operate and scale AI-first companies through human-AI orchestration
San Francisco x London
Joined August 2021
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DeepFlow retweeted
Today we're releasing data on models accelerating research at OpenAI.
Recursive self-improvement could be the most important contributor to AI capabilities over the next few years, but by default it will only be seen inside a few frontier AI labs. Being transparent is more urgent than ever, so we can inform the public discussion on whether and how to pace model development. I ask other AI companies to do the same.
openai.com/index/research-ac…
DeepFlow retweeted
I think this is spot on. The most useful work in the coming years will be about leveraging AI to help improve and reform liberal democracy, the rule of law, separation of powers, free speech, coordination, and constitutional safeguards.
One heuristic I have for AI is: if somone can instantiate their preference or desire really easily, if principal agent problems are materially reduced, if you can no longer rely on inefficiency or bloat as indirect hedge - then the 'rules of the game' matter more than ever.
These are all very difficult questions with or without AI. And I'm concerned with two things in particular: first, the easy appeal of anti-elite populism - people who just think 'well let's have vetocracy everywhere, let's leverage the emotions of the masses for short term gain'.
And second, the appeal of scheme-y behaviour - instrumental convergence for political operators. This is harder to pin down, but basically a variant of "I want goal X, so anything that gets me closer to this goal is good" - what leads to all sorts of bad policy and unsavoury alliances.
And instead of trying to 4D chess it or try to recreate politics from first principles, I think technologists should actively enage with experts in all sorts of discplines: constitutional scholars, public choice economists, game theorists etc. Converesely, many of these experts should engage with technologists more instead of coping with obsolete op-eds about how AI is fake or something.
Lastly, improved AI capabilities means you can now use these systems for more things than you could have before. I couldn't write software a year ago and now I can create a viable app in a day. This dynamic will continue, and will reward people who are agentic and creative.
Are you a local councillor? Well now you have 1000 agents at your disposal - what can you now that that was otherwise unthinkable? Are you someone who lives in their district? Now you have even better tools to hold them to account. Are you an academic? Great, now consider how the many bylaws, rules, structures, institutions, incentives are messing up incentives and progress, what should be improved, and how to get streamlined coordination rather than automated obstruction.
To be clear I am interested not so much in “what regulations should we have in 2045” (wrong question! Think like you’re at the continental congress, not a modern congressional committee!), it’s more:
“in a world with such powerful technologies, what limits do we place on state power, and how on earth will we enforce them, given the dramatic expansion of state power we have witnessed in the modern era, but also given how much we know that limits on state power hinder positive state capacity?” This is a hard question! Almost no one cares about this. But I do.
DeepFlow retweeted
When you see how people use Claude Code/Codex/etc it becomes clear that managing agents is really a management problem
Can you specify goals? Can you provide context? Can you divide up tasks? Can you give feedback?
These are teachable skills. Also UIs need to support management
DeepFlow retweeted
What you are describing is @DeepFlowAI : graph-based workflows that seamlessly pass work from many humans to many agents, with workspaces below each node. It’s a human-ai orchestration layer than sits on top of all existing applications, co-pilots, agents.
We’ve set a global benchmark here: arxiv.org/pdf/2510.02557
deepflow.com
@Guillaume_rx21 @latticecut
DeepFlow retweeted
Experienced founders know this already: 𝘵𝘩𝘦 𝘳𝘪𝘨𝘩𝘵 𝘦𝘤𝘰𝘴𝘺𝘴𝘵𝘦𝘮 𝘢𝘤𝘤𝘦𝘭𝘦𝘳𝘢𝘵𝘦𝘴 𝘦𝘷𝘦𝘳𝘺𝘵𝘩𝘪𝘯𝘨. @SlushHQ delivers that every single year.
Earlier this week, EWOR Fellows, Partners, and community members validated this again in Helsinki. Highlights included:
𝟬𝟬𝟭 Co-hosting the EWOR × @MerantixCapital side event at Slush. A select group of cracked founders and investors met for direct and valuable conversations. A big thank you to Noora Saksa, @AdrianLocher and @perlberg for making it happen.
𝟬𝟬𝟮 Co-hosting a dinner with several Nordic VCs with the theme “Building a Better Europe.” Several EWOR Fellows met top-tier investors for clear, high-quality discussions. Shoutout to Growth minds, Pinecone, Nordic Science Capital and @stripe for bringing everyone together.
𝟬𝟬𝟯 EWOR Fellows totally slushed it: @LajmiRihab spoke about @asendia_ai. Ashkan (AMARA), Mo (@EpimindsAI) and @jbgilmore (@DeepFlowAI) pitched at five.degrees to an audience of 150 talents.
𝟬𝟬𝟰 EWOR Partners Paul and Petter led packed sessions on navigating co-founder breakups and what the 'Zero to Unicorn' journey looks like in reality.
The momentum continued all over the city. Meetings. Introductions. Follow-ups. Spontaneous gatherings that moved work forward and connected the right people at the right time. The conversations that matter. The ones that only happen in person.
See you next year in Helsinki!
DeepFlow retweeted
This is literally @DeepFlowAI
DeepFlow retweeted
Julian Schrittwieser (Anthropic):
- Discussion of AI bubble on X is "very divorced" from what is happening in the frontier labs. "In the frontier labs, we are not seeing any slowdown of progress."
- AI will have a "massive economic impact". Revenue projections for OpenAI, Anthropic and Google are actually "fairly conservative".
- Extrapolating from things like METR data, next year, the models will be able to work on their own on a whole range of tasks. Task length is important, because it unlocks the ability for a human to supervise a team of models, each of which works autonomously for hours at a time (vs. having to having to talk to an agent every 10 minutes to give it feedback).
- "Extremely likely" that the current approach to training AI models (pre-training, RL) is going to produce a system that can perform at roughly human level in basically all tasks we care about productivity-wise.
- On Move 37: "I think it's pretty clear that these models can do novel things." AlphaCode and AlphaTensor "proved that you can discover novel programs and algorithms". AI is "absolutely discovering novel things" already, and "we're just moving up the scale of how impressive, how interesting are the things it is able to discover on its own."
- "Highly likely" that sometime next year we're going to have some discoveries that people unanimously agree are super-impressive.
- AI will be able to on its own make a breakthrough that is worthy of a Nobel Prize in 2027 or 2028.
- On AI's ability to accelerate development of AI: A very common issue in many scientific fields is that it becomes more and more difficult to make advances as the field progresses (i.e., 100 years ago, a single scientist could discover the first antibiotic by accident, whereas now it takes billions of dollars to discover a new drug). The same might happen with AI research - even though AI will make research of new AI more productive, there may not be an explosion due to new advances becoming more and more difficult to find.
Failing to Understand the Exponential, Again?
My conversation with @Mononofu - Julian Schrittwieser (@AnthropicAI, AlphaGo Zero, MuZero) - on Move 37, Scaling RL, Nobel Prize for AI, and the AI frontier:
00:00 - Cold open: “We’re not seeing any slowdown.”
00:32 - Intro — Meet Julian
01:09 - The “exponential” from inside frontier labs
04:46 - 2026–2027: agents that work a full day; expert-level breadth
08:58 - Benchmarks vs reality: long-horizon work, GDP-Val, user value
10:26 - Move 37 — what actually happened and why it mattered
13:55 - Novel science: AlphaCode/AlphaTensor → when does AI earn a Nobel?
16:25 - Discontinuity vs smooth progress (and warning signs)
19:08 - Does pre-training + RL get us there? (AGI debates aside)
20:55 - Sutton’s “RL from scratch”? Julian’s take
23:03 - Julian’s path: Google → DeepMind → Anthropic
26:45 - AlphaGo (learn + search) in plain English
30:16 - AlphaGo Zero (no human data)
31:00 - AlphaZero (one algorithm: Go, chess, shogi)
31:46 - MuZero (planning with a learned world model)
33:23 -Lessons for today’s agents: search + learning at scale
34:57 - Do LLMs already have implicit world models?
39:02 - Why RL on LLMs took time (stability, feedback loops)
41:43 - Compute & scaling for RL — what we see so far
42:35 - Rewards frontier: human prefs, rubrics, RLVR, process rewards
44:36 - RL training data & the “flywheel” (and why quality matters)
48:02 - RL & Agents 101 — why RL unlocks robustness
50:51 - Should builders use RL-as-a-service? Or just tools + prompts?
52:18 - What’s missing for dependable agents (capability vs engineering)
53:51 - Evals & Goodhart — internal vs external benchmarks
57:35 - Mechanistic interpretability & “Golden Gate Claude”
1:00:03 - Safety & alignment at Anthropic — how it shows up in practice
1:03:48 - Jobs: human–AI complementarity (comparative advantage)
1:06:33 - Inequality, policy, and the case for 10× productivity → abundance
1:09:24 - Closing thoughts
DeepFlow retweeted
New paper from the @DeepFlowAI team!
Orchestrating Human-AI Teams: The Manager Agent as a Unifying Research Challenge
As we start deploying AI agents for real tasks, the next step is Manager Agents that coordinate teams of human and AI workers.
📄 arxiv.org/abs/2510.02557
(1/3)
DeepFlow retweeted
As a researcher at a frontier lab I’m often surprised by how unaware of current AI progress public discussions are.
I wrote a post to summarize studies of recent progress, and what we should expect in the next 1-2 years:
julian.ac/blog/2025/09/27/fa…
DeepFlow retweeted
Task length is the key AI metric to watch - and it’s accelerating faster than almost anyone outside the industry realises
The METR paper that says that “the length of tasks AI can do is doubling every 7 months” radically undersells the scaling that we’re seeing at Replit.
It might be true if you’re measuring one long trajectory for a single model class.
But this is where an agent research lab’s alpha is at.
We build multi-agent architecture and use different models from various providers to tap into their latent abilities across various tasks.
DeepFlow retweeted
Everyone’s debating whether AI will take call centre jobs.
We’re missing the real story.
AI won’t just displace customer service – it’s coming for lawyers, consultants, and finance professionals. The careers that drive UK GDP. The “safe” graduate jobs.
We won’t need anywhere near today’s numbers. The billable hour is dying.
So where does all that economic value go? Increasingly to a small group of US tech giants – today’s Magnificent Seven becoming tomorrow’s fourteen, maybe twenty-one. The concentration of wealth in a handful of platforms while entire UK sectors restructure.
This doesn’t have to be our future.
We need two things, urgently:
• Education rebuilt for the AI age
• UK AI companies that can scale and list in London, not California
When companies list abroad, so do the jobs, tax revenues, and wealth creation.
The UK needs to decide: do we want to own part of the AI economy, or just work for it?
DeepFlow retweeted
“This is the slowest AI will ever move—and the worst AI will ever be.” – @jbgilmore, CEO of @DeepFlowAI.
@ChrisMadden_ and Jonathan talked about human-machine teaming, ‘process as a product,’ and the $10T opportunity in knowledge work.
Watch it here: youtu.be/rGFRv0qvNuk
DeepFlow retweeted
We’ve fined Monzo Bank Ltd £21,091,300 for its inadequate anti-financial crime systems and controls between October 2018 and August 2020.
Read more fca.org.uk/news/press-releas…
#FightingFinancialCrime #FinancialRegulation
DeepFlow retweeted
Internship opportunity!
Work with the ML team at @DeepFlowAI London to build LLM agentic systems for multi-agent collaboration with human and AI workers.
🚨Internship🚨: ML Engineer & Research
@DeepFlowAI London (LLMs, Agentic Systems, Multi-Agent Collaboration) — We are looking for a highly motivated intern to join our ML team led by Dr. Stefano Albrecht (Director of AI at DeepFlow)
Apply here: deepflow.com/internship-ml-e…
DeepFlow retweeted
Announcing: Multiplier Holdings - a technology company building AI-native professional services firms.
DeepFlow retweeted
Looking forward to sharing some of our recent research contributions at @MLStreetTalk's first London AI meetup 🤩
We are running our first physical event in London on 14th July! We have Tim Nguyen @IAmTimNguyen from DeepMind and Max Bartolo @max_nlp from Cohere and Enzo Blindow (VP of Data, Research & Analytics) at @Prolific joining us. Not many seats for the first one. lu.ma/9rgk4l5h
DeepFlow retweeted
@lorenz_wlf and his #RLDM2025 poster on “Heterogeneous Knowledge for Augmented Modular Reinforcement Learning”.
Preprint: arxiv.org/abs/2306.01158
