Adi Agashe

Fun fact: Brendan, Adni's technical co-founder, was in the second batch of Y Combinator ever. YC S06! He also has a previous successful exit (sold to UnitedHealthcare), patented an AI algorithm before it was cool, and is a published author... NBD! How has the startup ecosystem evolved over the past 20 years? Brendan shares his wisdom below. 💭 #startups #startupfounder #ycombinator #entrepreneur

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The difference between a $10K contract and a $100K contract is which budget you're competing for. Why startups can land contracts that are 10x larger than incumbents: A large platform focused on serving HVAC companies captures ~1% of their revenue. For example, a typical HVAC company making $1M pays the platform $10k. A startup, also specifically targeting HVAC companies, captures 4-10% of their revenue. The same $1M HVAC company pays the startup $40-100k. The difference is that the big-player platform is just another software expense, while the startup enables companies to operate with fewer employees. Replacing work lets your product capture a payroll budget that's 10x larger. This shift redefines your startup’s entire approach, while traditional platforms focus mainly on selling more seats and subscriptions. You focus on one industry and learn its operations inside out. You become part of their team. Big cos can’t work like this b/c they can’t focus solely & completely on a single vertical problem. Small teams can… and they have to. I’m interested in vertical AI companies that control entire workflows and are built by small teams close to operational work.

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Major drugmakers are partnering with AI x Bio startups at breakneck pace. And these aren’t small deals. They’re multi year, high stakes bets on AI as core infrastructure! - Fresh this week Insilico Medicine Medicine expanded its partnership with Eli Lilly with $100M in upfront and milestone payments (Nov 2025) - Accipiter Biosciences came out of stealth with a collaboration and license deal with Pfizer and with Kite, a Gilead Sciences company (Nov 2025) - Manifold Bio signed a $55M upfront deal with Roche with up to $2B in potential milestones (Nov 2025) - Nabla Bio struck a $1B deal with Takeda (Oct 2025) - Algen Biotechnologies announced a $555 million deal with AstraZeneca (Oct 2025) - Variational AI announced a deal with Merck with the potential value of $349M (Sept 2025) - Superluminal Medicines Inc. Medicines inked a $1.3B deal with Eli Lilly (Aug 2025) More and more keep showing up on my feed each week. At this point, partnering with big pharma looks less like a potential go to market path and more like *the* business model for AI in life sciences. But is it the right one? If AI platforms depend on back loaded payouts, pharma still holds the leverage. If upfronts grow, we may finally see both operating as true discovery peers, sharing risk, data, and value creation on equal footing. Anyone have insight into how these deal terms are actually shaking out? Image Credit: Fierce Biotech

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I had a great time chatting with Patrick O'Shaughnessy on Invest Like The Best. I've known Patrick since college, and this is the first time we've talked markets and investing at this much depth. The fundamentals of company building haven’t changed: people, products, and markets matter. But obviously, private markets have evolved substantially over my career: there are now ~6x more private unicorns than public companies with a $1b+ market cap. And at the end of 2010, just 2 public technology companies were among the top 10 in market cap; today it’s 8 of 10. AI (alongside software eating everything more generally) is clearly driving a lot of this. But it’s instructive to look at everything from the steam engine, to the early days of Facebook and Google user monetization, to real-time success stories like Databricks, Anduril, OpenAI and Waymo, to get a clear picture of where the opportunities lie. It was a pleasure to go deep on all this and more!

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The most salient narrative in tech today is the end of software. Public software companies have been pummeled, down 20%+ in the last 3 months. But I think the bear case rests on weak logic. There are 3 main categories of concern driving this narrative: 1. Vibe coding means businesses build their own software 2. AI automates away jobs, and seat licenses plummet 3. AI-native companies take share from legacy incumbents The first one doesn't hold up. The second is too important to dismiss casually, but lacks evidence thus far. The third strikes me as most threatening, but it, too, rests on a flawed logical foundation.   On rolling your own software, customers who choose to build almost always regret it. An internal IT team can build a CRM, but they can't maintain and improve it like a dedicated software company. Anthropic spending 8-figures on Workday for HR software shows that incumbent providers have more to offer than software skeptics realize.   Sure, there's a risk of AI automating away seats (and therefore seat licenses). But this creates opportunity for software incumbents to deliver AI-infused software that allows customers to do just that. Software vendors can easily shift to usage-based or other value-based pricing models. Relatedly, there's a larger fear that AI will trigger massive unemployment among knowledge workers. This deserves to be taken seriously. However, unemployment is currently quite low by historical standards, and current AI is beyond what most considered AGI a few years ago. I think the worriers here are moving the goalposts. The third concern--AI-native companies decimating software incumbents--is the most formidable. 20-25 years ago, Google hoovered up the attention economy by building the dominant consumer Internet product. Could Anthropic or OpenAI do something like this in b2b? It's not my base case, but it's the scenario that goes the farthest with me.   That said, I think there are 2 misunderstandings embedded here.   The first is that only AI startups can use AI tooling to accelerate product velocity. They aren't. The engineers at Shopify and Workday have the same access to Cursor, Claude Code, or open-source AI models.   The second, bigger misunderstanding is that these markets are zero-sum. By definition, technology expands the existing pie. So I disagree with the idea that AI growth will come at the cost of existing software companies. AI will massively expand software spend.   Which brings me to what's hardest to reconcile. If you believe AI-native startups will create major value, you're implicitly underwriting a world with fragmentation and switching costs among knowledge work tools. You can't hold that public software companies are all in the blast radius of Anthropic AND back the Harveys or Open Evidences of the world.   It doesn't add up, as the bull case of one is implicitly the bull case of the other. And while the market is voting like these are opposites, I expect the weighing machine to disagree.

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A lot of VCs use founder age as a proxy for how well they’ll leverage LLMs and the latest AI. In my experience, the stronger signal is how technical they are. Technical founders understand how LLMs work and can exploit them fully; non-technical founders see magic, not mechanisms.

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