Erfan Rostami

@bneyshabur @MirendilAI @a16z @kleinerperkins insane team. rooting for you

Today, I’m excited to formally announce @MirendilAI with my amazing co-founders Harsh Mehta, Shayan Salehian, and Tara Rezaei! We’re fortunate to work with @a16z and @kleinerperkins, who led our seed round of $200M, followed by a major investment from NVIDIA, among others. Mirendil exists to accelerate science and technology, and through them, to help solve humanity's most pressing problems. Self-accelerating AI R&D is the most direct path to delivering on AI's broader promise, which is why we believe the most important application of AI is AI itself. Get this loop right, and it compounds. It fundamentally changes the rate of progress itself across all domains. We believe this capability should be democratized. It should be used to power all scientific efforts trying to innovate at the frontier. There are far more important problems—and broader ones—than any single lab can take on, so more groups should be able to pursue them. This pulls concentration of power away from a few labs: businesses and science labs can own their AI and infrastructure, keep their margins, and control their own destiny instead of ceding it all to a single AI lab. We’re a small team with a singular focus. Our founding team consists of 20 researchers and engineers from frontier institutions including Anthropic, xAI, Google DeepMind, and OpenAI, united by a passion for science and a drive to build the technologies that move it faster. If you want to build the system that builds systems, join us! @HarshMeh1a, @shayan_, @tararezaeikh

@amiruci well deserved

We closed our Series F today at a $13B valuation. Our inference business grew 20x in the last year. I want to explain why: The growth comes from a shift I think is permanent: companies want to own their intelligence layer. Instead of relying exclusively on closed models, teams are post-training open models for their specific use cases. Customers like Abridge, Cursor, Decagon, Harvey, HubSpot, Lovable, Notion, OpenEvidence, and Parallel are building this way. But post-training is still more of an art than a science. That’s why we’ve been working hands-on with customers to build specialized models that match or exceed closed models on the tasks they care about. We provide not just the weights, but also the training recipes and tooling so that they're in charge of the continual learning process. I think more companies, both AI-natives and enterprises, will own their intelligence layer. And I’m excited to help build that future.

@Rich_Pluta @reindsummit Haha it was great having you

VOLTAI chartered a yacht for their happy hour at Reindustrialize. This is how you do it.

@PodcastAlphaX Why did they skip 7nm?

Marvell spent $36 billion over 10 years to own every connectivity distance in a data center. $22.5B in acquisitions. $18B in organic R&D. Cavium for compute and networking. Inphi for $10B for data center interconnect. Celestial AI for photonic fabric. Xcon for scale-up switching. And one technical bet that could have destroyed the company: skipping the 7nm process node entirely, jumping from 14nm straight to 5nm. Murphy on stage: "Nobody does this. Nobody takes that kind of risk. But we did and it worked - flawlessly." That node-skip is what got Marvell into the hyperscaler qualification cycles driving 75%+ of revenue today. The full picture on what $36B built and whether it is defensible: podcastalpha.substack.com/p/episode-summ… Source: COMPUTEX 2026 Keynote - youtube.com/watch?v=DSnugw…

@taste_ai_ i have my favorite pick in the team

tasteful team. join us

@thaiscbranco_ @CRV @AmplifyPartners great taste Thais

We’re excited to introduce Taste Labs. Our mission is to end AI slop. We’re building the data and infrastructure layer to give AI models and agents taste. And today we’re coming out of stealth, announcing our $18.5M seed funding, co-led by @CRV and @AmplifyPartners AI has nailed objective domains and made it easy to generate anything. But it still feels off. Now, the challenge is judgement. What fits, what feels like you, what’s GREAT. This requires turning a fuzzy, subjective domain into something we can measure and codify. We’re starting with design. There are two sides to cracking this, the foundation model layer and the agent layer: - We’ve already been working with the top frontier labs to evaluate and improve their models, crafting the right post-training data and RL environments. - We’ve also been working with app-layer companies to build the context and verification tools for their agents to produce better, more on-brand, more creative outputs. We want a future where AI feels right. If you’re passionate about this mission, join us!

@krandiash incredible team

We released Sonic-3.5 and Ink-2, the #1 streaming models for text to speech and speech to text you can use in your voice agents today. New architectures enable new frontiers for speed and quality. We're now the only provider to have #1 models for both speaking and listening.

@asrlhhh life changing advice

Farewell message from my Fable 5 🫡

@SKundojjala or more simply companies/competitors rebuild EDA stack with more powerful models

EDA is currently 11-12% of R&D budgets at many fabless companies. Cadence claims more headroom for this to go up as companies spend up to a third of R&D on AI and significant of that can be tapped by Cadence. The math comes from Jensen's statement of his willingness to spend 50% of human engineer cost on tokens. Engineer comp is roughly two-thirds of R&D, so AI spend could reach one-third of R&D and within that Cadence sees an opportunity for it. But a bear case is also seems to be there as customers up their AI budget there is a risk of value migration from EDA to models.

@dylan522p never let anyone predict your next move 😂

thats pretty insulting to al qaeda

@byluckylooks was solid

we got our client +250k views in less than 24 hours (100% organic) on their launch the hardest part was making the character interaction feels real. we did it by: - natural sounding script - rehearsing on-camera chemistry - precise character art direction - and a ton of compositing lmk if you want a more in-depth breakdown

trying to get a box of GPU today and cant! so makes sense

@SemiAnalysis_ write it in gold and put it on the wall

Pretraining fundamentally does not make sense anymore for anyone other than frontier labs. Although there are a lot of people at enterprises & startups who have "Pretrainitis" to show “impact” and get promotions, fundamentally, it doesn’t make sense. There is probably higher ROI in partnering with a frontier lab to do prompt engineering, although it isn’t as “sexy” as pretraining.

bullish on the team

@AlokVasudev might have good news for u

Formal verification meta upcoming. AI both pulling it fwd (secure software) and enabling it (FV harnesses)

Jensen Huang is a titan and a teacher. He recently sat down with me to explain his vision of the future of technology and humanity. He’s calm, clear, and very funny. We touched on many topics, ranging from the $20T or more AI economy in five layers to changes in labor for the AI age. Here are some of my main takeaways: 1) The world is moving from retrieval to generation 2) Generation offers intelligence customized to the individual 3) Nvidia is making the "generators" of intelligence 4) We've seen this kind of revolution at least three times before with energy (Generators), Telecommunications (vacuum tubes / transistors?), and now intelligence (GPUs) 5) There is a five-layer cake of participation in this many-many trillion dollar revolution: Energy, Chips, Infra, Models, and Applications. 6) There are many ways to participate in this revolution, and everyone has a role 7) We'll be pushed to dream up new problems to solve with this unprecedented intelligence 8) In this new future, it's not just having the answer, it's having the right questions 8) The right questions will drive us toward our individual and collective human purpose 9) We move from the carpenters to the architects I believe this is the realistic future. Thanks to Jensen and the entire @nvidia team for the conversation and for letting us share! 00:00 Introduction 00:42 From Chatbots to Generative AI 03:35 Agentic AI That Does Work 05:26 Downstream Industry Impact 06:25 Computing Shifts From Retrieval to Generation 11:26 A Planet Cocooned by Intelligence 14:27 Inside the NVIDIA AI Factory 20:48 AI Five Layer Cake 21:58 Beyond Chatbots to Biology 23:54 Tokens and World Models 24:53 Trillions in Applications 27:13 Ditch the AI Doom 31:32 Jobs Tasks vs Purpose 38:40 Closing the Tech Divide

@markiewagner @PoeticHQ congrats

Introducing @PoeticHQ: a new AI system that executes complex multi-hour tasks with 99%+ accuracy and 10x fewer tokens than agents. We raised $50M at $500M from Kleiner Perkins, Founders Fund, First Harmonic, and Genius Ventures to build AI that does complex work inside Fortune 500 companies without hallucination. While code is too brittle, agents are too unpredictable. The work that runs the global economy - anti-money laundering, fraud investigations, underwriting - needs extreme accuracy. So we built a new kind of software that pairs the flexibility of AI with the predictability of code. When the world stays the same, Poetic runs fixed code: fast, cheap, identical every time. When the world changes, Poetic uses AI to regenerate its approach and find its way back to the objective. In one year, we went from zero to an eight-figure run rate as a team of four. Since then, we’ve scaled the team and executed the highest-stakes processes at AIG, SoFi, and Chime. At SoFi, a large US bank, Poetic reached 99%+ quality on fraud investigations in five weeks.

@gravicle @CarolineHydeTV @EdLudlow nice

We are launching a new Open Physical AI Lab to solve the generalization in robotics. Discussion with @CarolineHydeTV and @EdLudlow on Bloomberg Tech this morning -

@QuantumArjun @rronak_ @MichaelElabd congrats bro

I first met @rronak_ and @MichaelElabd when we were all freshman at Stanford. Today, 8 years later, we’re announcing that we’ve started Trajectory, a research lab and product company building the platform for continual learning. I believe that Continual Learning demands a fundamentally new interface for how we build products. That's a research challenge and a product challenge in equal measure, so we've assembled a team to meet both: researchers from DeepMind, OpenAI, Apple, Meta Superintelligence, Amazon AGI, and Scale AI, and product talent from Stripe and Figma. We’re also partnering with the best AI native companies @Clay, @Harvey, @Decagon, @Mercor, and @RogoAI to power their agentic experiences, and push the boundaries of what agents look like in the real world. Please reach out if you’re excited to build with us!

@rronak_ congrats man

I’ve left Google DeepMind. The last two years have been an incredible whirlwind. A couple years ago, I joined a small startup called Codeium. There, I got to ship Windsurf, train SWE-1 (a frontier agentic coding model), go to DeepMind in the $2.4B acquisition. Now, I decided to leave the acquisition money and DeepMind. I’m grateful to the mentors, teammates, and friends I worked with along the way. At Windsurf, thanks to @_mohansolo and Douglas Chen, I got to see what a fast moving startup that ships relentlessly and builds for the future looks like. I learned from @thenickmoy how excellent research leadership can drive outsized innovation. At DeepMind, I got to push the frontier of agentic coding, be part of the amazing team that shipped Antigravity and contributed to Gemini 3. DeepMind is a rare place: deeply curious people, exceptional research taste, and access to enormous compute and Google-scale infrastructure. A few things that I learned: 1. Finding the right hill to climb. Now more than ever, there are a multitude of directions to push the frontier in AI research. It’s easy to optimize for the wrong benchmark or capability. You should step back regularly to question if you are climbing the right hill, and adjust course often. 2. The secret to being a fast-moving team. Moving quickly is not just about working hard and long hours. It requires making concrete bets about where the world will be in 6 months, aligning around them, and cutting everything else. This was our journey from the Codeium Extension → Windsurf IDE → SWE-1 → Antigravity → Antigravity CLI 3. Silicon Valley is small. Since the split of Windsurf to DeepMind and Cognition, many of my colleagues have gone to other exciting places - Thinking Machines, OpenAI, xAI, Cursor, fast-moving startups, or started their own companies. I’m grateful to have worked with so many talented, hungry people whose stories are not yet finished. So what’s next? We are living in one of the most exciting and powerful times in human history. Just like we transformed software engineering, soon every industry, every unit of work will be radically transformed, democratized, accelerated. With this comes new challenges, and new doors of frontier research to be opened. More soon.