Deedy Das

Deedy Das’s marquee portfolio bet is Goodfire, a mechanistic-interpretability lab he calls “brain surgery for AI models.” Founded by researchers from the early interpretability teams at Anthropic, DeepMind, and OpenAI, its premise is that all current explainability is merely empirical — GPT-4o’s sycophancy wasn’t really caught in evals — and Das teases a still-secret result: “they have done a big part of one pillar of the five things they’re looking to solve.”

His most tradeable macro frame: demographic decline means AI for unhireable jobs is a need-now market, not a bet on future capabilities. Kids want to be YouTubers and coders — “no kid is dreaming about being an accountant” — so insurance brokerages, trucking, and logistics struggle to hire, while the Valley mostly builds “AI for finance and AI for legal” because that’s where Ivy League kids went. “You’re not betting on some future where AI can solve math or coding… You need this now.”

“Almost no iconic company in the history of venture capital has come from anyone’s thesis area” — Das says VCs pitching theses are “semi-lying.” Invoking Khosla’s OpenAI investment, he says it wasn’t a thesis that LLM chatbots would be a thing; nobody chose Facebook over Hi5 and Orkut by framework. His real filter: founders who’d wake up in five years saying “I would be doing nothing else except this,” plus the ability to recruit with near-cult-leader conviction — the biggest bottleneck he sees.

Probe deeply and “at least 90% of companies are actually just changing a prompt on an LLM.” He watched the same founders swap “data flywheel feeds back into fine-tuning” for “it’s RL” a year later, then go silent when asked what RL means; he also says at least 70% of AI pitches reduce to “I just wanna build models. I’m not sure why it’s useful.” For genuinely deep teams, Carta’s 30% premium for AI-enabled or AI-named Series A companies can be fair — “I would 100% pay that premium” — though Molly’s steelman is that the premium for actually good companies may be much higher.

Over-raising can bend the capital-velocity curve negative: illusion of success, the “$50 lunch problem,” and recruiting that can’t show upside. Das says he can’t justify a billion-dollar seed from his own fund; one explanation is an SPV where the lead puts in $1 million of a $100 million round and is “effectively taking none of the risk here… just getting all the marketing value.” Founder secondaries create another bad incentive: “I kinda got the bag.”

His infra map: pretraining is data-bound, so we’re in the RL era — Mercor and Turing filling the hole Scale AI left after its acquisition — with unresolved bottlenecks beyond it. RL is “kind of a shitty paradigm for learning” (reward only arrives at the end, a point Karpathy discusses), and the open problems include sample efficiency, agents/test-time compute, memory, determinism, and context windows; his north star is Anthropic’s “economic Turing test.” His honest hedge: “I do not know the future. I do not know how to answer things like AGI 2027.”

He endorses the layoffs-as-fitness thesis bluntly: “most engineers don’t do shit.” Glean’s Arvind told him he knew Google ICs who hadn’t written code in ten years; AI-attributed tech layoffs are mostly a post-zero-interest-rate efficiency correction, with transitional pain but historically new work emerging. His best inherited wisdom, from Arvind: at any given moment only one question matters — “do customers love this product? Is the answer yes or no?”

🔗 Original source & video: Inside $6.8B+ AUM Fund Behind 80+ Public Companies, 165+ M&A

Glean’s real moat is accumulated enterprise drudgery, not an “AI search” slogan. Deedy Das says enterprise search “shut down the conversation” at Bay Area parties in 2019, but three years of permissions, connectors, ranking, freshness, evaluation, and adoption work positioned Glean for ChatGPT to accelerate its go-to-market. At a $7 billion valuation and several hundred million dollars of revenue, his formulation is blunt: “The moat is just we did the hard work.”

Anthropic’s revenue curve exceeded even its investors’ most optimistic case. Menlo first invested at roughly a $4 billion valuation when Anthropic had no revenue; Deedy describes a climb from $0 to $100 million in one year, $100 million to $1 billion in another, and a public projection from $1 billion to $9 billion this year. His call is that Anthropic is “the fastest growing software company of all time,” while explicitly conceding that nobody predicted this outcome.

Enterprise API share suggests durable model plurality, but Deedy would not underwrite a round north of $170 billion on share alone. Menlo’s surveyed spend data put OpenAI at roughly 50% and Anthropic at 12% in 2023, versus 25% and 32% respectively by mid-2025; these are enterprise LLM API dollars, not token volumes. At today’s scale, “revenue, margin and trajectory” matter more, alongside credible new markets and products.

The discussion weighs model-layer defensibility against thinner app-layer moats. Deedy’s asymmetric test is that Anthropic could enter an application category more readily than an app company could become Anthropic, especially while most AI apps still lack a sufficiently “meaty layer” above the models. Claude Code strengthens the case through usage and data flywheels, but he rejects the idea that it is universally preferred and warns that labs may eventually compete with businesses generating their token demand.

The $100 million Anthology Fund is a model-provider ecosystem fund designed to avoid conventional corporate-venture incentives. Menlo manages it externally because internal funds tend to prioritize “who uses my stuff the most,” while Anthology can back strategically important companies, heavy Claude users, or exceptional early founders without requiring any particular model. It has funded about 40 companies, with checks from $100,000 to $20 million; Deedy says its companies graduate to subsequent rounds at a significantly higher rate.

OpenRouter is Deedy’s exemplar of a PLG infrastructure moat built from annoying details others underestimate. The host says the company takes roughly 5% of routed spend; Deedy emphasizes its mindshare, provider-level performance data, privacy routing, and a product developers can use without sales calls. Its real risks are equally concrete: falling token prices compressing the fee pool, hobbyist churn, and enterprises using it for evaluation before contracting directly with a model provider.

The research portfolio is a set of hedged bets on futures that might become necessary, not confidence that every architecture wins. Goodfire’s mechanistic interpretability is “brain surgery for LLMs” aimed at making consequential model decisions inspectable; the host frames diffusion language models as delivering 80–90% of current quality at one-tenth the cost and latency; and the discussion identifies distributed training, talent access, and a broader vision as possible Prime Intellect upside. Deedy repeatedly stresses that strong technology can still lose to timing and market structure.

Coding agents create a paired security and human-capital risk: people may execute code they cannot inspect while losing the ability to reason through it. A host recounts a purported fake interview repository that allegedly concealed a data-exfiltration link inside a byte array, which Cursor reportedly detected; the same tools can become a “constant slot machine” of “please fix” prompts. The hosts’ proposed counter-model is fast, human-in-the-loop assistance that helps engineers read the right files while they still write and understand the code.

🔗 Original source & video: Anthropic, Glean & OpenRouter: How AI Moats Are Built with Deedy Das of Menlo Ventures