Jamie Downey

JPM's 2026 technology strategy is all in on AI, and it is big. A $9B commitment to agentic AI and infrastructure, to be exact. Roughly 4.5x their historical annual tech spend. That was not experimentation. That was a balance-sheet level decision. CFO Jeremy Barnum did wisely caveat that ROI is hard to prove for AI in banking, especially the amount of effort taken to navigate regulations and tech deployment. But they still made the investment. Case in point, inaction hurts financial institutions' bottomline more than AI adoption chaos. And the transition doesn't even have to be rough- not when banks pick the right solution.

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Truly honored to make the Lean AI leaderboard! 3 years ago, almost every investor doubted our decision to start by focusing on job seekers. But we believed they needed us more than employers did. It took us a year to hit $5M ARR, and to this day, 95% of our users are still on our free product , because we want this to be accessible to as many people as possible. This year, we’re taking it a step further: directly connecting job seekers with the companies that are the best fit for them — making both job searching and hiring 10X better. Thank you to every Jobright user and supporter. As AI continues to reshape the job market, we want Jobright to be everyone’s career success companion.​​​​​​​​​​​​​​​! ❤️

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So much of building a company can feel like following one set of best practices after another. Since YC, we’ve gotten advice on everything: GTM copy, conferences to attend, product to build, etc. So much of the advice is helpful, but it’s also important to hold onto the personal motivators even when they defy intuition. The onslaught of best practices can never end, and under pressure the core motivations that led you to founding are always in threat. And once those are gone burnout is close behind. In conversations with founder peers about all they’re working on, there often comes a lovely moment where the tone shifts, and you realize the founder is discussing what truly excites them. It can be so many different things: their cofounder, the market they get to work on, their customers, but the founder leverages that aspect to fuel all the hardship they’ll endure. For me with Cenote, it’s this: Buying online, especially for expensive or high consideration products, is often a strange, isolating experience. People are making decisions that will transform their bodies or livelihoods and have no one to ask questions or talk through doubts with. Cenote’s mission is to change this and make sure people don’t have to make these decisions alone.

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on ETA (across AI Roll-Ups funded by VCs, PE-style buy and build, HoldCos, traditional search funds, etc.) Context of my learnings: I've looked both at individual search fund deals from an investor perspective and larger platform ideas as founder. Over the past couple months, I've screened ca. 200 investment memorandums, did >10 site visits / superficial DDs, 2 deeper DDs, took 0 final leaps. I can't speak from the perspective of someone who's done it, but have talked to enough people to see patterns. Good news: The succession opportunity is real, the lag of technology adoption in SMBs is INSANE and there are some (!) high-quality SMBs with customer relationships so sticky, that it makes sense to continue something existing vs. building from scratch. I personally also believe many great operator talents exist, who have what it takes to lead an SMB, but might not be "0 to 1" founder types - and any form of entrepreneurship is net positive!! Bad news: Forget about risk-free returns and think through operational nightmare scenarios before you're stuck with them. Highly recommend the content of Alex Prokofjev / RollUpEurope, Christopher von Wedemeyer / Legacy Partners and Sahil Patwa / AI Roll-up Nexus to learn about common pitfalls. 1) Historical numbers suggest ca. 50% of search funds fail. Now adding ambitious add-on strategies, a wave of consulting / banking talents with no operator experience, and rising multiples.. I think this might trend up to 60-70% rather than down? Better than 90%+ failure risk of a startup, but no magic trick. 2) Thinking about the exit assumption, which is multiple arbitrage for most strategies: Which business really gets better by pasting together a couple of smaller ones, with different cultures, compensation practices, customer contracts...? Some do, because scale benefits can be powerful, but many don't. When speaking with people, who operated or acquired small cap platforms from leaders like Auctus / Ufenau / etc, it becomes clear that operational nightmares are the norm rather than the exception and the multiple arbitrage narrative could fade for many verticals. 3) Some VCs betting on deeper, longer-term tech value creation (...and maybe an IPO...in 20 years?) underwrite 15% to 40% EBITDA margin uplifts = relying less on multiple arbitrage, and maybe that's possible! But some of the "red hot" verticals are turning so competitive, with players funded near software multiples (have heard there are several already well-funded property managers raising again now), that sellers will realize they can push entry multiples up. VC-backed founders will feel pressure to put capital to work and will make mistakes. 4) More decentral HoldCos, with very high investment selection bars and less hands-on value creation, might bring the best risk-adjusted returns. However, it seems hard to attract brilliant young "builder" talent for those strategies and I'd personally always feel an itch to be more operationally involved.

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Most CPG startups do not fail loudly. They plateau quietly. As an investor, I have seen many talented founders work hard for years and still struggle to reach their first $10M in revenue. The issue is rarely ambition or effort. Progress slows because a few recurring patterns emerge early and compound over time, long before the business looks broken. I wrote a short piece outlining five patterns that consistently stall CPG startups before $10M, based on my observations across founders, retailers, and investors. If you are building, investing in, or advising a CPG company, this may resonate with you. Read the full newsletter here: [link in comment] #CPG #ConsumerStartups #FounderLessons #StartupGrowth #VentureCapital

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$100 billion of "Neo Labs" are quietly breaking the VC model... Everyone on LinkedIn keeps talking about the traditional math of venture capital: Power laws, fund sizes, IPO paths, and portfolio construction. That math works when capital is deployed into companies. Entities built with clear products, defined GTM paths, and a realistic shot at commercial liquidity. But we’ve entered the era of the Neo Lab. These are VC-funded AI institutes that operate more like academic research centers than startups. They prioritize "Scientific Superintelligence" over quarterly earnings. The scale of this shift is staggering: Safe Superintelligence Inc.(SSI): Raised $3B at a $32B valuation to chase a single technical milestone. Thinking Machines Lab: Mira Murati’s new lab, which hit a $12B valuation (and is reportedly eyeing $60B) on the strength of research pedigree alone. Isomorphic Labs: Alphabet’s spin-off recently took its first $600M in external funding to "solve" biology. Lila Sciences: Building "AI Science Factories" with $550M to automate the scientific method. Black Forest Labs: A $3.25B powerhouse for visual intelligence that prioritizes open research over enterprise SaaS sales. Where the model breaks: Venture capital expects asymmetry. Historically, that asymmetry comes from distribution, pricing power, and execution. It does not come from papers, demos, or benchmarks alone. One of these has to hit a multi-trillion dollar valuation to make VC math work. Are we witnessing the most expensive science experiments in history, a bubble, or a fundamental evolution of how humanity innovates? 

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Capital One paid $5.15B for Brex. That's 58% below the $12.3B valuation from 2022. Everyone's calling it a down round. But look at what Capital One actually bought. Brex hit $500M in revenue with 25,000 customers including DoorDash, Anthropic, and Intel. They built corporate cards, expense management, and banking into one AI-native platform. Capital One didn't just buy software. They bought $13B in deposits, an EU banking license, and enterprise clients across 30 countries. Plus they skip 3+ years of building AI-powered expense automation from scratch. Early Brex investors like Ribbit Capital who led the $7M Series A are looking at 700x returns. Late-stage investors who came in at $7.4B+ get liquidity. The public market reality: Chime IPO'd in June, down 40% from its opening price. Klarna fell below its IPO price within weeks of going public in September. 2021's funding created dozens of fintechs chasing the same markets. Corporate cards, expense management, embedded banking. These are crowded categories where there's room for 1-2 winners at scale, not 5-7 venture-backed challengers. For Brex, selling to Capital One meant certainty over hoping public markets would reward growth. #Fintech #VentureCapital #ProductStrategy #Startups

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