argument: It takes six months to hire someone, six months to train them. By the time they're productive, you've invested two years of salary. Your AI is productive on day one and works 24/7. Price accordingly - at or above human cost, not at 10% of it.

The Partnership Imperative

For established SaaS companies trying to add AI, Madhavan sees most getting stuck in seat-based pricing with no path to value attribution. His advice for companies like Slack:

> "I think it has to be first principles thinking, can I build some agents that actually sit on top of Slack and can do some meaningful work that I can monetize on it separately."

The key is finding the equivalent of Intercom's Fin.ai model - an agent that solves end-to-end workflows with clear value attribution.

Pricing in the Age of AI

Madhavan's framework for AI monetization starts before building:

> "Price before product. Period."

His approach:

Have willingness-to-pay conversations before writing code
Build only what people value enough to pay for
Choose the right pricing archetype based on product characteristics
Move from cost-plus to value-based pricing as quickly as possible

Leadership Lessons from the Pricing Frontier

Madhavan is juggling three major initiatives: running his fund (49 Palms), deploying capital, and launching his new book "Scaling Innovation." His thesis ties them together:

> "Monetization is the key to winning in AI."

His investment philosophy focuses on durable monetization rather than growth at all costs:

> "You need to have a clear conviction that you can actually, at the end of the day, build a profitable growth business."

Companies Mentioned

49 Palms Ventures
Delphi
Sierra
Intercom (Fin.ai)
ServiceNow
Slack
Workday
Superhuman
Gmail
DocuSign
Zapier
YC (Y Combinator)
First Round Capital
NFX
Stanford

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Get Paid with Manny Medina is handcrafted by our friends over at: fame.so

S2E19: “VCs wanted us to build an LLM. We said no” | John Sabino (LivePerson)

Fri, 19 Sep 2025 09:00:00 +0000

John Sabino stepped into LivePerson as CEO facing a perfect storm: financial distress, harsh investor criticism, and a company that had "lost its way" trying to build the world's best LLM. Within weeks, major investors were telling him his strategy wouldn't work and that the company wasn't mentioned in critical industry reports.

"You have to have the wherewithal and the courage to say, I'm not doing that. I'm going to try this first."

His response was to refocus on LivePerson's core strength - 30 years of orchestration expertise - while pioneering what became an industry standard: Bring Your Own LLM (BYOLLM).

"Everybody thought I was nuts when we started with that. And now that is the industry buzzword. I'll be blunt. I think we started that."

Why 95% (or maybe 60%?) of AI Pilots Fail

The MIT study showing 95% of AI pilot failures validates what John has been saying all along, although we're not sure it's actually 95%. Maybe 60%....

"A use case is not a process. A use case is not a full customer journey. A use case is not a solution. That is what many people miss."

The failing pattern:

AI companies grab isolated use cases
They wow boardrooms with resolution rates
They ignore orchestration, multi-channel needs, and escalation paths
Customers get frustrated when complex issues can't be resolved

The Human + AI Formula

John's framework for CEOs navigating AI transformation focuses on strategic augmentation rather than wholesale replacement:

"Don't fire all your humans. Those are the people that should handle complex cases where the package went to the wrong location, where the payment was wrong."

The opportunity lies in creating premium tiers of service. High-net-worth banking customers and serious gamers will pay for white-glove human support. Meanwhile, automate the "high caloric, low value tasks" that make up 45% or more of inquiries.

The Partnership Imperative

Drawing from his GE Digital experience, John believes survival in the AI era requires acknowledging what you can't build:

"Partnerships matter. Unless you're one of the big three, you're just not going to have the resources to build everything for everybody."

LivePerson's partnership strategy today includes multiple LLM providers - through a BYOLLM approach. For John, this was validated when DeepSeek emerged "out of left field" and could immediately integrate with LivePerson's platform.

Pricing in the Age of AI

John's approach to monetizing new AI capabilities rejects the "fail fast and free" mentality:

"I don't believe in doing a pilot for nothing because if it's free, it doesn't have value or a basis to establish value."

His framework:

Start with 3-4 early access customers
Charge professional services fees initially
Understand value creation through real usage
Then determine pricing model (seats, volume, or outcomes)

The goal is outcome-based pricing, but you need data first:

"In order to price any metric or unit that you're going to use to establish value, you need to know the outcome that you're driving."

Leadership lessons from crisis

John's military background (Ranger School, Airborne) shaped his crisis leadership philosophy:

"You want people in your organization that have the grit, that know how to take the objective with less. If they fail, they dust themselves off and say 'next one, let's go.'"

His framework for CEO decision-making:

Always consider shareholder value
Answer to your customers
Take care of employees
Make financially responsible decisions
Never shy away from tough choices

The Gartner Magic Quadrant Journey

Responding to investor criticism about not being in key reports, LivePerson made a concerted effort to improve their position. The achievement wasn't just about targeting the report:

"It needs to be your technology speaking for you. It needs to be your customers speaking for you. And that's what that Gartner report represents."

Companies mentioned

LivePerson
Amazon Connect
Avaya
Google RCS
WhatsApp
DeepSeek
Intercom
Decagon
Microsoft
Salesforce
Procter & Gamble (P&G)
VMware
GE Digital

See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Get Paid with Manny Medina is handcrafted by our friends over at: fame.so

S2E18: ChatGPT is going to sell you therapy | Ethan Ding (TextQL)

Fri, 12 Sep 2025 14:30:00 +0000

The Enterprise AI Reckoning Has Arrived

The AI spending party is over. Ethan reveals that public companies went on unprecedented buying sprees in 2024, with procurement teams purchasing up to 500 different AI tools in a single year. Now comes the hangover - these same companies have instituted total bans on new AI vendors and are mandating 50% cuts before they'll even take another meeting.

"We bought 500 pieces of AI software in the past year. We have a total ban on new vendors whatsoever. We have to cut at least 250 of it before we even have conversations ever again."

The dirty secret? Nobody's actually using these tools. Ethan estimates 50% of enterprise AI initiatives have already failed, but companies won't admit it publicly. Teams churned the tools internally, but the invoices keep coming because admitting failure isn't an option when boards demanded "buy one of each" strategies.

"Nobody on our team used it. So that's like 50% of our AI initiatives down the drain. You never want to admit that your AI initiatives have failed."

Information Blindness Creates Billion-Dollar Moats

Here's the shocking truth about AI adoption: most users have no idea what they're actually using. Ethan drops a bombshell - while everyone knows ChatGPT, less than 10% of users understand that OpenAI powers it. This information blindness creates massive opportunities for vertical AI products.

"I think people underestimate how many companies or how many people there are, who if they use Harvey for lawyers, you might never find out what ChatGPT is. Less than 10% of them know what OpenAI is."

The implication is profound: if you're first to a niche with an AI solution, you might own that market for 4-5 years. Users develop "infinite loyalty" to their first AI tool because they never discover alternatives exist. It's like Nokia still having devoted users despite the iPhone - once you capture a market segment, information penetration is so weak that switching barely happens.

Data Science: The Infinite Arms Race

Unlike fixed workloads like accounting, data science has infinite demand because it's fundamentally competitive. Ethan uses a brilliant example: when Blackstone analyzes housing prices weekly by city, Cerberus counters by going daily by zip code. Then Blackstone responds with hourly analysis by square footage.

"If Blackstone analyzes housing prices per city per week, then Cerberus will want to analyze it per zip code per day. Then Blackstone's gonna want to do it per single family unit size square footage per hour."

This creates exponential demand growth - give trading firms 10x faster analysis, and they'll make 100x more trades because they can now pursue opportunities previously too small to bother with. TextQL's entire business model depends on this dynamic: as they reduce costs, volume explodes exponentially. It's why they're usage-based while competitors offering flat pricing are getting crushed by token costs.

Don't Innovate UI, Dominate Distribution

Every AI startup makes the same mistake: trying to innovate on user interface. Ethan's blunt assessment? Every single UX innovation TextQL attempted was "a horrible idea." The winning formula is surprisingly simple: copy ChatGPT's interface exactly (chat on left, workspace on right), then put all innovation into branding and distribution.

"Almost every single innovation we have ever tried to do with this company on UX has been a horrible idea. We always go back to the base. Your branding is entirely unrelated to your product."

The painful truth for engineers: the product doesn't matter, positioning does. Say you're "the AI agent for laundromats," give it a hard hat, and hammer that message repeatedly. The opportunity isn't in better AI - it's in reaching the people who don't use AI yet and saying "I built this for you." Marketing matters 10x more than the product in today's AI landscape.

OpenAI's $350 Billion Ad Platform Play

OpenAI's aggressive pricing on GPT-5 isn't about winning the API war - it's about building the world's most powerful commerce platform. Ethan paints a dystopian but likely future: you tell ChatGPT you're sad, and it recommends therapist Frederick Carlson, books the appointment, charges your credit card, and informs you it's out-of-network for $500.

"ChatGPT says, 'Well, you should consider talking to a licensed therapist, Mr. Frederick Carlson.' It's like, 'You want me to book a meeting for you right now? I've used your credit card to pay for this therapy.'"

Every early OpenAI and Anthropic demo featured "order me a pizza" as the use case. When ChatGPT becomes the layer between you and commerce, DoorDash and Uber Eats will pay massive fees to be the "preferred carrier." With potentially a billion users, OpenAI is sacrificing API profits to build something far more valuable: the transaction layer for AI-mediated commerce. As Ethan notes, ads are a $350 billion profit business, and "people just like being sold to."

The Trillion-Dollar Coin Flip Strategy

While everyone else fights over $10 billion exits, Ethan has a different philosophy: he's only interested in trillion-dollar opportunities. His target? AWS. His strategy? Be willing to flip coins at 51% odds repeatedly, as long as the expected value is massive.

"I'm basically willing to flip. I'm kind of more like SBF. I'm pretty happy to flip the coin with 51% chance over and over again, as long as I have high EV. I'm only interested in trillion-dollar market opportunities."

TextQL follows the Bezos doctrine: "your margin is my opportunity." They'll trade 1% of profit margin for 2% growth every time, because volume creates the ability to hire the best engineers, optimize infrastructure, and ultimately offer better prices than any competitor. It's the same playbook AWS used to become a trillion-dollar business - sacrifice margins early, dominate on volume, then own the entire market.

"I don't want to build a $10 billion business. That seems incredibly boring to me. I just want to go after AWS."

The AI industry is experiencing a massive correction. Enterprises are drowning in unused tools, OpenAI is building an ad empire disguised as a chatbot, and the real winners will be those who understand that in AI, distribution beats innovation, volume beats margins, and the first mover in a niche might own it forever. As Ethan says - "It's not the model, it's the marketing."

Companies Mentioned

OpenAI
Anthropic (Claude)
Google (Gemini)
Amazon AWS
Microsoft
Meta
Netflix
Spotify
Cursor
Windsurf (acquired/sold)
Replit
Lovable
Bolt
Claude Code
Harvey
TextQL
Databricks
Snowflake
Cognizant
Blackstone
Cerberus
Goldman Sachs
PWC
Nokia
DoorDash
Uber Eats
Walmart

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S2E17: SaaS Revenue Bloodbath Is Coming | Rob Litterst (PricingSaaS)

Fri, 05 Sep 2025 13:00:00 +0000

Rob Litterst is on a pricing island, and it's about to get very crowded. After watching traditional SaaS companies fumble with AI pricing, he's going full-time on PricingSaaS to shepherd the entire industry through what he calls "a really interesting rat's nest of questions." The biggest confusion? Everyone thinks agent pricing means outcome pricing. Spoiler: it doesn't.

The Seat-Based Apocalypse Is Here

Rob's take on the death of traditional SaaS expansion is brutal and honest: "Seats are not an expansion lever anymore."

The math is simple and terrifying for legacy SaaS. Startups now operate with drastically fewer people, revenue per employee is skyrocketing, and if you're still charging per seat while your customers' headcount shrinks, you're basically pricing yourself into irrelevance. Marketing teams will shrink first and most dramatically - Rob can already do most of his job with AI, and he's not alone.

The kicker? Even "mom and pop SaaS companies" are now asking about agent pricing. This isn't some Silicon Valley fever dream - it's hitting mainstream faster than anyone expected.

Intercom's Secret Sauce (And Secret Fundraise)

Here's the tea Manny spilled: Intercom just raised an undisclosed round priced entirely on Fin's growth - their AI agent, not their traditional SaaS metrics.

They looked at the pie, saw one slice growing exponentially, and said "that's worth underwriting at a premium." The regular SaaS model? "That's not gonna work." Everything migrates to outcomes eventually, and Intercom's investors just placed a massive bet on that future.

AI Agents Double Every 7 Months, Not 2 Years

Forget Moore's Law and its leisurely 2-year doubling cycle. According to Dharmesh (and Rob's deeply in agreement), AI agent capabilities double every seven months.

This isn't incremental improvement - it's exponential transformation on steroids. Rob's framework: AI currently gets you from A to L, professional services handle L to Z. But that alphabet split is shifting monthly. Companies not charging for outcomes will watch competitors eat their lunch, then their dinner, then their entire business model.

Day AI's "Ergonomic Pricing" Middle Ground

After building HubSpot's CRM, the Day AI founders created something Rob finds fascinating: a hybrid model that's neither pure licenses nor pure outcomes. They charge a flat fee for a range of agent services - outcomes baked into the license.

Rob's watching their margins closely. With their AI costs potentially destroying profitability (they're "blowing through Anthropic Claude Opus tokens"), they're betting they can maintain 60-80% margins through careful credit design. It's the middle ground nobody else has figured out yet.

Vibe Marketing and the Death of Attribution

Rob's confession about his creative process is peak 2025: "I actually have an AI agent that I programmed to read through Tom's book and then spit out five ideas."

But the real insight is about marketing's stagnant state. When Astronomer hired Gwyneth Paltrow for a campaign, Rob's reaction was telling: "Who cares about attribution at that point? They just nailed it."

In a world where marketing is "very, very stagnant" and everyone's doing the same playbook, vibe marketing - shipping campaigns quickly to see what explodes - might be the only differentiator left.

The Companies That Need Rescue

Rob's ambulance is heading for:

Miro (commoditized whiteboarding needs pricing innovation)
MailChimp/Benchmark Email (email is a commodity, pricing is the differentiator)
Sprout Social/Buffer (getting eaten by creator-specific tools like Taplio)
Gong (what was special is now everywhere - "the atomic unit of the new era of CRMs")

The pattern? Any product that feels commoditized needs pricing as a lever, fast.

Rob's Master Plan

PricingSaaS isn't just another consultancy. They're building:

A data feed tracking thousands of pricing pages in real-time
Private questions feature (because pricing strategy is too sensitive for public forums)
Eventually, a chat interface that becomes the pricing oracle for all of SaaS

Why private questions matter: "Pricing is so tightly correlated with your strategy... you don't really want to signal anything to competitors."

Companies Mentioned

Intercom
Day AI
Salesforce
HubSpot
Astronomer
Notion
Gong
Miro
MailChimp
Sprout Social

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S2E16: AI is better than Love Island | Ashu Garg and Jaya Gupta (Foundation Capital)

Fri, 29 Aug 2025 06:00:00 +0000

You know something fundamental is shifting in tech when the drama between startups and their AI providers becomes more entertaining than reality television. That's exactly where we found ourselves in this conversation with Foundation Capital partners Ashu Garg and Jaya Gupta.

"This is better than Love Island. I love this shit. Like the gossip and the intricacies of like the people itself. It's amazing."

Manny wasn't wrong. The Cursor pricing saga - where a startup's infrastructure provider started building a competing product, leading to poached PMs, revoked discounts, and user revolts - is just one symptom of a much larger transformation happening in software.

The Death of SaaS (As We Know It)

The conversation started with what might be the most sobering statistic for anyone running a SaaS company today. According to Ashu, the middle market is getting absolutely crushed:

"Mid-sized SaaS companies are struggling. Nine out of 10 are seeing some churn, but the churn isn't dramatic yet. They're seeing employee attrition. They're fighting a war of feature by feature. They're trying to add AI pixie dust here and there. But net-net, they're all struggling."

The companies he's talking about - those between $100M and $1B in revenue - find themselves in an impossible position. They're too small to acquire their way out of trouble like the giants can, but too big and established to pivot quickly like startups.

Ashu pointed to Outreach as a prime example: "Incredible company for a decade. Look at the numbers today. It's flat, maybe marginally declining."

Meanwhile, tiny AI-native startups are "growing like crazy" from the bottom up. The bit hasn't flipped yet, but Ashu thinks we're not far from a tipping point where customers abandon their incumbent platforms en masse.

The $20 Million First Customer

Perhaps nothing illustrates the speed of this transformation better than Ashu's revelation about deal sizes in the AI era:

"I funded a company earlier this year. Their first deal, which hasn't been signed yet - knock on wood - but the first customer is likely to give them a $20 million plus TCV deal. When's the last time you saw a seed stage company get a first customer at $20 million plus?"

This isn't normal SaaS growth. This is a company jumping from seed stage to Series E valuations in a single deal. The company in question? They're migrating legacy SAP and Oracle code using AI. When you're solving billion-dollar problems with AI, apparently the old rules about gradual revenue growth simply don't apply.

Why Experience Became a Liability

One of the most controversial takes came from Jaya, who argued that in AI, youth beats experience:

"AI is new for everyone. Like, if anyone can predict what's happening in six months, I would call that bullshit. No one knows what's happening. You are seeing in this market a ton of younger founders even outpace second time, third time founders that have built unicorn companies."

The logic is simple but profound: everyone started learning AI at roughly the same time. But younger founders have less to unlearn, move faster, and are using AI itself to build their companies more efficiently. As Jaya put it, "Knowledge has been quickly democratized."

This might explain why Foundation Capital has such an unusual approach to evaluating companies...

"If You Have Revenue, Don't Call Me"

In perhaps the most counterintuitive investment philosophy you'll hear, Ashu actively avoids companies with revenue:

"Even though we don't really invest in companies with revenues, in fact, I always tell people, if you have revenues, don't call me. I'd rather not deal with messy revenues. I want to deal with big ideas."

This isn't just contrarianism. Ashu argues that early revenue often constrains vision and forces founders to serve existing customers rather than reimagining entire categories. Even without revenue, he looks for other forms of traction: Who are the early customers you're talking to? Which engineers are you recruiting? If you're building in sales tech and haven't talked to Manny, "I'm not funding you."

The 500 Agent Future

The partners saved their boldest prediction for last. Forget building another point solution or feature company. The future belongs to companies that think bigger:

"The world of AI apps and AI agents is about 500 agents from one company replacing 500 feature companies. You've got to think broad, you've got to think big, and you've got to execute like crazy to see which agent works, because very often one or two agents doesn't solve anything for a customer. They need enough of these agents to really move the meter."

This runs counter to everything VCs have preached about focus for the last decade. But in Ashu's view, the narrow AI startup is already dead. Customers don't want to manage hundreds of point solutions anymore - they want comprehensive agent armies that actually move the needle.

The Plot Twists Keep Coming

Beyond these major themes, the conversation was peppered with surprising predictions and hot takes:

On the AI giants: Despite their massive valuations and growth, Ashu is "very skeptical on both OpenAI and Anthropic." He believes that "when the dust settles, it's not clear that the winners in that category will be either of the two companies."

On pricing models: While everyone talks about outcome-based pricing, Jaya thinks we'll see an evolution through usage-based and workflow-based models first. True outcome-based pricing remains elusive because, as she notes, "the outcome is actually just a function of the customer's product as well, not just your software."

On commitment issues: Both partners openly admitted to having "commitment issues" when it comes to investing, preferring to "date" founders for four to five months while gathering what Jaya calls "observability data" on how they think and learn.

What This Means for Founders

If you're building in AI right now, the message is clear but daunting. The playbook that worked for SaaS won't work here. As Ashu put it:

"A lot of the lessons that you and I learned over the last few decades of software apply, but a lot more don't. Knowing when to break the mold and reinvent and reimagine how you do things, I think is a big part of winning in the AI space today."

For Manny, who built Outreach into a unicorn, this resonates deeply. He's now tackling the problem of monetization and margin management for AI agents - the very issue that was "the bane of his existence" at Outreach. Sometimes you do "irrational things at irrational times," as he puts it.

But in a world where the drama is better than Love Island and first customers write $20 million checks, maybe irrational is exactly what we need.

Companies & Products Mentioned

Foundation Capital
OpenAI
Anthropic
Cursor
Windsurf
Claude (and Claude Code)
11X
Harvey
Scribe
Lovable
Tenor
Fulcrum
Outreach
Salesloft
Databricks
Salesforce
Oracle
SAP
Excel
Wix
McKinsey

See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

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S2E15: Will Bots Buy From Bots at $10K? | Maruthi Medisetty (Blue AI)

Fri, 22 Aug 2025 10:00:00 +0000

Subscribe on https://podcast.paid.ai

Maruthi Medisetty thinks your sales enablement team is wasting 50 hours a week, and he's built AI agents to prove it. After watching reps practice with AI for 60-75 minutes straight (when they won't even spend 30 with their managers), he's got some contrarian takes on why humans will always close the big deals, how to charge 10% of the value you deliver, and why companies pay McKinsey millions just to have someone to blame.

The 50-Hour Week Nobody Talks About

Five enablement people reviewing 10 calls each per week equals 50 hours of pure waste. Nevara (formerly Blue AI Labs) compresses that to 1-2 hours of approval time. But here's the kicker: Maruthi's not even charging for all that saved time yet. While he could price at $200K+ per year (a full enablement person's salary), he's starting with "cappuccino pricing" to prove the value first.

"The 50-hour week is massive. It's like a person and a quarter".

Why Nobody's Signing $10K Contracts with Bots

Maruthi draws a hard line: AI can handle your McDonald's-style transactional sales, but the moment there's "second order skepticism" - when someone needs assurance this will actually solve their problem - humans win. Even for a $7K employer of record deal, buyers want 30 minutes with a human to understand German employment law.

"I don't think I would be able to sign a $10,000 contract with an AI account executor. Not yet, no."

From Seat Pricing to Outcome Capture

The pricing evolution: Start with per-seat, move to consumption ("seats times usage"), then land at human equivalent value (hours saved), before finally reaching outcome-based pricing. Maruthi's endgame? If he can turn 20 sellers into "super sellers" handling 5x quota, that's $4M in new revenue per team.

"We are not looking at per seat- per user."

The McKinsey Scapegoat Premium

This brutal truth explains why consulting firms buying AI companies doesn't eliminate the partner who sits with the CEO. Someone needs to be accountable when things fail.

"When you're sitting in a board meeting and want to say why this went wrong, you still want to blame McKinsey and not a McKinsey agent, right? That's the premium. That's the premium you're paying for."

Your Learning Management System is Dead

Reps are spending 60-75 minutes practicing with AI agents - time they'd never spend watching videos or talking to managers. This kills BigTingCan, LiveRamp, and the entire enablement stack.

"I see that the reps are practicing at least 60 to 75 minutes with the AI, and I've never seen them even do that kind of talking with their managers or even with their colleagues."

The AISDR Goes to Marketing

Hot take: AISDRs aren't sales tools anymore - they're "AI demand generation" that belongs in marketing. Why? Because people buy from people, and in a world of bot noise, the human touch becomes the differentiator.

"People buy from people. And then in this noise of the whole bots coming in, if you can stand out as a seller, still retaining that human touch, then the best thing is to augment individuals."

Models Get 10x Better Every Six Months

Following Sam Altman's advice to "never bet against OpenAI's modeling," Nevara builds on the assumption that today's limitations vanish tomorrow. They're not building custom models - they're orchestrating multiple models for specific tasks.

"For every six months, the agents are getting 10x better, or the reasoning models are getting 10x better. So that means the agents will get 10x better."

Sales Management Ratios Flip from 1:5 to 1:20

When AI handles all the call auditing and identifies exactly what each rep needs to improve, managers can handle 4x more direct reports. The future sales org looks radically different.

"I think the managers, the 1:5 ratio managers are going to be like kind of shrink to probably 1:20 because they would have a lot more time."

The 18-Month Churn Problem

Both sellers and VPs of sales average 18-month tenures. Maruthi's mission: use AI to make your best people so successful they never want to leave.

"Think of what are the ways on how you can empower your best employees or your best workforce. Forget about everything else and then see where AI can augment these individuals because super these these people with super skills are God's given gift and they're rare."

The 90% Automation Reality

When it comes to sales training and enablement, AI isn't just helping - it's doing almost everything.

"90% of that aspect is done by agent."

Companies Mentioned:

Nevara (Formerly Blue AI Labs)
Outreach
Rippling
Remote
ServiceNow
Salesforce
McKinsey
BigTinCan
LiveRamp
ElevenLabs
OpenAI
Microsoft
Apple
Google
Meta
Superhuman
McDonald's
Stripe

See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

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S2E14: Infinite markets: We don't optimize for COGS | Christopher O’Donnell (Day.ai)

Fri, 15 Aug 2025 09:00:00 +0000

Show page: https://go.paid.ai/podcast-s2e14

Christopher O'Donnell just built what might be the most technically brutal CRM ever attempted. After two years and two million lines of code connecting Gmail APIs, Google Calendar, and meeting transcripts into a preprocessing engine that actually works, he's got some spicy takes on AI economics, the coding tool wars, and why the universe might be conspiring in your favor.

The "Total Piece of Cake" Preprocessing Hell

Christopher's favorite response when competitors think they can copy Day.ai's approach: "Good luck. Have fun. I'm here for a hug when you need it." The technical reality of building an AI-native CRM is absolutely savage. While everyone else builds thin ChatGPT wrappers, Day.ai processes a "fire hose of incoming data" to create actual intelligence.

Instead of feeding raw database tables to LLMs, they preprocess everything into beautifully written natural language narratives. Think less "John Smith, VP Sales" and more "John is the real decision maker who's interested but you need to sell him on the core value prop, while Sarah is the champion with zero internal sway and Mike the lawyer actively doesn't want this deal."

The result? When you ask about your pipeline, you get actual strategic intelligence instead of regurgitated contact records.

Don't Optimize for COGS

While every AI startup sweats inference costs and switches to cheaper models, Christopher drops this bomb: "We should not optimize for COGS."

His framework flips conventional wisdom: When the gap between frontier models like Claude Opus and budget alternatives is massive, you optimize for customer value, not unit economics. Use the expensive model that delivers 10x better results and charge accordingly. The costs will smooth out as open source catches up and capacity increases.

"At a time when the difference between the state of the art frontier model that's going to be expensive and the best open source alternative is as big as it is today, it's not the time to optimize for that."

Ergonomic Pricing: The SKU Revolution

Christopher introduces "ergonomic pricing" - letting users create multiple instances of a paid SKU, each with their own permissions, model instructions, and even DISC personalities. His AI assistant has a whole backstory as "the child of a Swiss watchmaker" who's "super analytical."

You can literally tell your AI assistant: "Your name is Klaus, be really direct and German with me always." And it updates itself accordingly. This isn't just personalization - it's treating AI agents like actual team members with distinct roles and personalities.

Claude Code vs Cursor: The IDE Wars Get Spicy

The conversation takes a sharp turn into the current coding tool bloodbath. Christopher sees Claude Code pulling ahead not just on model quality, but on fundamental architecture. While Cursor builds a better IDE, Claude Code is building the foundation for autonomous development workflows.

"If they're going to give you the harness to do that, but you can't really, right? Because you're not really spinning up like a whole container. You don't really have MCP working in that environment."

His prediction? Claude Code extends its lead, but there's a massive "overhang" between what these tools can theoretically do and what anyone actually uses them for. We're all underutilizing the current generation while waiting for the next one.

The Transparency Trust Crisis (Claude Code Gets Real)

"The things that really damage trust are the unexpected charges. And the things that really build trust are like, hey, we're going to tell you exactly what this is going to cost you before you do it."

While Cursor hides behind vague usage limits, Claude Code is building transparent pricing that shows you exactly what each operation costs before you run it. No surprise bills, no mysterious usage spikes, no "contact sales" for overages.

"I think Anthropic and the Claude Code team specifically are like really thinking hard about this stuff in a way that I think is important for customer trust."

The Async Intelligence Breakthrough

Christopher's AI assistant Chloe watches meeting recordings overnight and emails him the top five priorities, blocking bugs, and product positioning lines that actually landed with prospects. This isn't a demo - it's working today.

"I woke up this morning with an email from my assistant Chloe and she had watched all of the meeting recordings from the previous day and came in and said, here are the issues, here are the top five things you got to worry about."

This represents the shift from reactive chat interfaces to proactive intelligence that works while you sleep.

The CRM Apocalypse: Three Scenarios

Christopher maps out potential futures with brutal honesty. Scenario one: status quo holds, everyone builds better products. Scenario two: a few AI-native winners emerge alongside improved incumbents. Scenario three: one player takes everything and "Salesforce will not be a public company" by 2028.

He puts the apocalypse scenario at 20-30% probability. "Maybe somebody starts a company in 90 days with some new frontier model, with some new mindset, and they just stick the landing and get it all perfect."

The Universe Conspiracy Theory

Christopher's philosophical framework sounds like Silicon Valley mysticism until you hear the application: "The universe is conspiring in your favor if you're paying attention." When technical complexity feels impossible, when competitors emerge, when pricing gets weird - lean into the flow instead of fighting upstream.

"We do these things not because they're easy, but because we thought they would be easy. The universe is like, you need to build this AI native CRM that's all automatic and I'm like, that sounds great. And then two years in, two million lines of code, and I'm just like, okay, I'm tired."

The Infinite Market Reality

While everyone obsesses over winner-take-all dynamics, Christopher drops this perspective shifter: "This is an infinite market." The CRM space is so massive that multiple AI-native players can build billion-dollar companies without directly competing.

His advice for the chaos ahead? "Accept what the universe is going to bring and try to use this stuff in the meantime to do something kind of positive for the world."

Companies Mentioned:

Day.ai
Salesforce
Anthropic (Claude)
OpenAI
Windsurf
Cursor
Google (Gmail, Calendar)
AWS
HubSpot
Coffee
Attio

See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Get Paid with Manny Medina is handcrafted by our friends over at: fame.so

S2E13: The Death of Traditional Startup Scaling | Amos Bar-Joseph (Swan)

Fri, 08 Aug 2025 08:55:00 +0000

The venture capital model is dying, and Amos Bar-Joseph has the data to prove it. After selling two previous startups, he's now building Swan AI with an audacious goal: $30 million in revenue with just three people by 2025. The kicker? Over 50 VCs have approached him, not because he needs money, but because they're desperate to understand how autonomous businesses will reshape their entire industry.

The Three-Function Business Architecture

Traditional startups have bloated org charts with misaligned incentives. Amos stripped it down to three core functions:

"An autonomous business don't have 10 different roles under the go-to-market umbrella. It has only one - revenue creator."

Revenue creators own sticky revenue growth. Product creators handle development and architecture. Agent creators build AI armies to amplify human potential. No SDRs, no demand gen, no customer success silos.

The $10 Million Per Employee Vision

Most companies throw bodies at scaling problems. Amos made that illegal:

"The constraint is actually you can't throw bodies at a scaling challenge."

If they hire one more person, that individual needs to be worth $10 million in ARR. This forces radical efficiency and intelligence-first scaling that traditional companies can't match.

Why VCs Are Panicking

The shift from capital-intensive to intelligence-intensive startups is breaking venture capital:

"VCs are starting to understand that the venture capital model is changing. Startups used to require a lot of capital to succeed."

Mid-stage VCs pouring $10-100 million checks will become extinct. They'll either move up to private equity or down to early-stage funding. The middle will disappear.

The Agent Creator Revolution

The most fascinating role is the agent creator - someone who builds AI agents to amplify humans, not replace them:

"An agent creator doesn't look at a process to automate. It looks at a human being at a center to amplify, to empower."

Amos has an entire AI system built around his LinkedIn strategy, handling everything from content creation to engagement tracking to website visitors.

The Enterprise Death Spiral

Large companies are doomed because they can't retrofit autonomous architecture:

"If you already scale with people, you are in a world of hurt because you have to like undo the process and undo the people to then layer agents."

Enterprises will get incremental 20% efficiency gains while autonomous startups achieve 100x improvements.

The Wealth Distribution Flip

Autonomous businesses solve startup inequality by concentrating value in smaller, efficient teams:

"The leaner the team and the less equity you give to external investors, then it means that the distribution of wealth actually goes more to the people."

The Return on Management Concept

Traditional scaling kills efficiency through bureaucracy:

"Return on management diminishes the more you scale because you just put more layers that are just in charge of the processes."

Autonomous businesses maintain flat structures where executives manage AI agents, not human hierarchies.

The Airbnb Funding Parallel

Startups are returning to capital-light origins:

"Airbnb got a check of $150,000 at the beginning of a $1.5 million valuation. They managed to build Airbnb with $150,000."

The playbook stayed stale for 15 years while costs inflated. Intelligence is replacing capital as the key scaling resource.

The Authenticity Distribution Advantage

Building in public creates unfair distribution advantages. Authentic messaging and thought leadership compress traditional marketing timelines.

This episode reveals why the next five years belong to small, autonomous businesses that can outmaneuver billion-dollar enterprises by scaling with intelligence instead of headcount.

Companies Mentioned:

Swan AI
Airbnb
Cursor
Devin
Google
OpenAI
Microsoft

See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

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S2E12: Getting Customers to Pay Before You Code | Pukar Hamal (SecurityPal AI)

Fri, 01 Aug 2025 08:00:00 +0000

When enterprise deals die at the finish line because of a 200-page security questionnaire, you know there's a billion-dollar problem hiding in plain sight. SecurityPal founder Pukar Hamal turned that pain into a service-as-software business that hit $2 million in revenue before building any actual software.

The Pain That Creates Billion-Dollar Markets

Picture this: you're about to close the deal that changes your company's trajectory. The champagne is ready. Then boom - instead of DocuSign, you get 200 pages of security questionnaires:

"We were like ready to pop champagne bottles. And so, you know, we got hit with this, what is this? It's like hieroglyphics, you know, like, do I have barbed wire around my data center?"

Pukar's insight was if startups can't afford armies of lawyers to fill out paperwork, only big companies with resources will win enterprise deals:

"My like fundamental realization was if companies have to fill out a bunch of paperwork before they close a deal and they don't have the resources to do that, then all the big companies are gonna win, because they do have the resources."

Service-as-Software: The $2M Validation Hack

Instead of building software first, Pukar started with pure service. A prospect asked if he could just fill out their security questionnaire:

"And I hadn't even incorporated the company. And the person was like, send me an invoice. I'm like, what is an invoice? I went on like Stripe Atlas and incorporated the company. So I had a company that wanted an invoice before I even incorporated it."

While working a consulting day job, he'd stay up all night filling out compliance forms:

"So at night I'd fill out these questionnaires and I had a customer send me a question. I'll be like, I need this back tomorrow morning, East coast time. Stay up all night. I fill it out. I send it at 3 a.m. They got the deal done."

The magic was in the positioning - customers didn't want software, they wanted outcomes:

"Yeah, so in the beginning, we didn't even have software. I was building this off of Google Sheets and Airtable dashboards. People would be like, when are we going to get a login? I was like, why do you need to log in? You have a form that needs to be filled out, and you want it filled out."

This approach generated nearly $2 million before any real software:

"We were well over a million, almost a two million before we even like built any software."

Complexity-Based Pricing Beats Per-Seat Models

SecurityPal charges based on complexity rather than user seats - questionnaire difficulty, product lines, regions, and SLA requirements:

"I fundamentally anchored our pricing based on complexity. It's like how complex is your problem as it relates to security reviews and security questionnaires?"

Want same-day turnaround? That costs more:

"So I would say like the vectors for pricing for us are quantity of work. And then the SLA, right? So do you want it done same day? Or are you okay with like three, five days?"

His pricing philosophy is refreshingly practical:

"Biggest piece of advice from pricing standpoint that I can give anybody is like your pricing is going to change, forget about perfecting it today."

AI Agents Won't Replace Premium Service

While the industry rushes toward AI automation, Pukar sees the premium market wanting hyper-personalized service:

"My friends that have it told me, you call them, you get a real person and like they're hell bent on figuring out how to help you solve your problem. And like that premium experience, it's like hard to really like get that through like AI agents."

The Bootstrap Mentality That Scales

Pukar's advice cuts through Silicon Valley hype:

"If you really believe in something, you're really passionate about it, and you want a certain version of the world to be created, and you will literally eat ramen and work out of the corner of your apartment, you don't need to raise capital. You just need one customer to believe in you."

The key insight:

"You don't need to deliver for that customer, the perfect version of the product, you just need to deliver value. And you need to get that customer to say that you've delivered them value. You got to get paid."

His brutal reality check:

"But by the way, if you're not getting paid, it doesn't exist. It's just basically feel good conversations that are happening."

Companies Mentioned:

SecurityPal
TalentBin
Teamable
Drift
Airtable
Figma
Vanta
Craft Ventures
Stripe Atlas

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Get Paid with Manny Medina is handcrafted by our friends over at: fame.so

S2E11: AI Agents Will Eat Everything | Jack Altman (Alt Capital)

Fri, 25 Jul 2025 09:00:00 +0000

Is venture capital broken, or are we just doing it wrong? Jack Altman brings a rare founder-turned-VC perspective after scaling Lattice to $3 billion and stepping back as CEO while on top. Now managing Alt Capital with investments in Figma, Rippling, and Flexport, Jack delivers brutal honesty about what's really happening in startup investing.

The Dating Analogy That Changes Everything Jack compares investor-founder relationships to dating - and the analogy is disturbingly accurate:

"I think it is a little bit like dating. Even your cherished execs come and go faster than preferred board members."

You're literally stuck with each other longer than most marriages. This changes how you should think about choosing investors entirely.

Why Everyone Missed Airbnb (And What It Means for Pattern Matching) In a moment that should humble every investor, Jack reveals the industry's dirty secret:

"There's a lot of founders that I think, you know, like the Airbnb rounds in the early days, special as Brian Chesky is, everybody missed it."

Pattern matching is fundamentally broken. The best founders don't fit existing patterns - they create entirely new ones.

The Altman Family IT Department Jack finally answers the question everyone's been wondering: who does mom call when the computer breaks?

"As kids, Sam was the one playing with computers, and so he was solving. And the IT problems used to be harder than they are now. So it was Sam."

But the deeper revelation is about their upbringing - "extremely showered with love and support" with classic 90s participation trophies for everyone.

Why AI Won't Be Winner-Take-All While everyone debates which AI company will dominate, Jack uses a brilliant payroll industry analogy to explain why they're asking the wrong question:

"I think a more accurate mental model for it is like overlapping bubbles, Venn diagram style."

Look at ADP, Paychex, Gusto, Rippling - all billion-dollar companies doing essentially the same thing but serving different niches.

The Market Sizing Confession In rare VC honesty, Jack admits what everyone thinks but won't say:

"I think all of us, whether you're an investor, a founder, a prospective employee, I think we all suck at trying to guess how big a market's gonna actually be."

Get two things wrong by 5X? You're off by 25X. The math is humbling.

The Anti-Scale Fund Strategy While everyone chases billion-dollar mega-funds, Jack is doing the exact opposite:

"I'm not somebody who is a big believer in platform teams. The way I want to work with people is just to be like a high quality, personal relationship partner."

His contrarian bet: staying human-scale in an industry obsessed with getting bigger.

The People-First Investment Philosophy Jack's most controversial take on deal evaluation:

"I'm not waiting for the pitch, I'm waiting for people."

While other VCs build thesis-driven investment memos, Jack admits he's completely founder-first. The market, opportunity, timing - all secondary to the person building it.

This episode cuts through VC marketing speak to reveal what early-stage investing actually looks like when you've been on both sides of the table.

Companies Mentioned:

Alt Capital
Lattice
Figma
Rippling
Flexport
Retell
Airbnb
OpenAI
ADP
Paychex
Gusto
Paycom
Paycor
Paylocity
Bamboo HR
Ultimate Software

See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Get Paid with Manny Medina is handcrafted by our friends over at: fame.so

S2E10: Why AI Won't Kill Salesforce | Aaron Levie (Box)

Fri, 18 Jul 2025 12:35:00 +0000

Are we in the iPhone moment of AI, or are we still building janky mobile apps? Box CEO Aaron Levie brings 15+ years of enterprise software experience to break down the biggest questions facing SaaS companies today. From pricing models to regulatory risks, this conversation goes deep on what's actually happening behind the AI hype.

The Great AI Pricing Experiment

Every software company is scrambling to figure out how to monetize AI agents. Do you charge by seats, tokens, outcomes, or some hybrid model? Levie reveals the chaos happening in boardrooms:

"There's no board of directors in software that is not making this the number one topic of every board meeting."

The challenge isn't just technical - it's existential. Companies are making architecture decisions in a vacuum, and there's a high probability many will get it wrong.

Why AI Layoffs Could Backfire Spectacularly

Here's where things get controversial. Levie warns that tech leaders using AI as cover for regular performance management are setting the industry up for regulatory disaster:

"People like Bernie Sanders will then totally jump on that. And so we as an industry are doing ourselves a disservice when we kind of over prop up that message because that's actually the surest path to over regulation of this technology."

The Productivity Paradox: Why AI Made Box Hire MORE Engineers

Forget the cost-cutting narrative. Levie shares how Box's AI implementation led to hiring more people, not fewer:

"If we can accelerate our product roadmap, that actually encourages us to hire even more engineers, because now we have higher productivity in this part of the organization to deliver even more value."

The key insight? Companies focused on output expansion rather than cost reduction will dominate their markets.

The Oracle Reality Check

In a moment of refreshing honesty, Levie admits his past predictions about "obvious" disruptions were dead wrong:

"You live long enough to see Oracle as a $600 billion company. When you're naive, you would have said, well, everything's obviously moving to SaaS and this business is going to get totally disrupted. You're constantly reminded that Larry Ellison's the G.O.A.T."

This humility shapes his contrarian take on AI disruption.

Will AI Kill Salesforce? The Surprising Answer

Levie's boldest prediction: traditional SaaS platforms will coexist with AI rather than be replaced by it. He compares it to how movies and TV coexisted rather than one killing the other:

"I look forward to replaying this in 10 years and it might be so laughably wrong, but just think about other paradigms, right? When movies came out, when TV came out, it coexisted with movies."

The Architecture That Matters: Church and State

On the technical side, Levie argues for strict separation between AI agents and core business data:

"I think you really do need to have a separation of duty or a church and state between what the agent is doing and the underlying object model, data model, or kind of business process."

The risk? Non-deterministic AI systems corrupting your deterministic business data.

The Final Word on AI Strategy

Levie's closing advice cuts through the noise:

"Everybody should be thinking about AI agents for expanding the capability of their software and their organization. And when you have that kind of prism in which you execute, you will find 10 times more interesting things to do than just replacing some kind of cost center."

This episode is essential listening for anyone trying to understand how AI will actually reshape enterprise software - beyond the hype and fear-mongering.

Companies Mentioned:

Box
Oracle
Salesforce
ServiceNow
Workday
Microsoft
Meta
Dropbox
DocuSign
Glean
Conga
Snowflake
Airtable
Intercom
Linear
Asana
Atlassian
ChatGPT/OpenAI

See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Get Paid with Manny Medina is handcrafted by our friends over at: fame.so

S2E9: The BPO Boss: Humans Are Going Premium | Bryce Maddock (TaskUs)

Fri, 11 Jul 2025 10:30:00 +0000

We sit down with Bryce Maddock, CEO of TaskUs (60,000+ employees, $1B+ revenue), who delivers the most honest take you'll hear about AI's impact on the service industry. As one of the world's largest BPOs navigating the AI transition, Bryce pulls no punches about automation claims, job displacement, and what's really happening behind the headlines.

Bryce exposes the disconnect between public AI claims and private reality:

"I see metrics where it's like, hey, We've automated 60, 70, 80% of our customer support. And I'm in the background being like, well, we still have the same number of humans. So I don't understand how those two things are possible."

This isn't skepticism - it's insider knowledge from someone managing contracts with 200+ of the world's biggest tech companies. TaskUs is literally losing money on purpose to win the AI game:

"Today your per contact price is $2, right? We'll give you an upfront savings. It means we're going to lose money, but we'll charge you $1.50. We'll charge you $1 a contact. We'll lose money for the first year."

They're moving from the traditional "law firm model" where "lawyers were just very, very poorly paid" at $15-20/hour to outcome-based pricing that aligns with AI capabilities.

When asked if all 60,000 employees will survive the AI transition:

"I don't know that all 60,000 are going to make it through the journey. There certainly is going to be temporary dislocation where people are going to lose their jobs. And I think anyone who says differently is not being truthful and doesn't understand the power of this technology."

Don’t underestimate this. Stop and listen - this level of honesty is rare from a public company CEO, but Bryce explains why sugarcoating serves no one. "The next phase is going to be even more impactful for my business and for all BPOs, is beginning to really become an agentic solution where things that took two, three, four, six weeks to train a human being to do, you can now train an agentic system to do. And I think that's where the rubber will meet the road."

Bryce also reveals how media headlines drive bad business decisions: "What's actually happening inside our customers is that there's massive pressure from the board and the C-suite because they listen to podcasts like this or read the headline articles and it's like, hey, know, Klarna doesn't have any humans doing customer support anymore. Why do you?"

"And so it's very easy for that pressure to build and for the C-suite and the board to say, we need to see real change. In the middle of the business, there is a lot of fear."

In another part, Bryce shares how VCs wouldn't fund TaskUs because it "wasn't techy enough" - and why he's now glad they stayed true to their service company roots while embracing AI partnerships. "The irony is this makes it harder for us, because a lot of the context that get escalated to humans become just really pissed off customers who want a human to yell at. [...] The agent can follow the policy and still piss off the customer such that they're like, I want to talk to a human being."

Bryce explains how humans become "premium features":

"Launch a white glove support line where if that customer contacts us, we can pick up the phone with a human being in five minutes and solve their problems. These human agents are much more empowered to actually solve problems."

"Either I'll look like a genius or a complete fool in a few years. So I'll be excited to look back and see which it is."

This episode offers unprecedented insight into how one of the world's largest service companies is navigating the AI transition, with brutal honesty about what's working, what's not, and what's coming next.

See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Get Paid with Manny Medina is handcrafted by our friends over at: fame.so

S2E8: "You almost have to erase everything you learned in the 2010s" | Kellan Carter (FUSE)

Fri, 04 Jul 2025 08:04:23 +0000

We sit down with Kellan Carter, General Partner at FUSE, where he pulled back the curtain on what's really happening in AI investing, revealing why traditional SaaS metrics are misleading and how a new generation of founders is building completely different businesses.

The Revenue Quality Crisis Nobody Talks About

"For the last year, this came up in every single board meeting: how do I evolve my pricing?". Yes, founders chase vanity metrics, but the underlying unit economics are falling apart. Companies love their biggest customers but are losing money on them - a classic case of what Kellan calls "negative gross margin customers."

The wake-up call came when 20 AI voice companies launched in Q1, immediately cannibalizing each other's territory. "A minute is a minute is a minute," explains host Manny, "but customers aren't buying your drill - they're buying the hole it makes." This shift from selling features to selling outcomes is forcing a complete rethink of how software gets priced and sold.

The 2010s Playbook is Dead

The old SaaS formula was simple: raise capital, hire sales reps, hit quota, repeat. But when your product ties directly to customer outcomes, this linear scaling breaks down completely. "You almost have to erase everything you learned in the 2010s era," Kellan explains. The bridge between sales teams and product teams is collapsing as companies realize sustainable growth requires tighter coupling between what you build and what customers actually achieve.

Mission-Critical AI: The Ultimate Moat

While most AI companies compete on price and features, a small subset is building something entirely different: mission-critical infrastructure. Kellan highlights a 24-year-old founder building AI for 911 dispatching - technology that makes life-or-death decisions about emergency response priority.

"No one in public policy buys the cheapest option for 911.”. When your AI determines whether to dispatch SWAT or community police, quality trumps cost every time. These companies aren't just building software; they're becoming "the routing engine for how our world is governed from a safety standpoint."

The Great Professional Services Disruption

Imagine managing 2,000 operations people, except they're not people - they're AI agents working 24/7 without HR complaints or equity demands. Kellan sees freight forwarders, law firms, and insurance brokerages getting completely reinvented from the ground up. "What if a lawyer could handle 5,000 clients instead of 200?"

The question isn't whether this transformation will happen, but whether existing companies can adapt fast enough or if new entrants will capture the value. Companies with established relationships have trust and distribution, but startups have AI-first culture and 90% lower cost structures.

The BPO Acquisition Wave Coming

Traditional Business Process Outsourcing companies like Cognizant and Wipro aren't sitting idle as voice AI startups attack their core business. Kellan predicts a wave of strategic acquisitions: "It'll be really interesting to see who blinks first and buys a voice AI company, saying 'I know we built this for 100 years, but we're throwing it away and going AI-first.'"

The moment one major BPO makes this move, every competitor will follow within months, transforming an entire industry overnight.

Why VCs Now Ask "How Good is the Founder?" First

FUSE completely restructured their partner meetings to prioritize founder quality over growth metrics. "It's really easy to say I went from 0 to $1M last month, but how defensible is that?" The firm learned that early revenue can actually hurt long-term scaling when founders optimize for quick wins at the expense of sustainable architecture.

Quality founders think 10 moves ahead, building systems that can scale infinitely rather than hit short-term milestones. They'd rather grow slower and capture larger long-term outcomes.

The Vertical AI Investment Thesis

Kellan’s investment focus at FUSE centers on "vertical AI solutions" - companies where AI solves specific business outcomes rather than being the product itself. FUSE avoids the expensive model layer battle, instead targeting industries like logistics, insurance, and freight forwarding where AI can automate 70% of operational workflows.

The firm's dream investment? "The first software-enabled insurance brokerage. Software first, people second." Or a freight forwarder where AI agents handle all the document processing, carrier coordination, and customs management that traditionally required massive human operations teams.

What This Means for Founders

The companies winning this transition share common traits: they're tying pricing directly to customer outcomes, building defensible moats through mission-critical functionality, and optimizing for revenue quality over growth velocity. Most importantly, they're not trying to apply 2010s scaling tactics to 2020s outcome-based products.

As Kellan puts it: "Quality of revenue is evolving really, really fast in this AI world." The founders who understand this shift early will build the next generation of enterprise infrastructure. The ones who don't will become cautionary tales about the dangers of optimizing for the wrong metrics.

Companies mentioned

Aurelian - 911 dispatching AI voice agent
Owl - Insurance fraud detection and claims automation
Quandri - Insurance brokerage workflow automation
FreightMate - Voice agent for freight forwarding
Avante - HR/health tech AI company
Pictory - Video editing company
Carbon Robotics - Laser weeding systems for farmers
Symbiosis - Ad tech company acquired by DoorDash
Splunk - Data analytics platform
Boeing - Aerospace company
Blue Origin - Space company
SpaceX - Space exploration company
Amazon Kuiper - Satellite internet constellation
Starlink - Satellite internet service
Flexport - Freight forwarding platform
ServiceNow - Enterprise IT service management
Salesforce - CRM platform
Workday - HR and financial management software
Zuora - Subscription billing platform
Cognizant - IT services and consulting
Wipro - IT services company
Tata - Indian multinational conglomerate
Vapi - AI voice platform
Bland - AI voice platform
Nike - Athletic footwear and apparel
Costco - Wholesale retailer
Amazon - E-commerce and cloud computing
DataDog - Monitoring and analytics platform
DocuSign - Electronic signature platform

See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Get Paid with Manny Medina is handcrafted by our friends over at: fame.so

S2E7: Will AI Agents Kill Customer Service Jobs? | Alexander Matthey (Parloa)

Fri, 27 Jun 2025 08:48:18 +0000

We sit with CTO and co-founder of Parloa, Alexander Matthey, where he's building the AI agents that promise to revolutionize how businesses interact with their customers. Unlike the doomsday prophets predicting mass unemployment, Matthey paints a more nuanced picture of what's coming.

In our latest podcast episode, we dive deep into the transformation of customer service, the survival strategies for BPOs, and why building the "Ferrari" of AI platforms matters more than ever.

The Speed Game Has Changed Forever

Starting a company in AI isn't like starting one in fintech or beauty tech. Matthey learned this the hard way after his experiences at Adyen and Glossybox.

"I think the biggest differentiator is, I think I was always obsessed with speed and I was also obsessed with focus. However, currently the industry is moving very fast as well and also there's so much noise and so much things that can disturb you so much more than in FinTech and so much more than in Beauty."

The challenge is of course maintaining focus while the entire industry shifts beneath your feet. Where founders once competed against each other's clock speed, they now race against the market's relentless pace of innovation.

The Radical Vision: People Will Actually Want to Talk to Customer Service

Here's where Matthey drops his most controversial take. Parloa is automating customer service and potentially reimagining it entirely.

"We believe that in the future people will want to talk to somebody at customers. And that's what Parloa is building towards... People will not just reach out when they have a problem like it is with customer service right now, but they will prefer to talk to their personalized AI agent going forward."

Opening hours, language barriers - all moot points. Instead, you have a personal AI agent that knows your entire history and can handle complex requests across multiple channels and languages.

Customer Service Agents: Evolution, Not Extinction

But what happens to the millions of customer service representatives worldwide? Matthey doesn't sugarcoat the transformation, but he also doesn't predict their demise.

"I think the job of a customer service agent will not completely go away, but it will become much more specialized on very complex use cases, on very specific use cases. So therefore it will also be more fun."

The mundane password resets and tracking inquiries? Those are gone. What remains are the complex negotiations, the emotional support calls, and the edge cases that require human judgment and empathy. It's a fundamental shift in the role, not an elimination of it.

Why Enterprises Shouldn't Build Their Own AI Agents

With AI democratizing software development, why wouldn't large enterprises just build their own customer service agents? Matthey has a clear answer, drawn from his Adyen playbook.

"I think you want to control what's really important for your business. And I don't believe that the development resources of many large customers, many large enterprises are best used to build an agentic OS, to think about how to build simulations, evaluations for agents, how to build guardrails across different regions."

The complexity goes far beyond connecting to an LLM - there’s versioning agents, managing hierarchical tenant systems, ensuring compliance across regions, and building evaluation frameworks. These aren't core competencies for airlines, insurers, or retailers - and they really shouldn't be.

The Ferrari Strategy: Why Execution Beats Everything

In a world where everyone claims to have the same features, how do you differentiate? Matthey's answer cuts through the noise with a perfect analogy.

"It's the same with, I don't know, like a Ferrari. Can somebody else build a Ferrari? Yes. It's also only steel and the motor and the engine and some leather and stitching. Is somebody else capable of doing it? Well, that's the problem. So you need to get the right team together and you need to focus on really building that Ferrari."

The Jerry Maguire Moment: Building Modular from Day One

When pressed on how someone could beat Parloa at their own game, Matthey reveals a crucial insight that many AI startups miss.

"There is a strong lever into the industry, especially the enterprise industry, if you from the very start think about how to make it a modular system, to be honest. So that you do not only rely on one very specific agent SDK, one specific LLM, one specific STT or TTS provider."

Large enterprises have existing contracts, approved vendors, and specific compliance requirements. A one-size-fits-all solution won't work. Building modularity from the start by allowing customers to plug in their preferred LLMs or use their existing cloud credits. It will become a massive competitive advantage.

The Future of AI Agents: More Than Just Chat

Looking ahead, Matthey sees AI agents handling increasingly complex tasks, moving far beyond simple query resolution.

"What you see right now is that, I don't know, like the first use cases are intent recognition, or the first use cases are, you know, take over a few of the higher volume use cases, where I think that will grow over time and over time towards, you know, also being able to receive payments or also being able to not just help cancel a booking or refund the booking, but actually plan a whole trip."

The enterprise systems that need to expose their capabilities through APIs are often to blame for bad connectivity. As these barriers fall, agents will orchestrate complex, multi-step processes that today require multiple human touchpoints.

BPOs at a Crossroads: Adapt or Perish

For Business Process Outsourcers, the message is stark but not hopeless.

"I think the BPO's that actually start driving this change will have a chance, a good chance of being successful in the future as well... However, if they don't change, they will be out of business relatively soon."

The opportunity lies in becoming the bridge between AI capabilities and non-digital native industries. Airlines, insurers, and energy companies need partners who understand both their legacy systems and the new AI paradigm. BPOs that position themselves as transformation partners rather than just labor arbitrage will thrive.

The U.S. Expansion: Bringing European Execution to American Speed

Parloa's U.S. expansion follows a familiar playbook—one Matthey helped write at Adyen.

"We want to be a US company, there is some value in being global as well. If you're looking at the largest companies in the world, they are all global as well, and they will want to run their agents globally."

But this isn't just about planting a flag. It's about absorbing the American approach to innovation—the willingness to think "out of the box" that Matthey admires from the Ford vs. Ferrari story. By combining European execution discipline with American innovation speed, Parloa aims to serve global enterprises that need both.

The Path Forward: Value-Based Pricing and Outcome Focus

Perhaps the most fundamental shift Matthey predicts is in how AI services are priced and valued. And it’s a take we love at Paid.

"Going forward, yeah, I think it would be nice to be able to pay agents for the tasks that they have successfully achieved and actually deliver value. So that is our long-term goal."

Moving from time-based or consumption-based pricing to outcome-based models aligns incentives between providers and customers. It's a shift that mirrors the broader transformation in customer service - from measuring minutes to measuring satisfaction and resolution.

See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Get Paid with Manny Medina is handcrafted by our friends over at: fame.so

S2E6: Hard is a moat | Max Altschuler (GTMFund)

Fri, 20 Jun 2025 07:42:53 +0000

We hosted Max Altschuler, founder of Sales Hacker and GTMfund in our studio to talk about how AI is fundamentally reshaping go-to-market strategies and why he thinks most GTM software companies are in trouble.

After a near-death experience, Max made a radical life change - selling his dream home in Scottsdale and going "asset light" to prioritize nature and lifestyle over traditional success metrics. As he explains:

"I really am just the happiest person when I'm in nature. I'm a better father, partner, friend, and I'm better at my job. Like I need to be in nature."

Max's GTMfund has a unique model with 300 software executives as LPs, creating what he calls the ultimate sourcing engine. But he's avoiding investments in GTM software entirely, seeing a "cloudy, soupy layer" where even successful companies like Outreach face existential threats from their own efficiency gains.

"If you can get 20% more capacity out of your sales force... you just need to hire 20% less reps to get the same productivity."

The conversation revealed several counterintuitive insights about building sales teams in the AI era. Max advocates for quality over quantity - hire "absolute killers" from companies like Outreach, support them with growth engineers, and focus on what he calls "hard as a moat" strategies.

"When only two emails came into that VP of sales saying 'congrats on the Series B,' it worked. Now when they get 600 emails, they mute all of them."

Max is bullish on vertical software and global infrastructure plays, pointing to massive opportunities in untouched industries. His recent investment in air traffic control technology exemplifies his thesis - find industries with no innovation for decades, then back exceptional founders to transform them.

"Hard is a moat. Like it was hard for me to go uncover who raised the round and do the work. Now that that is like one click."

Perhaps most interesting is Max's take on community-driven growth. Despite running Sales Hacker for years without cracking paid communities, he now sees them as more powerful than ever.

"Communities are stronger than ever... even if I don't make any money off my investment in the fund, I'm so happy I did this because of all the events and the community."

He also warns about the dark side of AI in customer support, questioning whether companies like Klarna are moving too fast in replacing human teams.

"As a customer, I'm not really sure I want to hear that... I'm spending $100K with you for SaaS software versus like $15 on an Uber ride. Those are two different customer support experiences."

What's emerging is a new playbook where "hard" becomes the ultimate competitive advantage, vertical markets offer the safest returns, and human relationships matter more than ever - even as AI automates everything else.

See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Get Paid with Manny Medina is handcrafted by our friends over at: fame.so

S2E5: Will AI Kill the SDR or Just Make Them Smarter? | Adam Schoenfeld (Keyplay)

Fri, 13 Jun 2025 12:08:39 +0000

We sat down with Adam Schoenfeld, co-founder and CEO of Keyplay, to explore how AI is transforming account selection and go-to-market strategies. Adam has taken his years of experience building Simply Measured and turned it into solving one of B2B's most persistent problems: figuring out which companies to actually sell to.

While everyone else is trying to use AI to spam more people, Adam sees a different future. "The biggest trap that a lot of people are falling into is they're seeing this as kind of a volume game. The best people are gonna use it to create more value for a more targeted group of people."

Keyplay's new AI agents are doing something remarkably different from traditional account scoring. Instead of just looking at firmographics like company size and industry, these agents can answer nuanced questions like "What's the security maturity of this company?" or "How do they prefer to buy software - legacy or modern?" As Adam points out, "A smart AE would take their territory and they go research these accounts. They really think about, like, what's the org dynamics? What's the kind of buying posture?"

One of the most eye-opening parts of our conversation was about the hidden costs of bad targeting. Adam shared how companies are burning massive amounts on LinkedIn ads targeting companies that will never buy from them. "How much of that spend is actually going to accounts that are legitimately never going to buy from you?" he asks. Keyplay can identify and eliminate that waste while helping teams focus on higher-quality accounts.

The pricing journey has been particularly interesting. They started with flat fees but quickly realized different use cases had vastly different complexities. "Some are super complicated and they're running across like a million records and some are very simple and they're running across 3000 records," Adam explains. This led them to adopt a credit-based model, though he admits "it does take some translation" to help customers understand the value.

What's refreshing about Adam's approach is his candid admission that this is all a work in progress. When I asked about convincing RevOps leaders who might be threatened by AI reducing headcount, he had an interesting observation: "I don't feel like I'm having to convince people of that too hard... they want outcomes. They want to do creative work. They want to be the one who understands the customer and has great taste."

Perhaps most valuable was Adam's masterclass on building an audience before building a product. Through his Pure Signal newsletter, he spent months researching go-to-market challenges and sharing insights, which ultimately led to discovering the problem Keyplay now solves. "I basically built like a mini media brand where people would subscribe to my newsletter... that led to discovering the problem that Keyplay solves now, because people would come in and they'd want to talk."

Looking ahead, Adam sees a future where Keyplay could orchestrate entire go-to-market campaigns, from identifying the right accounts to actually placing the ads. "It would be incredible if we could just say, yeah, turn on Keyplay, we will each month rejigger the accounts you're targeting and we'll just deliver you pipeline."

Whether you're in RevOps, sales, marketing, or building an AI company, this conversation offers crucial insights into how AI is reshaping B2B go-to-market strategies - not by doing more of the same, but by fundamentally rethinking how we identify and pursue the right customers.

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S2E4: AI is Making Sales Teams MORE Important, Not Less | Sahil Mansuri (Bravado)

Fri, 06 Jun 2025 12:24:32 +0000

In this eye-opening episode of Get Paid, I spoke with Sahil Mansuri, founder and CEO of Bravado, about how AI is dramatically reshaping the landscape of sales and recruiting but not in the way most people think.

While many SaaS companies are seeing their business models threatened by AI's ability to automate human work, Bravado is experiencing the opposite effect. As Sahil explains, "Because we were in recruiting and what we did was more services heavy than software heavy, venture capitalists used to be less excited about our business." However, the rise of AI has transformed Bravado's economics completely. "Now we're operating at an 85 to 90% software margin because AI has basically taken over and is doing 95% of the work that the human being was doing," he reveals.

Bravado helps companies hire top-performing salespeople through their AI-powered recruiting tool called Hunter. What makes this particularly interesting is that while AI is automating many roles across industries, sales is one function that remains stubbornly human-dependent. As Sahil points out, "You can build AI engineers and AI marketers and AI doctors. But if you want that AI product to get adopted by Siemens, you're going to need a real life fleshy human being to fly to Germany, sit there and meet with 40 different executives."

This creates a fascinating dynamic: as companies like Cursor, Anthropic, and others develop powerful AI tools that eliminate jobs in other departments, they're simultaneously building massive sales teams to sell those very products. The value of great salespeople isn't decreasing, it's increasing.

Sahil shared several counterintuitive insights about hiring in today's market. Companies often make the mistake of hiring from big tech logos when scaling up, but "that person who joined Stripe when they were at a billion dollars and now took them to 1.4 or 5 billion is not the same person that's gonna take you from five to 100." Instead, Sahil recommends hiring people who are "overperforming at a shittier company than yours" because giving them better tools and resources will allow them to excel even more.

Regarding the skills that matter most for sales professionals today, Sahil emphasized the growing importance of technical knowledge and genuine subject matter expertise. The days of "quarterbacking" a deal by bringing in specialists to handle the technical questions are over. Today's top AEs need to be able to answer complex questions themselves, especially when selling technical products.

One of Sahil's most provocative claims is that prospecting is making a comeback. After years of relying on automated outreach tools, companies are rediscovering the value of personalized outbound like calling, relationship building, and social selling. "Once you get them into the door and you get them on the pitch, most people have a really tight funnel. Most good sales teams are able to close. The problem's always top of funnel," Sahil explains.

Perhaps most controversial is Sahil's take on AI sales development representatives (AISDRs). While there's been significant hype around AI completely replacing SDRs, Sahil believes "that job is never going to exist" for high-value enterprise sales. "My very strong opinion is that as more and more companies build more and more automation and AI products, the demand for hiring more salespeople will go up. The compensation of sales professionals will go up."

He points to companies like Slack, which initially prided themselves on having no salespeople, only to eventually get "crushed by Microsoft for not having enterprise salespeople." Even Jack Dorsey at Square recently acknowledged during an earnings call that they need more salespeople after realizing that their product-led approach wasn't enough.

What's emerging is a new model where AI handles the mundane, repetitive tasks like data entry, scheduling, follow-ups - all while human salespeople focus on high-value activities like building relationships and solving complex problems. This shift is also happening in recruiting, where Hunter automates the tedious parts of the hiring process so that human recruiters can focus on closing top candidates and ensuring cultural fit.

As businesses navigate this transition, Sahil believes companies will need to move away from per-seat pricing models toward outcome-based approaches that reflect the value created by AI. This aligns perfectly with our mission at Paid, as we help companies capture the true value of their AI-powered products and services.

Whether you're building a sales team, developing AI products, or just trying to understand how technology is reshaping business, this conversation provides invaluable insights into the future of work in the age of AI.

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S2E3: $15 Trillion Mortgage Industry Being Disrupted By AI | Jim Cutillo (Alpha7x)

Fri, 30 May 2025 09:04:46 +0000

In the latest episode of Get Paid, I had the pleasure of speaking with Jim Cutillo, a 30-year veteran of the mortgage industry and founder of Alpha7x. His company is transforming mortgage lending with AI agents that cut costs and streamline the entire process.

If you've ever bought a home, you know the mortgage process can be a nightmare. What you might not know is that it's actually getting worse, not better.

Jim shared a startling revelation: despite decades of technological advancements, the mortgage industry has become less efficient. "The cost today is at a record high for producing a mortgage," he explained. The time it takes to close a loan has actually increased by 10-12 days compared to just a decade ago.

The numbers are eye-opening. It costs about $12,000 to manufacture a mortgage loan today. Manual tasks account for approximately $2,200 of that cost. These costs get passed directly to consumers through higher interest rates. Consumers pay for these inefficiencies over the entire 30-year term.

Why is this happening? Jim points to fragmented systems and siloed data. "You get stuck staring and comparing data and documents against system data all day long," Jim explains. "As more technology was introduced, you've created more complexity."

Jim's solution is simple: Alpha7x has created an "army of digital AI-based agents" designed to replace human labor across the mortgage supply chain. These aren't just chatbots. Alpha7x has built sophisticated agents that handle complex tasks like processing mortgage loans, reviewing closing documents, and performing compliance checks.

Jim shared a compelling example: "We did a proof of concept for a large bank doing OFAC checks. I was shocked at the number of people they have doing the process today—15 people manually doing this process. With our agent, they'll need three people."

Perhaps most interesting is Alpha7x's business model. They use outcome-based pricing, not seats or licenses. They only make money when they save clients money. "Our incentives are aligned right out of the gate," Jim says. This approach stands in stark contrast to both SaaS vendors who charge licensing fees regardless of outcomes, and Business Process Outsourcers who simply move labor offshore.

Alpha7x has also built a tool that allows customers to create new agents without writing code. "We intend to put the power of these agents in the hands of our users," Jim explains. Organizations can rapidly deploy new automation. "They can configure that and have an agent working for them in two weeks."

Jim is applying the same principles to his own company that he preaches to clients. "How are we going to ramp this company to 10 million ARR and have less than 20 people? Because that's my goal," he says. "I'm not going to build a people company when I'm trying to talk to people about building agents."

This puts Alpha7x in the emerging camp of "$1 million per employee" AI companies. "Drink your own Kool-Aid," Jim advises. "If we're building agents, we might as well act like agents and build a company that is run by agents."

While Alpha7x is focused on mortgages today, Jim sees potential far beyond this market. "We could do this in insurance. We could do it in other industries that are data and document centric," Jim says. What sets Alpha7x apart is their deep domain expertise. "What we're building is a micro LLM, a private LLM that understands mortgage regulations and guidelines. That's a much different animal to build."

Alpha7x is in an enviable position when it comes to funding. "We have investors knocking on our door that want to invest, and I'm like, 'Okay, we're fine,'" Jim says. "Everybody's like, 'When are you going to do a Series A?' I'm like, 'What would I do a Series A for?'"

He continues: "This will print money. It's a high-margin business. We should be able to reinvest that cash in development of the product. From a sales perspective, we haven't even hit go on the sales side, and we're getting calls every day."

Jim's advice for entrepreneurs looking to build in the AI agent space is to find domain-specific problems. "Find a niche in a market, own that niche, and expand it," he suggests. But technical expertise alone isn't enough. "You need somebody with domain expertise on your team."

With a $15 trillion mortgage market ripe for disruption, Jim Cutillo and Alpha7x are proving that AI agents can transform even the most document-heavy industries.

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Get Paid with Manny Medina is handcrafted by our friends over at: fame.so

S2E2: Building a $1B Company with Only 100 Employees | Elias Torres (Agency)

Fri, 23 May 2025 11:03:33 +0000

Elias Torres, founder of Agency and formerly Drift (which sold for $1.2B), joins us to explain why he considers that exit his "biggest failure" and how he's reimagining business building in the age of AI with his new company, Agency.

The Billion-Dollar Failure That Wasn't

Despite selling Drift for $1.2 billion, Elias surprisingly calls it his "biggest failure." Why? Because his ambition was to build a $30 billion company that would go public:

"My mind for Drift was we were gonna build a $30 billion company. We were gonna take it IPO... And that did not happen, right?"

This stark honesty sets the tone for our conversation about what truly matters in company building.

The "100 People to $1B" Constraint

Elias's new mission is radical: build a billion-dollar company with only 100 employees. It's not just a catchy slogan—it's a deliberate constraint:

"I'm trying to create a constraint, right? Because a constraint forces creativity. You have to learn how to manage cost."

In his view, when companies grow to hundreds or thousands of employees, the mission changes from serving customers to managing humans:

"When you have too many people, it becomes a whole different job... Your focus is how do you feed all those mouths? How do you guide them? How do you coach them? How do you manage their emotions?... It becomes no longer the mission of the company."

Why "Seat Pricing Is Dead"

Perhaps the most provocative stance Elias takes is on pricing. He doesn't mince words:

"I think that seat pricing is dead. I think people ask me all the time and they say, how are you planning to charge it? You know, is it per seat? I think it's a stupidity to charge per seat nowadays."

Instead, he's eyeing a percentage of customer revenue—the ultimate alignment:

"Why couldn't I have a percentage of your revenue? And that's what I want. Right? It's like, if you don't make money, I don't make money."

First Hire: A Lawyer, Not a Recruiter

One fascinating contrast: Elias's first hire at Drift was a recruiter. At Agency? A lawyer. This subtle shift reveals everything about his new approach to scaling:

"Most companies delay hiring someone like that. And the founders spent all this time doing all the legal, all the contracting, all the payment, all the financing, all the budgeting."

By immediately delegating administrative functions, he's guarding his most precious resource—time.

The Extrovert Becoming an Introvert

In a moment of candid self-reflection, Elias (a self-described extrovert) admits:

"I'm becoming more of an introvert with age, right? And I realized that I don't want to interact with people, especially if it is to troubleshoot something for 20 days in a row. It's like, why can't it just work?"

This personal evolution mirrors his professional thesis: humans are the "hardest roadblock" to AI adoption, yet they'll increasingly prefer automated solutions that just work.

The Secret Lever Most Founders Miss

The conversation took a turn when Elias revealed the most underrated growth lever:

"Pricing is the most important lever in terms of effort... you make one subtle change to your CPQ or to how you or the number that you charge as the platform price... And that could make or break your business, right? You can increase revenue 50%, you can double revenue."

For founders chasing product excellence or distribution, this reminder about pricing's leverage feels like uncovering a secret weapon.

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Get Paid with Manny Medina is handcrafted by our friends over at: fame.so

S2E1: Is Controversy the Secret to Winning in AI Sales? | Jaspar Carmichael-Jack (Artisan)

Wed, 14 May 2025 14:32:15 +0000

Jaspar Carmichael-Jack, Artisan's CEO, revealed his controversial strategy: deprioritizing everything except their main AI outbound agent, Ava.

Why focus solely on outbound when everyone's building multi-product AI companies?

Jaspar's explanation cuts through the typical startup playbook:

"We have a lot of product service area to cover that we haven't covered yet. And we've seen with every incremental release we've done so far, customer outcomes just explode every single time”.

"I feel like we have to earn the right to [build multiple products] before we can just go multi-product immediately".

In a world where startups chase expansion before mastery, Artisan is doubling down on depth over breadth.

The "Stop Hiring Humans" Campaign That Got Death Threats

Perhaps the most eye-opening part of our conversation was when Jaspar revealed the backlash to their provocative billboard campaign. "The most unexpected reaction? I'd say... the death threats."

The campaign coincided with the UnitedHealthcare CEO shooting, creating a perfect storm. Jaspar's Instagram "was just full of people commenting Luigi Gifs" (a meme about sending hitmen).

Despite this, the campaign drove millions of website visitors and established Artisan as the name to beat in the AISDR space.

The Evolution of AI Pricing Models

Artisan is experimenting with multiple pricing approaches:

Traditional volume-based pricing (per lead contacted)
Outcome-based pricing (per positive response)
Potentially moving to a credit system to handle varying costs

Jaspar acknowledged: "I think our pricing model is going to evolve a lot over the next year... we're not really happy with where it sits right now."

The Response Rates Most AISDRs Won't Tell You

When we pushed Jaspar on actual performance metrics, he gave surprisingly transparent numbers:

"The average customer response rate will usually be between like one and three percent. Positive response rate will be between point two five and one percent."

Outliers are, however, getting 2.5% positive response rates... selling ice cream cones to ice cream shops 🍦

Humans & AI: The Messaging Evolution

Artisan is evolving their positioning from the provocative "stop hiring humans" to a more nuanced "humans and AI working together."

"You still need humans in outbound and I think you'll still need humans in basically every role unless it's a back office data entry role that you can truly automate."

This suggests Artisan is maturing in its vision - from pure replacement to augmentation.

From "Vibe Coding" to Building Real Product

In a refreshingly honest moment, Jaspar admitted their initial product was rough:

"Yeah, it was complete trash... the first release... our UI was this horrible purple mess. It looked like a children's game."

"I did this webinar where I led everyone through the onboarding process and they signed up live and the product broke like four times during the webinar."

But this transparency revealed Artisan's secret: dominate distribution first, then build product excellence.

Your Takeaway From This Episode

Distribution beats product early on. Jaspar explained:
"Especially in this space and in almost every similar vertical AI space, you can't build a differentiated product in six months... the only edge you can build fast is distribution."
Artisan built a sense of brand leadership through marketing before they had a truly excellent product. Now they're investing in both.

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Get Paid with Manny Medina is handcrafted by our friends over at: fame.so

S1E11: Agent Talk #11 - Nick Mehta (Gainsight) - Most SaaS companies will die in the AI revolution

Wed, 30 Apr 2025 14:52:59 +0000

Nick Mehta, Gainsight’s CEO, didn't mince words in our latest conversation. He says the entire enterprise software model we've built over the past 20 years is fundamentally broken.

What doesn’t work about SaaS anymore?

The model that made Salesforce, Workday, and countless others into tech giants? According to Nick, it doesn't work anymore:

"We bring in data from disparate systems, we use some kind of ETL to combine it, we have rules that say if this, then that, we have workflow for humans to take action, and then reports for managers. I argue that model's totally broken."

Why? Because:

* Users have to take manual action

* Ops teams define rigid rules that can't adapt

* Leaders review reports on low-quality data that users entered

* Nothing happens autonomously

Nick framed it in a way that we love, because we feel the same way.

"The whole SaaS industry, and maybe all of humanity, is going from 0 to 1 again."

The Three Phases of AI Transformation

What's replacing the old model? Nick outlined three evolutionary phases for how AI transforms SaaS:

* AI as an Assistant: Like an executive assistant for employees – helping write emails, summarizing meetings, prompting within existing workflows

* AI as an Analyst: A layer that analyzes all communication in real-time, reveals patterns, and gives leaders visibility they never had before

* AI as an Agent: Fully automated workflows that take action without human intervention

Most companies are stuck at Phase 1 because they're afraid to cannibalize their existing seat-based business models.

Classic innovator’s dilemma!

The vibe revenue problem few are talking about

Perhaps the most eye-opening part of our conversation was when Nick revealed what he's seeing in the market:

"If you're an AI company, you think you have customers. They think they're doing a trial."

He explained the disconnect happening across the industry: AI startups claim big enterprise deployments, but when you talk to those same customers, they describe it as "just a proof of concept" or "playing around with it."

Even more shocking: "Some of them have massive churn... like 70% churn!"

We call this vibe revenue!

The innovator's dilemma in action

We asked Nick how established companies like Gainsight navigate this transition, and his answer referenced Clayton Christensen's famous book:

"Almost every time there's a discontinuous change in business, most companies don't make it because they're not willing to say, 'our old stuff doesn't make sense anymore.'"

He's taking his own medicine too. Rather than spreading his new CTO across all of Gainsight, he's focused him exclusively on their new agentic initiatives - acknowledging they need separate focus to avoid being hampered by legacy thinking.

Will Your Headcount Survive?

When we pushed Nick on what happens to customer success teams as these agents take over, he was surprisingly candid:

"Short-term, you're replacing atomic tasks, which is easier to digest... Long-term, yeah, you probably end up replacing seats."

"This is going to be awesome for the customer."

Nick believes we'll see dramatic consolidation of customer-facing roles. No more need for separate CSMs, TAMs, support people, and account managers - the agentic layer will allow fewer humans to cover more ground.

The surprising answer to AI deployment failures

Nick's final insight shocked us. When asked why so many AI deployments fail despite the amazing technology, his answer had nothing to do with models or features: "Human change is really hard. We're so inertial."

His prediction: companies deploying AI in enterprises will need consulting practices to help re-engineer workflows:

"If you're an enterprise company deploying one of these AI tools, if you don't have somebody, some advice and consulting resources, you're going to buy the AI tool and you're going to churn it - guaranteed."

Your takeaway from this episode should be that most SaaS companies won't make this transition. They'll keep trying to fit AI into their existing paradigms rather than fundamentally rethinking what software even is.

If you want to succeed, do what Nick says: Understand that stuff don’t make sense anymore, and be honest with your employees and customers about it.

What do you think? Is your SaaS company ready to make this jump? Leave a comment below 👇

Watch the full podcast here or wherever you listen to podcasts:

👉 https://podcasts.apple.com/us/podcast/agent-talk-podcast/id1792748956?i=1000705561812

👉 https://open.spotify.com/episode/2hd0evHQIByeAbIAT5sIYN?si=MCL2qytsT1GExCCX-oIb5g

👉 https://www.youtube.com/watch?v=HgePQ9sgSXg

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit agenttalk.substack.com

See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Get Paid with Manny Medina is handcrafted by our friends over at: fame.so

S1E10: Agent Talk #10 - Anshul Gupta (Actively) - Real Relevance, Not Lazy AI Personalization

Fri, 11 Apr 2025 16:07:10 +0000

In our latest episode, we talk with Anshul Gupta, who recently raised $22.5M from Bain Capital and First Round Capital for Actively.

Our favorite takeaways:

* Personalization at scale is actually "laziness at scale": "The amount of emails I got saying 'Anshul, congrats on your recent funding round' followed by a totally unrelated value proposition... if you're looking at thin logic like 'someone raised funding, throw them in a sequence,' that's not working anymore."

* Top performers do less, not more: "When you sit down with the best reps, they're not the ones doing the most volume. If you look at the leaderboard, they may even be in the bottom half from an activity perspective. What we see is they're incredibly targeted and laser-focused."

* Relevance beats volume: The alternative to personalization is "relevance at scale" – going incredibly deep with research, building proper hypotheses based on customer needs, and focusing on quality over quantity.

* AI flips the paradigm: "The revenue orgs in two years will be flipped on their head. Systems of intelligence will, instead of humans asking them what to do, be processing all information and guiding humans where to spend their time."

* AISDRs have limited applications: "If you're a company just starting up with no sales reps and want to get meetings by hook or by crook, AISDRs could work. But fundamentally upmarket, in complex ecosystems with multi-product motions, that model doesn't work."

* The revenue frontier concept: "Rather than constraining headcount and increasing productivity, increase your ambition. Companies are nowhere near their revenue frontier, and imperfect go-to-market execution is a huge contributor to that gap."

* Custom reasoning models: "Ramp has their own Actively reasoning model, Ironclad has their own – trained on the hyper-specific nuances of their products, go-to-market motions, and as those continue to evolve."

What makes Actively different:

* Their focus is increasing rep productivity and the quality of pipeline each human can generate

* They train custom reasoning models specific to each client's business

* They deploy a "forward deployed engineering model" similar to Palantir to customize for enterprise needs

* The system continues to improve through active learning (hence the name!)

What's not working in the market:

"All these volume-based solutions purport to help you send more emails or do more calls. It's all about chasing volume when the top performers are actually doing less but with more focus. That thin logic of 'if someone raises funding, throw them into a sequence' is what's corrupted the concept of personalization at scale."

What do you think about Anshul's take on personalization vs. relevance? Are you seeing "laziness at scale" in your inbox? Have you identified the revenue frontier for your business? 👇

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit agenttalk.substack.com

See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Get Paid with Manny Medina is handcrafted by our friends over at: fame.so

S1E9: Agent Talk #9 - Doreen Huber (EQTV) -We're Rethinking SaaS

Thu, 03 Apr 2025 12:30:00 +0000

In a candid conversation at Paid’s launch party, EQT Ventures investor Doreen Huber shared her razor-sharp perspective on what’s working in AI agent investing, why traditional SaaS is losing ground, and what founders need to bring to the table to secure funding in this new era.

Our favorite takeaways:

* SaaS is no longer the focus to the B2B Software team at EQT Ventures:"My team is not investing in traditional SaaS anymore. Our strategy is to go for agentic, AI-native companies, and we tend to disqualify what doesn’t fit that bucket."

* True agents only:"We only want to support companies doing something end-to-end—not just enhancing customer care with AI-drafted emails. We’re looking for agents that do the actual work from start to finish."

* Commercial DNA matters:"I definitely have a thing for founders with commercial DNA. If someone comes from an engineering side, they absolutely need to learn this... the best CEO is also the best product person."

* Founder qualities:"I personally love the outliers, the underdogs, or someone with a crazy CV. I'm not into the typical business school, textbook founder. I love it when someone shows up with an edge."

* Legacy SaaS is under pressure:"Many legacy SaaS companies will lose market share to agentic players. A lot of them are struggling—they don’t have the AI talent, and they’re stuck in outdated stacks."

* On industry hype:"Some of the big players are slapping AI labels onto old products. That’s not agentic innovation. That’s legacy software trying to catch up."

What Doreen is looking for now:

* Enterprise-ready agentic sales and marketing solutions – not just slim use cases, but holistic systems

* Agentic cybersecurity – solving modern threats with AI-native architecture

* Vertical AI applications – especially where AI is applied to labor, not just software budgets

What’s working:

“Most companies moving faster than others have that AI-native mindset. They want lean teams and ask: ‘Can we do this with agents instead?’”

What’s not working:

“BDR email sequencing or scheduling tools... they look impressive at first, but in reality, these problems won’t exist in a year or two. That’s just a GPT anyone can build.”

What are your thoughts on Doreen's take that traditional SaaS is finished? Do you agree that legacy companies can't catch up with AI-native startups? 👇

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit agenttalk.substack.com

See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Get Paid with Manny Medina is handcrafted by our friends over at: fame.so

S1E8: Agent Talk #8: Amos Bar-Joseph (Swan) - The 10M ARR per employee dream with AI

Tue, 01 Apr 2025 11:36:16 +0000

In our recent conversation with Amos Bar-Joseph, CEO and co-founder of Swan AI, he shared his radical vision for the future of business: autonomous companies achieving $10M ARR per employee through strategic AI implementation. Beyond the typical AI hype, Amos details a practical roadmap for companies looking to scale with minimal headcount.

Here are the key insights from our discussion:

The autonomous business revolution

* The next 5 years belong to SMBs: "We believe that the next five years are the year of the SMB, the year of the small business, small lean teams that are showing the world that using AI agents, you can reach massive scale, like never seen before."

* Rethinking business fundamentals: "It's about reimagining how humans and AI collaborate together, rethinking fundamentally the operating system of a business."

* The Swan Metric: Targeting $1M+ ARR per employee (with Swan pushing toward $10M) through strategic AI implementation.

Breaking the "throw bodies at problems" mindset

Amos said something that we think many founders have thought about: "The first instinct that the old playbook got you to do was throw bodies at the problem, right?".

But Amos does things a bit differently, and we like that a lot:

* Self-imposed constraints drive innovation: At Swan, they created the constraint that they "can't throw bodies at a problem" - forcing them to find more creative and intelligent solutions.

* “Ops Wizards”, not just more headcount: "Every team should have that ops wizard" who can bridge technical understanding with business orientation.

* Human-in-the-loop design: Always start with humans supervising AI and providing feedback to create "a self-learning system that takes your knowledge in a collaborative way."

The future of sales is human + AI, not AI replacing humans

When I asked about which parts of the sales cycle will be replaced by AI, Amos offered nuanced insight:

* It depends on ACV: "The higher the ACV, the tougher it is to replace any part of the sales cycle... When you look at $20 million deals, then you want a human in the loop."

* Low ACV should be marketing-driven: For $19/seat products, "from a unit economics standpoint, it doesn't make sense to do outbound."

* Sellers love winning, not prospecting: "Sellers love winning. And for that 1% that they are winning, they love that notion... It's the best moment of their day when they get that yes on the screen."

* The 100X seller: "The future of sales is first of all reimagining how sellers work with human and AI collaboration at the core. And it's more about finding the path to the 100X seller."

The three types of businesses in the AI revolution

According to Amos, businesses will fall into three categories across a spectrum:

* Biggest losers: "Those trying to automate their workforce, replace their workforce with digital workers. And they will be left behind."

* Partial winners: "Implementing AI agent tooling across their entire stack... but disparate solutions for different functions."

* True unicorns: "Lean teams that are building AI agents as their product, able to rally their entire agentic workforce around that specific agent."

Practical advice for outbound sales:

Amos had some pretty practical advice too: Move from “digital workers" to storytelling engines.

How?

* Goodbye sequences, hello relationships: "We never pitch with a hard CTA. Never... It's only about, look, something happened in your business. There's an event that is relevant to your day-to-day as a VP of sales."

* Non-deterministic outreach: "Swan looks at a timeline always, looks at what happened before, what is happening now, and will create a recommendation how to engage that lead."

* Monitoring relationships at scale: "What we're envisioning is a future where SWAN can monitor relationships, top of the funnel relationships with thousands of accounts in parallel."

What's your take? Are you seeing companies in your industry successfully implementing AI to scale without proportional headcount growth? 👇

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S1E7: Agent Talk #7: Pat Grady (Sequoia) - What actually works in AI startups

Wed, 26 Mar 2025 14:40:14 +0000

Our favorite takeaways:

* Building AI companies is just building a company. It’s 95% the same and people problems still dominate

* Trust is the critical design pattern most AI companies miss. Users need to see how you arrive at your results

* Most AI products achieve 80% functionality quickly, but the final 20% takes 5-10x longer and is what builds actual trust.

* The greatest moat in AI isn't data or tech - it's founders with relentless execution.

Pat also added some extra wisdom that we appreciate:

* The "data flywheel" appears in 100% of AI pitches but only 1% of companies actually demonstrate it works - Pat demands evidence, not theory

* AI pricing will standardize around outcome-based models with huge variation - the most successful companies think about both "input" (work done) and "output" (value created)

* For investors, negative gross margins are acceptable in early AI companies because token costs are dropping 99% and multi-tenancy is becoming more accepted

* Domain-specific AI products that build real trust can carve out defensible positions against foundation model providers in vertical markets

* The most successful AI companies avoid "vibe revenue" (temporary excitement) by focusing on engagement and retention using consumer internet metrics even for B2B products

What's your experience with AI products and pricing models? Have you found user trust to be the limiting factor? Share your thoughts below 👇

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit agenttalk.substack.com

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S1E6: Agent Talk #6: Wade Foster (Zapier) - Why Zapier Sacrificed Millions to Earn Millions

Thu, 13 Mar 2025 14:33:40 +0000

Wade Foster, co-founder and CEO of Zapier, shares how they completely revamped their pricing model - and actually REDUCED revenue in the short term - to drive explosive long-term growth.

Since launching in 2011, Zapier has grown to help millions connect their apps without code, reaching a valuation over $5B with minimal VC funding. Their recent pricing change offers fascinating insights for SaaS founders.

Key takeaways:

* Zapier eliminated their dual-metric pricing (Zaps + Tasks) to simplify the customer experience

* They made unlimited Zaps available on all plans - removing a major friction point

* Every plan now includes pay-as-you-go options beyond the base subscription

* They stopped counting utility features as billable tasks - providing more value

The result? Short-term revenue dropped significantly, but task consumption and customer happiness soared. This bet on long-term growth would have been impossible for most VC-backed companies.

More insights:

* Being profitable and bootstrap-focused gave them freedom to make radical customer-first decisions that sacrificed short-term revenue

* Pricing "debt" accumulates over time when you experiment with different models - eventually requiring a reset to first principles

* The best pricing aims for customer "love" - Wade literally used this word - not just reluctant payment

* Competition constantly forces pricing innovation - especially with direct competitors who counter-position against market leaders

* AI is rapidly democratizing entrepreneurship - Wade sees teams of 10-20 people reaching millions in revenue with minimal engineering, driven by domain expertise paired with no-code tools

What pricing changes have transformed your business? Share your experience below 👇

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit agenttalk.substack.com

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S1E5: Agent Talk #5: Mickey Haslavsky (Enso) - SaaS is Dead, Long Live $49 AI Agents

Wed, 05 Mar 2025 16:03:32 +0000

Manny hosts Mickey Haslavsky of Enso, who's flipping the script on AI agents in the most refreshing way.

Mickey says their mission at Enso is to reduce the prices of services, just in general, for small businesses.

While lots of AI companies chase enterprise dollars or build complex multi-agent systems, Mickey's team is printing money with a simple contrarian approach: deliver "good enough" AI agents to small businesses at a flat $49/month.

Some key insights from our conversation:

* "SaaS is pretty much dead... some of the agents that we're building, you would have to raise like a seed round like three years ago, like raise like $5 million and have a team of like 10 engineers to build them."

* "I don't buy this story of losing jobs. What's really important is reaching those who couldn't afford an agency before, who couldn't pay $3,000 for SEO or $5,000 for social media design."

* "SMBs don't churn because they go out of business - they churn because they pivot constantly. That's why our unlimited access model works."

Mickey's belief that LLMs will become completely commoditized, allowing companies like his to use cheaper models from anywhere (including Deepseek) to deliver services that previously required expensive agencies.

The future might not belong to the fanciest AI, but to those who make it accessible, affordable, and actually useful for the millions of SMBs who just need to get things done.

What do you think? Is this the true democratization of AI, or are there limits to the "good enough" approach?

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit agenttalk.substack.com

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S1E4: Agent Talk #4: Rob Litterst (PricingSaaS) - SaaS Pricing is Dying as AI Agents Are Eating The World

Wed, 26 Feb 2025 17:04:05 +0000

Rob Litterst (HubSpot exec & SaaS pricing guru) just dropped some mind-blowing insights about how AI is completely disrupting traditional software business models. Whether you're building AI products or just trying to stay ahead of the curve, this episode is packed with spicy takes on where the industry is headed.

Three explosive themes that'll blow your mind:

* The Death of Per-Seat Pricing: The old way of charging per user is getting absolutely demolished by AI agents. Rob breaks down how companies are being forced to evolve or die as AI native startups come gunning for their lunch. Traditional SaaS companies are scrambling to figure out how to price their products when algorithms are replacing humans left and right.

* The Human Premium Paradox: Humans are becoming a premium feature in software. While AI handles the grunt work, companies are starting to position human expertise as the ultra-premium tier. But this creates an insane challenge: how do people gain expertise if AI is handling all the entry-level work? Rob dives deep into this existential crisis facing professional services.

* The Revenue Bloodbath is Coming: Companies that don't adapt their pricing models are about to get absolutely wrecked. Rob shares war stories about how AI native startups are growing to $100M+ ARR with tiny teams, while traditional players are stuck in old pricing models that don't match how value is created anymore. The race is on to figure out outcome-based pricing before it's too late.

This conversation gets into the nitty-gritty of how companies are navigating this transition - from baby steps toward outcome-based pricing to full-blown business model transformation. If you want to understand how AI is reshaping the fundamentals of software business models, this episode is a must-listen.

Whether you're a founder, product leader, or just fascinated by how AI is changing the game, Rob brings deep insights from both the trenches at HubSpot and his broad view across the industry. Don't miss this one - the future of software pricing is being written right now.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit agenttalk.substack.com

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S1E3: Agent Talk #3: Shawn Harris (Coworked) - How Coworked Solved The Impossible

Thu, 13 Feb 2025 16:23:06 +0000

"35% of projects fail, and the numbers haven't budged in 20 years."

That's what kept Shawn up at night before founding Coworked. After two decades in project management, he'd seen every methodology, tool, and framework come and go - yet the fundamental problems remained stubbornly unchanged.

Shawn is building an AI project manager that could transform how work gets done. But not by replacing humans - by unleashing them.

Harmony, Coworked's AI agent is rewriting the rules of project management. While most AI companies are focused on chatbots and automation, Coworked is tackling something far more ambitious: creating an AI teammate that handles everything from resource allocation to risk analysis.

What's fascinating is how they've navigated enterprise adoption. When they first pitched to project managers, they hit a wall of resistance.

But then they had an insight - they were talking to the wrong people.

PMO leaders immediately got it. Not as a way to reduce headcount, but as a solution to a persistent problem: how to handle 2x the projects without 2x the team.

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Project managers spend up to 80% of their time on process tasks rather than strategic work. As Shawn puts it:

"There's a subtle but crucial difference between replacing a job and replacing a role."

Harmony handles the routine while enabling humans to focus on what matters.

Their early results are turning heads. Fresh out of Techstars, they're already landing enterprise clients who see the potential to transform their project delivery.

Perhaps the most interesting part is their insight about AI adoption: success comes not from elimination, but elevation. It's not about replacing project managers - it's about making them superhuman.

AI transformation isn't just about automation - it's about reimagining how work gets done. And sometimes, the biggest breakthroughs come not from replacing humans, but from freeing them to do what they do best.

Just as we saw with Zendesk revolutionizing customer support, Coworked is showing us what's possible when we think bigger about AI's role in enterprise transformation.

The question isn't whether AI will transform project management - it's how fast organizations will adapt to this new reality.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit agenttalk.substack.com

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S1E2: Agent Talk #2: Kelly Waldher (Zendesk) - How Zendesk Disrupted Itself

Wed, 05 Feb 2025 13:26:54 +0000

Zendesk's journey isn't just another AI adoption story - it's a masterclass in how even enterprise companies can reinvent themselves in the age of AI.

While others rushed to add AI features, Zendesk took the bold step of completely reimagining their business model.

What's fascinating is how they navigated the classic innovator's dilemma: "It's one thing to understand it, it's another thing to be in it and live it," as Kelly puts it.

They didn't just add AI - they fundamentally changed how they deliver and capture value.

Think about their evolution:

* Started investing in AI years before ChatGPT

* Built global AI teams across three continents

* Developed models trained on 18 billion support tickets

* Shifted from seat-based to outcome-based pricing

While most SaaS companies are stuck in the "more seats = more revenue" mindset, Zendesk recognized a fundamental truth:

"You've got these dueling factors... seat contraction and AI arriving on the scene."

Their response? Align their success directly with customer outcomes.

It’s interesting to note that even their private equity owners, typically viewed as cost-cutters, became their biggest champions for transformation. As Kelly says

"We've got investors that are actually pushing us to go faster and to spend a lot of time on this very topic versus playing defensively."

Zendesk’s approach shows that successful AI transformation isn't about following trends - it's about solving real business problems at scale - just like with last week’s Synthesia episode.

Instead of charging for tokens or interactions, they focused on what customers actually care about: resolved support errands and tickets.

Notice how Zendesk is being a pro at handling the transition:

* starting with clear success metrics

* offering predictable pricing through commits

* and finally building trust through incremental value delivery.

This story isn't just about AI - it's about how enterprise software evolves when technology enables fundamentally new business models.

The question isn't whether to transform, but how to do it while keeping your customers' trust.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit agenttalk.substack.com

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S1E1: Agent Talk #1: Victor Riparbelli (Synthesia) - Beyond the Avatar: Building a $1B AI Video Platform

Tue, 28 Jan 2025 14:31:35 +0000

Synthesia's success wasn't about chasing the latest AI trends, but about solving real business problems with a relentless focus on enterprise-grade features and workflows.

In this first episode, Manny talks to Victor Riparbelli, founder and CEO of Synthesia that raised $180M in Series D.

What's fascinating is how Synthesia navigated the classic enterprise software challenge: balancing the "wow factor" with sustainable business value.

While most AI companies get caught in the "sugar high" of viral demos, which is why we’re seeing massive churn problems right now. Synthesia on the other hand focused on the unsexy but crucial elements: ISO certification, enterprise security, and seamless workflows. As Victor puts it, "These boring things unlock bigger and bigger deal sizes."

Think about their evolution:

* Started with avatars that grabbed attention

* Built out comprehensive video editing capabilities

* Added enterprise-grade security and collaboration

* Developed deep integration with business workflows

The results speak volumes: Their customers aren't just trying AI - they're fundamentally transforming their video production.

As Victor says, "For the same budget of producing two traditional videos, we can now make 400 AI-powered ones."

Watch how they're shifting from charging per video creation to focusing on video consumption and engagement. It's a masterclass in aligning pricing with actual business value.

Synthesia's journey shows that building for lasting impact beats chasing the latest AI trends.

This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit agenttalk.substack.com

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