Joining @MenloVentures! Running it back with @mmurph @Jormont1978. Here is why I am so so excited: linkedin.com/pulse/all-menlo…. @venkyganesan @shawnvc @amywumartin @deedydas @mejoff @timt @jpsanday @vcrama @Bad2theSloane @CroomBeatty @derekgxiao

To me, there are four firms that have crushed this wave of AI investing like no one else: 1. Menlo (Anthropic, Lovable, Legora) 2. Spark (Anthropic, Sierra, SSI) 3. Thrive (OpenAI, Cursor, Databricks) 4. Khosla (OpenAI, Factory, Physical Intelligence) So I sat down with @mmurph GP @MenloVentures to discuss the questions that every other interviewer is too shy to ask: - Does open-source cannibalise Anthropic’s business? - Leading Lovable at $13BN? What is the upside case from here? - Openrouter? Is the routing layer even valuable or a commodity? - When a partnership makes $3BN in carry? Does it last? This was such a banger and summarised my thoughts below. 1. Why We Broke All Our Investing Rules to Invest in Anthropic Menlo bypassed its traditional fund parameters because Anthropic delivered elite benchmarks while spending a fraction of the capital. Dario Amodei’s technical leadership and ability to attract exceptional talent made it clear Anthropic could become the dominant alternative to OpenAI. When a generational wave hits, flexibility beats rigidity. 2. Does Every Model Provider Have to Build Their Own Chips Today? Soaring infrastructure bills make custom silicon worth exploring for $100 billion giants optimizing specific workloads. But competing with Nvidia is brutal and requires a specialized team. Most providers should use custom in-house technology selectively while paying for superior external options where they make more sense. 3. Why the Open-Source Rise Will Not Deter Anthropic’s Revenue Growth Open source handles basic workflows well, but it lacks the specialized intelligence to displace elite frontier models. Using Anthropic directly drives stronger retention, platform engagement, and revenue for enterprise applications. The market will mature into a hybrid tapestry where developers route calls across models to optimize cost and performance. 4. Why Anthropic Is Not a Threat to Legora Foundation models may eat generic wrappers, but application layers survive through deeply defensive workflows. Legora solves a complex, multi-constituent problem spanning corporate lawyers, law firms, and finance directors. Generic frontier models cannot easily replicate these intricate, multi-stakeholder workflows. 5. The Hard Part About Series A Today The compressed timeline between Seed and Series A lets startups reach $1 million ARR quickly with minimal proof of product-market fit. Many hit the milestone, but it no longer signals durable demand, even as valuations stretch toward $200 million. VCs need a barbell strategy: get in early at seed or wait for proven breakout winners. 6. Why Ownership Matters Less and Being in the Mega Outcomes Is the Only Thing That Matters Venture is no longer about owning 20% of a $500 million exit. Returns are driven by extreme, compounding outliers. Rigid ownership targets can price you out of generation-defining companies. A tiny stake in a massive winner is better than a large stake in a company that fails to move the needle. (links below)

Artificial Analysis launched a Model Recommender. Set your priorities for intelligence, speed, and cost, and it ranks the best models for your stack. Mercury 2 ranks first. See the full ranking on @ArtificialAnlys: artificialanalysis.ai/models…

First view of our GPU + @dMatrix_AI Corsair configuration for high-performance agentic workloads. The future of inference is heterogeneous! Technical writeup here: gimletlabs.ai/blog/low-laten…

Mercury 2 is live 🚀🚀 The world’s first reasoning diffusion LLM, delivering 5x faster performance than leading speed-optimized LLMs. Watching the team turn years of research into a real product never gets old, and I’m incredibly proud of what we’ve built. We’re just getting started on what diffusion can do for language.

We just raised $20M (Menlo, Sequoia, Felicis, Snowflake, Databricks) and launched Kai—the first superintelligent AI co-worker for customer success. It shouldn't take 100 people to serve 1,000 customers. Kai knows every customer, acts instantly, never drops the ball. Agency.inc fortune.com/2025/11/12/elias…

Elon believes a majority of AI workloads will be diffusion models. I’d pay close attention to Inception Labs, a team of Stanford professors who are doing foundational work here. In the history of computing, no single ML architecture has been dominant for more than a decade.

Just uploaded my brain to Delphi’s Library of Minds. 🧠 I recorded a pod with @withdelphi founder @daraladje. That conversation—along with all my past interviews, articles, blog posts, and talks—is now live inside my Delphi digital mind. You can now chat with all my past content and ask me questions: delphi.ai/jesslee Delphi captures not just your knowledge and stories, but the way you think. In the podcast, I shared several powerful mental frameworks I've collected over the years: 1) The EQ/IQ/PQ/JQ framework, h/t to my partner @shaunmmaguire 😀 Emotional Quotient: One-on-one people skills 👫 Political Quotient: System-level people skills 🤓 Intellectual Quotient: Raw intellectual smarts 🎯Judgment Quotient: Good judgment Some brilliant people (high IQ) make terrible decisions (low JQ). PQ is a force multiplier because leading teams requires navigating group dynamics. Very few people excel at all four dimensions. 2) Moving 3 points on a 10 point scale, via Cheryl Dalrymple, CFO of AdMob, Confluent, and my startup Polyvore: 💪 Hard work typically moves you just 3 points on a 10-point scale. 🎯 It’s far better to push from 7→10 in your strengths than struggle from 2→5 in your weaknesses. ⚡ Since your energy is finite, invest it where you naturally excel. 🤝 Hire exceptional people who thrive where you don’t. 🚫 A common startup mistake: seeking perfectly well-rounded people who score 7+ across every dimension—they’re rare and expensive. 🦔 Instead, hire “spiky” talent—people who are 10s in one dimension, even if they’re 1s elsewhere. 🧩 Build teams where collective strengths cover all critical areas. 3) Startups are turn based games and why velocity matters, h/t @mvernal: 🎮 Startups are like turn-based games. 🂡 You’ll flip a lot of cards and make a lot of moves. 🔁 Most moves won’t be perfect—but what matters is how quickly you turn the next card and learn the next lesson. 🏆 Winning requires a mix of playing the right card and playing quickly. ⚡ It’s easier to be faster than it is to be right-er. So play fast. 🚀 Speed compounds. If you want to go deeper into lessons from my time at Google, the truth of my founder journey at Polyvore, or my hot takes on the future of consumer AI, watch below or have a conversation with my Delphi. 00:00 Intro 1:00 Who is Jess Lee 02:50 The EQ / IQ / PQ / JQ framework 03:44 What early Google taught her 05:35 How ambition is a double-edged sword 07:34 Customer discovery vs visionary intuition 09:31 Polyvore: from user → CEO 12:37 Imposter syndrome & finding authentic leadership 15:20 Picking the wrong market 18:24 Firing fast & setting high performance bars 20:12 Building cult-like community and emotional loyalty 22:13 Velocity vs delight in product 24:32 What she looks for in founders (turn-based velocity) 25:59 The business model wake-up call 27:27 Storytelling as a founding superpower 28:26 Hot take: consumer isn’t dead, it’s being reborn 31:50 AI-generated media, fanfic, and the next YouTube Check out the full library minds at libraryofminds.com/ Or create your own Delphi at delphi.ai!