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- Meet Jerry Dischler, Angular’s latest Partner
Meet Jerry Dischler, Angular’s latest Partner
Esteemed newsletter subscribers,
Hello! I’m Jerry and I’m Angular’s latest Partner. I joined officially (but somewhat secretly) at the beginning of the year, but have been involved with Angular since the firm was an early idea, first as an advisor, then as a Venture Partner, and now an equal Partner.
(As an aside, if you just joined the newsletter, apologies for the repeated information! I promise that you’ll get something more interesting in my next contribution in a few weeks. Also feel free to read through the archive here.)
For a bit of background on me beyond my LinkedIn profile: Early in my career I worked as an engineer, mostly at startups on early stage teams. After grad school, I worked at Google for nearly two decades. During this time, I led Google’s advertising products. I also was the President of Cloud Applications, which included leading the Workspace productivity suite and building AI agents for customer service, sales, and a few other domains. Earlier on, I led Google’s consumer payments and eCommerce teams.
Why is this the right next move for me? A few reasons.
Team: I’ve known Gil (who founded the firm) since grad school, have worked with Angular since it was an idea in 2014, and with David since he joined in 2021. They’re both exceptional people and investors. I have also co-invested alongside Angular in 3 companies (Levity, FalkorDB, and specific.ai), have advised many more, and became a venture partner in 2024. Since stepping away from Google, I’ve spent more and more time with the firm. At first, these guys were wondering why I was showing up at 6am to every weekly portfolio call. I was just drawn to working with Gil and David and to doing early stage venture, which brings us to the next point…
Stage: Angular is an inception firm, working with founders at a very early stage. I get huge satisfaction helping entrepreneurs start new efforts and lay the foundation for what will be future large companies. I’ve had the chance to reflect over the years and this is where I get my energy, working closely with small teams on hard technical and business problems to deliver significant impact. Whether early in my career working as an engineer in early stage startups, later in my career advising smaller tech companies, or as an angel and now venture investor, this is work I truly love.
We’re at an especially interesting and important moment given the current wave of innovation in AI. The largest tech companies are primarily focused on the most horizontal problems and platform adjacencies. They’re acting rationally in order to maximize their impact. As a result, new teams have considerable room to innovate to become the next generation of big tech companies. My role is to identify and support these companies.
Market Focus: Half of my angel investments have been European or Israeli companies, and this is because I’m a big believer that great ideas come from anywhere. I’ve had the privilege of helping many teams over the years. Though some of the best ones have been in the San Francisco Bay Area, others have not, and I’ve helped those outside SF lean into the advantages and minimize the disadvantages. Further, AI innovation and adoption will aggressively globalize over the next 5-10 years and I want to help facilitate that.
In terms of how I’m spending my time these days, I’m working with several very early stage teams in the US, Europe, and Israel, am talking to companies that are further along about potential investment, and am working with companies in the Angular portfolio to help them grow. The ideas are pretty diverse; though I mostly am working with companies in AI infrastructure and applications, I’m also looking at robotics, defense/dual-use, fintech, and cybersecurity.
More specifically, I’ve taken a board seat at Reco, an Angular portfolio company that is innovating in AI security. And I’ve recently made my first of many Angular portfolio investments, an as-yet-unannounced European team that relocated to California recently, which you’ll hear about shortly.
This is the first of many newsletter posts you’ll see from me. In the meantime, if you’d like to chat, you can reach me at [email protected].
Best,
-Jerry.
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WORTH READING
HARD TECH
10x More Expensive - Dwarkesh Patel posted a quick fire blog post / short essay on ‘Why compute might get 10x+ more expensive in coming years’. Compute, rather than capability, is becoming the binding constraint on AI. Patel argues that Anthropic's revenue is 10x-ing yearly while lab compute only 3x-es, and that the gap closes mainly through compute getting far more expensive rather than fatter margins or more inference spend. It’s an essay he wrote in two hours (which he calls time boxed writing) and he is not without bias given his close relationships with key figures in larger labs like Anthropic and OpenAI, so the essay sparked debate on X and elsewhere. The evidence he points to is that spot prices have risen over 40% since February and Google reportedly paying SpaceX ~2x spot rate for GPU capacity. As models get smarter they monetize the same chip much better (a human-level AI engineer on one H100 implies ~15x today's rental rates), while compute supply stays capped at ~3x/year by Moore's Law, fab buildout, and wafer allocation limits. Unlike the Simon-Ehrlich commodities bet, substitution won't bail this out. The essay argues that the upshot is that frontier labs' lead compounds since expensive compute favors whoever can pay for it and monetize it best, efficient models command steep premiums over weaker ones, and low-value AI use cases (slop generation) get priced out as compute's opportunity cost rises.
HARD MARKETS
Directionally Correct - Leopold Aschenbrenner's AI-focused fund, Situational Awareness, was forced to sell most of its public stock holdings to Citadel after banks called in loans during a recent selloff. In the midst of excessive schadenfreude, John Foley’s Financial Times piece, among many, make a case for why Aschenbrenner was not as situationally aware as he might have thought, and certainly less aware of the historical complexities of the public markets. “Leopold Aschenbrenner was hailed as a great prognosticator, but he did a poor job of interpreting history. The tech wunderkind’s misfortune is a reminder — useful for the AI era — that an investor can be right and still come undone.” The lesson: he was right on AI, but used leverage on volatile positions, and when AI stocks dropped sharply (holdings down ~21% on average last month, Sandisk down 45%), the debt turned a paper wobble into a forced liquidation. He's kept his unleveraged holdings, including a stake in Anthropic. The piece uses this as a cautionary parallel for AI data center builders taking on massive debt (~$9tn projected by 2030). Man Group's "temporal mismatch" risk, where financing timelines don't match the pace of chip depreciation or model efficiency gains (chips wearing out before loans are repaid, or cheaper/more efficient models like Kimi K3 making existing infrastructure obsolete). The broader point: being right about AI's trajectory doesn't protect against being wrong about timing, winners, or leverage and banks, having pulled back from Aschenbrenner once markets turned, are still happy to finance others' AI leverage for a fee.
HOW TO STARTUP
PLG turns 10 - Product-led growth turns 10 this year, and Kyle Poyer (who helped coin the term in 2016) argues in a Linkedin post that it looks nothing like it did then. The main shifts he sees are that B2C/B2B lines have blurred, free users are now a real cost center rather than pure CAC, seat-based pricing is giving way to hybrid/AI-credit/outcome-based models, and "users" now include AI agents alongside humans (some of whom may never touch the actual product UI). Growth has also fragmented: SEO is largely tapped out, docs have become a growth channel in their own right, prompt bars have replaced guided tours, and there's no single onboarding funnel anymore since AI agents often handle activation. His bigger-picture claims are that shipping features is easy now, but getting anyone to care is the hard part. Multi-product adoption is the new must-win and PLG alone no longer cuts it as a strategy. What hasn't changed, per Poyer: B2B buyers are still just consumers at work who want to try before they buy and hate friction. He invites comments and dissent in his (always lively) comments section.
HOW TO VENTURE
Go Big or Go to a Big Lab - The Wall Street Journal charts a trend where start-ups are losing not only employees, but founders to big labs. Pete Soderling of Zero Prime Ventures shares an experience with the WSJ where after signing a term sheet to back a London based AI start-up, one of the founders quit to join “a big AI lab”. Even well funded start-ups like Thinking Machines aren’t immune, as they lost a co-founder last week who returned to OpenAI (ostensibly for her health). Soderling goes further on Linkedin “I get it, I spent my career as a founder before I started writing checks and a much larger, very hot, tech co dangling life-changing money is hard to say no to, but this poaching of founding teams is happening at every stage. PitchBook counted 322 US AI startup acquisitions with undisclosed values last year, up from 181 the year before (undisclosed usually means "we wanted the team"). 2026 is running hotter.”
PORTFOLIO JOBS
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