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

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

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 

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

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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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.

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.

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.

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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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.

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.

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.

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