Fractional CFO for VC/PE firms graphadvisors.com prev. @expa, @foursquare, @usv

Joined December 2006
1,058 Photos and videos
Good way to think about risk and another thing to add to our conversation for those who want to work with a forward deployed engineer graphadvisors.com/outcomes-e…
If a Treasurer of a Fortune 1000 company kept all of their cash in one bank they’d be fired for incompetence. Similarly, if the leadership of a Fortune 1000 company bets the farm on only one frontier lab and their models you’re taking a lot of risk. This risk compounds as the labs’ intentions and public actions are bewildering and show them to be increasingly unpredictable. This is why every major enterprise needs a model agnostic “control plane”. Get the work done, increase the productivity, make more money, save costs, increase efficiency but do it with governance, auditability and control. Capabilities across all models are converging. Open source prices are, in some cases, 1/100th of the frontier labs. But governance, control, compliance and collaboration capabilities don’t exist unless you first focus on the right “control plane”. If you do not find such a “control plane”, you’re increasingly taking risk as the frontier labs become increasingly unpredictable. This is why 8090’s Software Factory is used this way in every major part of the economy including governments.
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So much of business is just bundling and unbundling
Introducing the Fusion API, the smartest compound model in the market. Fusion achieves Fable-level intelligence at half the price. How it works 👇
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Auditing this Standford class and told a friend and they might join: mse435.stanford.edu/material… If anyone else wants to do the same join us!

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Cloudflare might be one of the best platforms for agentic coding right now to build on
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The US government, citing national security authorities, has issued an export control directive to suspend all access to Fable 5 and Mythos 5 by any foreign national, whether inside or outside the United States, including foreign national Anthropic employees. The net effect of this order is that we must abruptly disable Fable 5 and Mythos 5 for all our customers to ensure compliance. Access to all other Claude models is not affected. We apologize for this disruption to our customers. We believe this is a misunderstanding and are working to restore access as soon as possible. Read our full statement: anthropic.com/news/fable-myt…
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You might be right about the last part But they are at least going to try to get there and we have started helping them. Once you realize you need full observability to ask the right questions behavior changes
Increasingly, I believe companies may need to be rebuilt from the ground up, where you have a single timeline of all observability product metrics file changes laid out in a retrievable system, like Datadog Posthog Google Drive Slack (really unified filesystem of Claude Code chats Codex chats). This might be the new data foundation for any and all companies to maximize AI. Needs to be rebuilt because keeping track of diffs on existing system basically impossible to produce longitudinal information on decisions and rollbacks, something coding agent storage companies are actively trying to figure out, but this should extend to businesses as a whole. Highly skeptical existing businesses will adopt this though because it means overhauling everything about their instrumentation and business data, but I think businesses built on this foundation probably can execute 100x better and faster
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I brought together and organized a ton of medical info related to a scan for my daughters knee into AI and I am in awe. Brought in Scans/Xrays/MRI, transcripts of visits, findings and results. I know that I am not a Dr. nor is AI but its a peek at the future of medicine
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The “for you” feed is too good and there’s simply too much amazing AI stuff to look at, learn, and read everyday. Pls fix.
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“We only hire 52x engineers”
Replying to @AnthropicAI
Each time we release a model, we run the same test: give it code that trains a small AI model, ask the new model to speed it up. It takes a skilled human 4-8 hours to reach 4x faster. In May 2024, Claude Opus 4 averaged a ~3x speedup. This April, Mythos Preview achieved ~52x.
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I was hoping it would be a little longer. This is one of those gradually then all of the sudden moments. At least we can tell our kids we surfed the web with more humans than bots
Welp, that happened faster than I predicted. Thought it would be end of 2027, then early 2027, but agentic traffic growing so fast that bots have now passed human traffic online for the first time in the Internet's history. radar.cloudflare.com/traffic…
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#3 “the zombie VC era” needs a solution for what the GPs do with the fund and I wrote our solution from 2023 graphadvisors.substack.com/p…

Monthly VC/LP debrief. What I actually saw in May 2026: 1/ SF is in full gold rush mode again, but history says the current winners won't stay on top forever. Every dominant technology eventually gets surpassed – newspapers, telecom, cable, Google in ads, IBM in computers. In AI the same pattern is already playing out: compute will hit walls, chips get dramatically more efficient, new energy sources emerge, and entirely new model architectures appear. The people feeling left behind today may just be early in a much longer cycle. (h/t @TurnerNovak) 2/ The largest $10B funds went from 140–150 collective early-stage deals per year in the SaaS era to 370–400 in the AI era. But the concentration is at the top of the market – top-decile rounds, known founders, proven operators. @kevinhartz calls it "option value": a small check today for the right to lead Series A tomorrow. The average seed round remains territory for EMs. 3/ We might be entering a Zombie VC era. ~85% of 2017–2018 vintage funds still haven't returned 1x DPI after 7–8 years. Median DPI sits at $0.34 on the dollar, while median IRR for the same cohort looks respectable at 11.6%. Paper returns hide the reality. The liquidity window opening over the next two years will be the moment of truth for most of these funds. 4/ @SpaceX IPO might be the single largest DPI event in VC history dropping into the lowest-distribution moment in venture capital history. @foundersfund alone, with an early $20M check in 2008, could return $60B (~3000x). When that capital hits LP accounts, it needs to be redeployed and that will circulate a new wave of fundraising for the same funds and fresh allocations from LPs who finally have liquidity to work with. 5/ The @cerebras IPO was the first real data point on crossover returns after two years of everyone writing off the model – both early-stage VCs and late-stage crossover funds made money on the same company, and LP conversations shifted from "do we have any exposure to the winners" to "how do we get into the next one." The same strategy that was declared dead in 2022-2023 got fully rehabilitated by a single exit. (h/t @MeghanKReynolds) 6/ Monte Carlo across 1,391 VC funds: concentrated portfolios (15 companies) and diversified ones (100 companies) produce the same average fund return – 2.44x. But compounded across multiple vintages, diversified wins: 2.25x vs. 1.78x. Concentrated funds carry more variance per fund, and variance drag compounds against you over time. The extreme outcomes (15x ) are almost exclusive to concentrated funds but the probability is tiny either way. (h/t Steve Kim) 7/ EM activity is showing the first real pulse in years. @cartainc logged 78 new US venture funds in the $10M–$100M range in Q1 2026 – a 34% jump from Q1 2025. Still well below the 2022 peak of 147, but the post-winter bottom might finally be in. The managers raising right now are doing it without a favorable macro, without easy LP recycling, and into a market where mega-funds are more active at seed than ever. (h/t @PeterJ_Walker) 8/ 76% of all EM-focused FoFs are American. The entire addressable market for a Fund I or Fund II isn't 132 FoFs – it's roughly 33. The other 100 exist, but Classic and Government-Led FoFs structurally can't anchor an early-stage vehicle: the check size doesn't justify the overhead, and a pension board can't be sold on a first-time manager without a track record. Geography and fund type filter out 75% of the market before the first meeting. (via @murphcapital) 9/ The 10-year fund is structurally mismatched with the assets mega-funds are holding. @SpaceX has been private for 18 years. @stripe for 15. For managers at that scale, @sequoia's move makes sense – open-ended, permanent capital, indefinite horizon. For small funds the logic runs the opposite way: the 10-year horizon enforced as a hard constraint, secondaries at Series C/D as the default exit, actual distributions on schedule. (h/t @credistick) 10/ There are only 3 positions that matter in a startup's cap table story: first investor, most helpful investor, biggest investor. Biggest is reserved for ~10 megafunds. First requires conviction most managers don't have – and LP preferences for concentrated portfolios often push against it structurally. So 90% of firms end up competing for "most helpful," which is why every pitch deck has a platform slide and every GP talks about their right to win oversubscribed rounds. (h/t @arian_ghashghai) Every month I track new fund launches, LP events, market reports, and what's actually moving in VC/LP. All of it in the @murphcapital newsletter: murphcapital.substack.com/p/…
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I need Claude CoWork<->Claude chats connector to take things from a mobile convo back to CoWork
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With so many instances of apps for services businesses like ours @ClaudeDevs should allow MULTIPLE connectors to the same service (Notion is a great example) The alternative is disconnecting, reconnecting each time
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I’m playing around with ChatGPT finance (paid tier allows Plaid connection to bring in and parse all dat) and realizing that OpenAi being in the flow of all this data exhaust it crazy valuable (I am paying for the product and am the product value 😅)
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I’m reading The Coming Wave and this is both amazing and terrifying! amzn.to/3PI3JVJ
I just sequenced a human genome to 30× coverage entirely at home. As far as I know, this is the first time this has been done. I didn’t step foot in a lab once. Every step - from saliva collection, to running the sequencer - took place in a single room with a dining table kitchenette. Six weeks ago, I had never done wet lab biology before. I used an Oxford Nanopore P2 Solo - the only commercially available sequencing device portable enough to do 30x human genome sequencing at home. Biggest takeaway - I could build something that combined software, hardware, and molecular biology far faster than I thought was possible. I can name >100 specific instances where AI helped me solve a technical problem that would previously have blocked me because I lacked access to a domain expert. For example: how do I save my sequencing run when my DNA extraction yield is 4x lower than I need it to be, and I have this limited set of reagents to hand? To make this work, I had to navigate multiple disciplines: - writing software to monitor sequencing runs and orchestrate remote GPU infra for basecalling - learning executing 5 hour long molecular biology protocols - building a hardware device to quantify DNA concentration Apologies for the hyperbole, but I feel super lucky to be living in 2026. A few weeks ago I decided to sequence a human genome to 30x at home. Then I actually did it. And I did it really quickly.
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This makes me want the markets to only be open for only 1 second every day (an idea I first heard about from @matt_levine )
Just a friendly reminder - these are the quants you’re competing against.
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Accountability Chrome Extension V2.2 is ready - a simple new tab "todo list" system that lets you see what you should focus on every time you open a new tab Fun nights and weekends project that just got accepted by the Google Chrome store
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You can grab it here -> chromewebstore.google.com/de… My latest addition is blocking YouTube Shorts and Side bar recommendations
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Writing about my updates and projects has been fun too: buildingthemachine.com/proje…

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