As the agentic world moves towards more specialized workflows, efficiency becomes very important! A librarian who has been working at the same library for 30 years doesnt need to look up skills for how to answer a question or search through her tools to know that the library opens every day at 9 am! Same for agents! As agents specialize, they should become way more efficient at solving tasks. Thats why I am really excited about our research on intelligence density. The goal here being how do you get models to perform better on tasks with higher token efficiency!

I made a list of great startups to join. It's called the Breakout List. The list has 92 companies. These are the 20 with 25 or fewer employees: - Hone (@moritz_stephan, @CarloWillem, @oqbrady) - Normal (@ansonyuu, @hudzah) - Standard Intelligence (@G413N, @devanshpandey) - Tacit Labs (@ninklefitz, @AmDroste) - American Terawatt (@atroyn, @rslparker, @aranibatta) - Conduit (@clemvonstengel, @riopopper) - Convergent (Omkar Savant, Vivek Katara, @debnilsur) - Core Automation (@MillionInt, @_arohan_) - Engram (@dan_biderman, @EyubogluSabri, @realJessyLin) - Instinct (@noahrshinn) - Keenable (@styskin, Matthias Petri) - Lumaril (Mark Elliot, Ben Duffield) - Neion Bio (@Dimkell, Sam Levin) - Pangram Labs (@max_spero_, @bradley_emi) - Quadrillion (@echinaceous) - Re (@karnsaroya, @AnandDhillon, @thecliffwhite, @benaneesh) - Ricursive (@annadgoldie, @Azaliamirh) - Sail Research (@neilmovva, @blintzbase) - Trajectory (@rronak_, @michaelelabd, @QuantumArjun) - Watney Robotics (Sean Cheong, Ryan Gannon) Picks from Elad Gil, Charlie Songhurst, Keith Rabois, Mike Vernal, Alana Goyal, Sonya Huang, Ramtin Naimi, Marc Bhargava, Cory Levy, Aashay Sanghvi, Konstantine Buhler, John Luttig, Varun Gupta, Ray Tonsing and Avichal Garg. Disclosure: I'm a small investor in American Terawatt, Convergent, Standard Intelligence and Trajectory (in this post), and in Factory, Physical Intelligence and SF Compute (elsewhere on the list). I didn't vote. The full list is on Breakout List.

At Trajectory, we care about storytelling. The storytelling about continual learning, the storytelling about the research breakthroughs it’ll take to get there, and the storytelling about the product that we need to will into existence. Brand is part of how we tell it. Here’s a behind-the-scenes look at the work we did with @metalab to craft ours

Intelligence and Experience are orthogonal vectors Terence Tao is perhaps the world’s smartest person, but drop him into an accounting firm or onto a construction site and on day one he’s not going to be very productive @trajectorylabs calls this The Experience Gap, and they have a way to close it @QuantumArjun explained how at our Sovereign AI event: 00:00 Introduction 00:12 Building the platform for continual learning 01:33 The experience gap: models have IQ but no tenure 02:52 Traceability → model spec → better models and harnesses 05:27 Four wishes for the agent ecosystem 06:34 Wish 1: Trace the whole tree — and capture the corrections 08:03 Wish 2: Evals from real traffic, graded in the real harness 09:26 Wish 3: Let the agents cook, and make tool responses informative 10:34 Wish 4: Get comfortable on open weights, experiment with routers 11:51 Why owning your intelligence shouldn't be consulted away 13:15 Demo: import a benchmark, train a model, deploy it 14:28 Q&A: What's the trainable object — weights, harness, or context? 16:08 Q&A: Continual learning without training on customer data 17:13 Q&A: Episodic memory and the hierarchy of feedback 19:37 Q&A: Where continual learning matters most

Intelligence Density or intelligence per token is one of the most important metrics for specialized intelligence. As each company starts to own their own intelligence, doing a task right doesn't become the only goal but how efficiently the agent can complete the task. Really excited about the research coming out of Trajectory on how to make models have way higher intelligence density!

Solving for whole body control🤸 and dexterous manipulation 🦾 is probably one of the hardest co-optimizations anyone could attempt! It’s a trade-off: tying knots, threading a needle, etc. comes at the cost of coordinating a full humanoid frame: bending, reaching, etc. Usually when you push for one you lose the other Really cool to see Gemini Robotics move the pareto frontier forward (WBC x DM) by delivering a model that does both in a single system!

Trajectory just signed the call for Open Weights 👀👀 For enterprises, open weights means you can run models on your own terms, adapt them to your data, and ship without betting your roadmap on another company. In other words, its the difference between renting and owning intelligence! Also, for science, having open weight models allows scientists to build compounding insights, to not just stand on shoulders of giants but to propel scientific advances at an ever-growing speed! Thats why we at Trajectory are very proud to have signed the call for Open Weights and are very excited to keep pushing the frontier with our partners!

Inkling is our first open model from @thinkymachines and is now available on Tinker! Check out these quotes from Tinker customers on their experience with Inkling: @_Mantic_AI: "Not only does Inkling outperform Kimi K2.6 on our forecasting evals, it does so with half the output tokens." @trajectorylabs: "We’ve been impressed by how sharp and efficient the model is. Its reasoning is concise, its tool calling is consistently strong, and it holds up well on complex, long-horizon agentic tasks. It feels like a meaningful unlock for what teams can build with open-source models designed for customization." @lightningrodai: "We came away impressed by the model’s underlying reasoning ability. It’s thoughtful, original, and refreshingly unsycophantic.”

A model drop by Thinky 🚨🚨 Have been doing some early testing on the model for the past couple of days. Here are some of my findings 1. The reasoning is sharp and concise! Always love to see models that dont ramble 2. Tool calling is beautifulllllll, Its consistent, clean, very well designed for streaming , multi-tool call per step, etc.! 3. Holds up impressively on agentic tasks. In my testing, I was particularly impressed with its ability to run long-horizon tasks with solid error recovery. 4. Most importantly, it was built from the ground up for post-training and customization and this is a real unlock for teams building on open source!! This will be greatly beneficial for continual learning workflows at @trajectorylabs! Amazing step for American OSS models, really excited to keep "tinkering" with it lol

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!

At Trajectory, our goal is to bring continual learning to every company. That means training on production data as it actually arrives: one task, one trajectory, often stale with respect to the current model. Making SDPO work on long-horizon agentic tasks is a major step toward env-free RL and real online learning!

Day 4 of Trajectory!!!!!! For today, we are showcasing our vision to the world. What will the world look like? Where do product companies fit into this world? How will software change and evolve over time? Beautiful writing by our @QuantumArjun, really crisp telling of how we think the world will evolve.

We’re taking a quick break for the 5 days of Trajectory, but wanted to take this time to say that we’ve been named to @Redpoint’s 2026 Infrared 100 as one of the companies shaping the future of AI infrastructure. We're so grateful for the recognition so early in our journey, and want to congratulate the other awardees as well!