the best time to hire a GTM Engineer to scale our data platform was before we hit a $1B revenue run rate the second best time is right now this person will be responsible for building our... - company and people graphs, tracking companies, labs, researchers, and engineers - forecasting inputs for both compute capacity (supply) and sales pipeline (demand) - customer and user journey state machine, ensuring that we're surfacing the right action to the right person at the right time you wouldn't be starting from scratch - we have an incredible team, a cracked engineering culture, the latest in agent infrastructure, and the mandate of Heaven to build the best GTM data platform the world has ever seen apply at the link below and/or DM me a piece of technical writing or presentation about a data system/product you've built jobs.ashbyhq.com/modal/dd6fe…

these guys totally aren't hiding anything.. 🧩 hone.com

you can play snake on the 404 page on the new Hone website

early mornings, i see roomie @jess_liu_ at the dining table repeating over and over again... "own the outcome, set the standard, win with class, commit to the racing line" i once asked her why, and she yelled 'WAKE UP. BE AN ENGINE.' i stumbled away dazed. but i understood. you should too @hone.

“You can give the Engine a goal or an ambiguous workstream, and it creates the loop to understand, measure, iterate, and self-improve.” Hear how @ChrisPrinz at @modal is rethinking GTM with Hone Engines that go beyond executing tasks to own entire workstreams, continuously measure results, and improve performance. See what Engines can do for your organization at Hone.com.

Such an inspiration to work with @moritz_stephan and the team @hone. They saw the gap between what AI can do and what organizations can put to work—and are closing it. The shift enables orgs to go from buying tokens to entrusting AI with meaningful, long-running work that delivers real value.

Today Hone launches Engines: AI that owns business outcomes. Chatbots give answers. Agents do tasks. Engines deliver outcomes, and we hold them responsible for creating real value, measured in actual dollars instead of tokens or vibes. You point it at a business metric, and the Engine figures out the rest. It onboards itself, builds the necessary agents, skills, and memories, collaborates with the right people, and gets better with experience. From AI leaders to global enterprises, our customers have already used Engines to: • Uncover millions in procurement savings • Optimize product funnels • Manage credit risk • Increase qualified pipeline in new territories • Accelerate implementations and improve account management To scale this impact across the economy, we’ve raised a $60M seed led by Benchmark and Index with participation from Elad Gil, Hanabi, Definition, Diffusion, Lux, SV Angel, and Align. AI’s raw capabilities are far ahead of the value it creates in the real world, and Engines will help close the gap. Here’s how they work…

Today Hone launches Engines: AI that owns business outcomes. Chatbots give answers. Agents do tasks. Engines deliver outcomes, and we hold them responsible for creating real value, measured in actual dollars instead of tokens or vibes. You point it at a business metric, and the Engine figures out the rest. It onboards itself, builds the necessary agents, skills, and memories, collaborates with the right people, and gets better with experience. From AI leaders to global enterprises, our customers have already used Engines to: • Uncover millions in procurement savings • Optimize product funnels • Manage credit risk • Increase qualified pipeline in new territories • Accelerate implementations and improve account management To scale this impact across the economy, we’ve raised a $60M seed led by Benchmark and Index with participation from Elad Gil, Hanabi, Definition, Diffusion, Lux, SV Angel, and Align. AI’s raw capabilities are far ahead of the value it creates in the real world, and Engines will help close the gap. Here’s how they work…

Former OpenAI, Cognition staffers want AI agents running businesses bloomberg.com/news/articles/…

What if every organization could access the operational leverage of a world-class internal engineering team, without having to build and maintain one for every function? At Ramp, I saw firsthand how much leverage exceptional internal engineering teams can create. But that model has an important constraint: engineering talent is scarce, and its opportunity cost is enormous. Every company building its own internal AI platform is probably not the Pareto-efficient equilibrium in the long run. Companies have always faced a tradeoff: invest in internal infrastructure that improves operational efficiency, or invest in the core product, where returns can compound into durable competitive advantage. Ideally, you do both. But not every company has the resources to do so, and even those that do need to consider where the marginal return on engineering effort is highest. AI has the potential to fundamentally shift that production frontier. What if the people closest to the problems, the domain experts who understand the business best — could directly shape, deploy, and improve the systems that solve them, without being bottlenecked by engineering capacity? That's what we're building with Engines at Hone: AI that learns an organization's context, coordinates across people and systems, and continuously works toward measurable business outcomes. The opportunity isn't just automating workflows. It's changing how organizations allocate scarce technical talent, while empowering the people who understand the problems to own their solutions. Let engineers compound competitive advantage in the core product. Let domain experts drive the systems that improve how the business operates. Making this possible requires a rare combination of frontier research, engineering, and deep operational expertise. That's what makes the team we're building at Hone so exciting. We're hiring. Come build with us. DM me or learn more at Hone.com

Somewhere between personal agents booking flights and frontier labs solving Navier-Stokes sits a large, quiet gap. Inside most organizations, AI still produces no measurable return. Five months ago I founded Hone with Moritz Stephan and Oliver Brady. Today we’re announcing our $60M seed led by Benchmark and Index Ventures, and launching AI Engines: persistent agents that own business outcomes. Most AI products serve individual productivity. They assist with tasks, answer to no outcome, and get a fraction of the freedom or accountability you’d give a model this smart. The few that run at the organizational level are usually lengthy custom builds. Today it is surprisingly hard for a business to point frontier intelligence at its most critical outcomes. An Engine is built for exactly that. You point it at a business outcome. It onboards itself, builds the agents, skills, and memories the job needs, works with the right people, and gets better with experience. It doesn’t produce output. It produces results. As a team, we believe AI should be held to a higher standard. We measure our work not by benchmarks, but by the business results we drive for customers in the real world. Intelligence is no longer scarce, and the economy should grow like it. That happens when subject matter experts stop doing the work and start building the systems that do it. Watch the demo to see how Hone enables that.

We believe translating frontier intelligence into economic value takes AI systems that work for the organization, own outcomes instead of tasks, and get better the longer they run. That's why we built engines. Today we're sharing some of what we've learned building them, starting with our simulation work for self-improving harnesses. A company can't be rewound. For an engine to test a change to itself, we simulate the systems and people around it and replay weeks of work in minutes. We're a small, talent-dense team of engineers who helped build frontier AI products at Cognition, Mercor, Decagon, Ramp and Midjourney. If this work is interesting to you, DM me.

Today Hone launches Engines: AI that owns business outcomes. Chatbots give answers. Agents do tasks. Engines deliver outcomes, and we hold them responsible for creating real value, measured in actual dollars instead of tokens or vibes. You point it at a business metric, and the Engine figures out the rest. It onboards itself, builds the necessary agents, skills, and memories, collaborates with the right people, and gets better with experience. From AI leaders to global enterprises, our customers have already used Engines to: • Uncover millions in procurement savings • Optimize product funnels • Manage credit risk • Increase qualified pipeline in new territories • Accelerate implementations and improve account management To scale this impact across the economy, we’ve raised a $60M seed led by Benchmark and Index with participation from Elad Gil, Hanabi, Definition, Diffusion, Lux, SV Angel, and Align. AI’s raw capabilities are far ahead of the value it creates in the real world, and Engines will help close the gap. Here’s how they work…

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.

Introducing SWE-2, our closest model yet to the frontier. On leading evals, it scores on par with recent frontier models – at up to 70% lower cost. We scaled RL to multiple trillions of parameters, with a refined recipe that pushes the Pareto curve on both capabilities & cost.

Happy to announce that I've joined Hone. We're a small, ambitious team taking a new angle on what AI working for you will actually look like. Models have advanced incredibly fast, but 90% of the tools the world uses at work haven't kept up. We're changing that.