AI × XR × Culture | Co-founder @MidBrainAi and @thepointlabs | ex-@volvocars innovation lead |Teaching machines to make sense. Nerd by day rock star by night 🎸
Copenhagen, Denmark
Joined September 2011
- Tweets5.4K
- Following2.1K
- Followers1.9K
- Likes22.2K
Building AI that learns while it works.
We’ve just launched the new MidBrain website. It brings together our research and product, and gives a clearer picture of where we want to take things.
For the past year, we’ve been working on a question:
How can each new experience make an AI system more capable?
When an agent repeats a mistake you’ve already corrected, you end up doing the same work again. We want that correction to change how it handles the next relevant task.
The bigger ambition is that the longer a person or team works with an AI system, the more useful it becomes. It should develop capabilities through experience and retain what it has learned as the work changes.
At @MidbrainAI , we’re connecting research in continual learning with infrastructure people can use today. Our product turns feedback into learned procedures agents can apply to future tasks. Our research explores how learning can happen during an interaction itself, and how systems can keep adapting without losing useful knowledge.
The new site shares our published research, the questions we’re investigating, and the product you can try.
If you’re building agents that need to improve over time, we’d love for you to put MidBrain to work. Try it on a real workflow. Challenge our assumptions and tell us where it falls short.
There’s a lot we still need to figure out, and working with people using these systems is part of how we’ll get there.
Explore the research and try the product: midbrain.ai
Timmy Ghiurau retweeted
When Mercor earned a pilot with DeepMind in 2024, the entire company pulled an all-nighter to handwrite all of the data in 24 hours.
Building a legendary company requires extreme intensity, especially in the early days.
Timmy Ghiurau retweeted
Surprised that so few people are talking about this.
Persistence has become a core capability of recent models (Opus 5.5, GPT 6 Astra, Gemini 4 Argon,...).
I was just reading through the Gemini 4 Argon Minecraft paper details and noticed that no roles were assigned to the agents.
Somehow the four agents completed the goal after 27 hours, 2,783 programs, and 605 deaths. They recovered items, revised strategies, and shared lessons. I think this is a good example of persistence helping them eventually succeed.
Some more thoughts for discussion:
So this is not just about hooking an agent up to an external memory system.
It's also not about loop-prompting it with /goal, although that is one of the most effective techniques right now.
This is about the model's native ability to choose what to persist, and how and when to use it.
It unlocks long-horizon capabilities, as agents often execute more complex tasks more effectively through persistence.
So it's less that the models got more intelligent and more that they got better at coordinating and planning for more complex tasks.
We are in the early days of this. But this is an important capability to optimize to really unlock AI for scientific discovery.
This is also important for building more personalized agents with identities and roles that can track and follow human preferences well and learn continually over time.
We also don't entirely know how to quantify and measure it as a field. Reward hacking combined with persistence can also enable weird, unsafe agent behaviors.
Just a few quick thoughts, and I hope we can start deeper conversations about this.
I am tracking this space closely and will be sharing research on the topic as it surfaces.
does your agent get better the longer you work together?
I think better models raise the baseline for everyone. Your decisions, corrections and experience give an agent something specific to learn from.
and with that, the connection between observing work, memory and failure logs in this post. The progress should be visible: less explaining, fewer repeated mistakes, more work you can trust.
I spent 48 hours in SF hanging out with billionaires, data labs and AI founders and execs running $1B to $100B companies.
My observations:
1. Sooo MANY stories of data brokers making like $2M a WEEK selling company data to labs. I can't tell if it's the beginning or a bubble, and I kept wondering which niches are left. Digging into it actively.
2. At dinner, someone asked the table, "If a new model drops next month, does your company get better or worse?" HALF the table went quiet! I think that's the best question to ask about any AI startup right now.
3. The bar people talk about now is hitting $1B in revenue in 12 months (I guess that's the new unicorn?). What I didn't hear much about is retention. AI revenue can come in fast and leave fast, and I think we're going to see a lot of stories of AI companies with huge revenue and terrible retention.
4. It's pretty clear we're living through the reinvention of the computer. The model is the CPU, the harness is the operating system, and skills and plugins are the apps. Thinking about it that way makes it easier to see where the whitespace is.
5. We're maybe 12 months from an agent that can watch you work for a week and then do your job without any instructions. The screen recording plus agent memory plus local model combination makes this possible right now.
6. Agent harnesses feel like the new GPT wrappers, in a good way, especially vertical ones. The best teams I talked to treat the model as swappable. One founder walked me through routing different models to different jobs: one for taste and visuals, a cheaper one for the everyday stuff, another for text.
They use prompts they can move across models over fine-tuning, and A/B test the output on what users actually do with it: do they edit it, export it, share it? A vertical harness is basically four things: the connectors for that industry's data, evals written by people who know the work, rules for what the agent can and can't touch, and a clean screen for a human to review. The model is maybe 20% of it.
7. There's a lot of reverse engineering going on/cloning going on more than I've ever seen. People are decompiling software and rebuilding it with agents, and someone showed a suite of like 10 apps that are worth $5B+ each all that he cloned open source. Me and everyone are still thinking through what it means when software gets this easy to copy.
8. My guess is the value moves to data. If the software can be rebuilt quickly, what's worth paying for in a legacy SaaS company is the years of customer data and workflows inside it. Some SaaS companies are secretly data companies.
Gets me thinking about how do you truly value data for this saas companies? Like if we were buying these dying saas companies at 2x ARR before (as example), maybe there is a better metric. Idk, just thinking out loud here.
9. It feels like a good chunk of legacy software won't make it to the agentic era, especially anything built around people clicking through a lot of screens.
10. Every company I talked to wants to pipe its internal data into one place and expose it to agents through MCP. Then they look at the data and realize it's a mess: five versions of the same customer, old Slack channels, PDFs nobody can search. Cleaning that up feels like a big consulting opportunity!
11. A lot of founders are building the basic building blocks for agents: APIs, data layers and tools. Some of the companies I saw barely have a UI because the main user is an agent. I really believe the startups that build primitives to this agentic era will do extremely well. Building blocks is the best way to think about it.
12. Labs can get plenty of training data, but it's harder to get real examples of agents failing inside companies. Whoever has those logs has something valuable.
13. Someone running a big consumer company told me the next fight is over memory. The model that remembers the most about you wins, and the smartest model matters less than people think.
14. Physical AI came up a lot. Astra I think was the first model that got people into AI can actually do CAD, and I think a lot of people are thinking what does vibe manufacturing really look like. The line I keep thinking about came from a robotics founder: the hardest problems in AI are deployment problems now, not research problems. Their robots work on their own, call a human when they get stuck, and every one of those calls becomes training data.
15. Cybersecurity companies are having a moment and are actually cool companies to build now, especially the ones that clean up after a breach. Every agent with access to your systems is a new door into your company, and a lot of those doors were left open.
16. Some startups are worth more dead than alive now. A shutdown company with 5 years of Slack, email and code can get paid for licensing all of it. I wouldn't be surprised if founders start factoring that into when they shut down.
17. People are starting to talk about monetizing the agent web. It feels really early, but when the most popular user of the internet is an agent, what startups should you be building?
18. I think agent insurance is coming. Once an agent makes an expensive mistake, insurers will want audits before they cover anyone.
19. Several people said the hire they want most is someone with taste who can write evals. The models are getting good at generating ideas, so the hard part is judging which ones are good.
20. New trend I observed: startup raises VC, uses venture dollars to become a creator, startup fails, founder falls back on being a full-time creator and incubates other products bootstrapped.
21. Getting SIGNIFICANT distribution is HARD right now. Founders are sponsoring buses, buying billboards like crazy and throwing parties with famous DJs to get noticed. Kinda feels dotcom era in that sense.
Attention is what's scarce! The growth guy going viral sharing his UGC strategy from Wispr Flow is a good example. It's the sauce everyone wants, and I truly believe there are probably only 50 people on the planet who know how to scale these types of businesses efficiently in this era.
Always good to get that dose of SF. I felt sooo lucky to have such an incredible trip during such an incredible time in building businesses. Surreal in so many ways.
Happy to be home and locked in.
Until next time!
memory can turn a suggestion into permission.
an agent proposes: "i could send these updates for you every friday."
i move on without answering. later, the proposal gets compressed into "send weekly updates."
the memory is there. the agreement never happened.
that's the failure in this paper that interests me. remembering more about a person doesn't mean understanding what they've agreed to. more context can even make the agent more confident about doing something it was never asked to do.
i'd want the system to keep the proposal and its status together. who suggested it, whether i accepted it, what it covers, and whether i've changed my mind.
but the answer can't be "ask me every time" either. if i've agreed to something within clear limits, making me approve it again is another kind of failure.
the useful test is whether the agent acts when the agreement applies and holds back when it doesn't. not whether it can recall the conversation.
the paper tests this in controlled conversations. real agreement can be messier: gradual, conditional, sometimes contradictory. i wouldn't treat this as solved.
if i'm going to build a long-term relationship with an agent, it needs to remember the boundaries of that relationship too.
arxiv.org/html/2610.18684v1
Timmy Ghiurau retweeted
there is a massive different between intellectual output & intellectual presence.
i.e. intelligence can be demonstrated through artifacts but intellectual chemistry, the ability to think in real time, improvise, use analogies, develop ideas with others, & have genuine conversational rhythm can only really be experienced through irl interaction or a podcast type setting where that person is the guest.
this is why when you meet ppl irl off of here it is rarely if ever the same experience as their posts & can be super disorientating. as if the internet bifurcated individuals & created two different personas. it is very very rare to find ppl whose intellectual presence is as interesting as their intellectual output.
I dont think software is dead, but the idea of software as a finished product might be... or software lifecycle.
i agree that we're moving towards a world where anyone can build software for themselves. the cost of creating it is collapsing for sure.
but building something and having something that understands you are two very different things.
cause if every application starts from zero, we're just replacing one kind of friction with another.
the big change happens when software accumulates experience. when it remembers why you made certain decisions, understands your preferences, learns from mistakes, and adapts as your needs change.
at that point, we're no longer talking about generating applications on demand... we're talking about systems that evolve alongside us.
and there's another question here. if everyone can create software, what becomes valuable then?
perhaps less the ability to build, and more the ability to understand what is worth building in the first place.
the fundamental basic economics misunderstanding that ppl have esp on here is that they think normal ppl need to use models to build their own software to be disrupted. they obviously don't & they obviously won’t.
but even if just 1% of the population can build software, that's enough to flood the entire supply side with millions of new products collapsing the cost to near zero.
you absolutely don't need universal adoption to destroy scarcity. you just need enough producers to overwhelm demand. you can feel this now already. i never pay for software because i just ask claude to point to something like a chrome extension or a mac app & it’s just there. at some point your agents will also auto update & maintain your software automatically too.
Praxis → Leap → Multiplicity.
Back at UNFINISHED. Different years, different questions.
From designing experiences, to taste and creative judgment, to memory and what we carry from one another.
Different questions, perhaps the same curiosity.
A few moments from 2026.
i was on a plane with bad internet and a paper i'd seen in my inbox stuck in my head.
still managed to use instinct in whatsapp. it found the paper, and we riffed on a couple of ideas. turned them into a plan and put it in midbrain memory.
codex picked up that plan. by the time i landed, i already had a prototype.
that's the bit i care about. i started with a half-formed thought on my phone, not a brief in a code editor. i didn't have to wait until i was at my desk, or explain the whole thing again when the work moved to codex.
it's a prototype, not a finished product. the handoff still depends on what we write into memory.
but i got off the plane with something i could try, instead of another idea to get back to later.
i'd judge a personal agent by what i can take with me when i leave.
chat history isn't enough. i want the corrections that changed how it acts.
portable memory cuts lock-in, but carrying every old rule creates a new problem.
i'd review the rules before moving them.
Timmy Ghiurau retweeted
there’s something quite awkward about all the personal agents in the current hype cycle
muse, grok bot, instinct, dots, and whatever google, anthropic will come up with
none of them is “mine”
i’d be trusting a vendor for some of the most sensitive data about me and bet on them handling it with extreme responsibility. what if they have a data leakage incident? what if they hired a wrong employee? what if they simply break bad?
i’d also be betting on their model. right now i’ve set up a lot of my stuff in grok bot, but what if their model falls behind? what if another model becomes 10x better on pure intelligence? then i’ll either miss out on a better assistant, or have to bite the bullet and do a full migration
i’d also have to bet on their product. they may not build the features i want. they may not allow me to customize enough. and one day my assistant may show me ads
that feels like way too much betting than what i’d be comfortable with, if i truly want literally everything in my life to go through and be taken care of by an assistant
with all that considered, i’m more bullish on open source personal agents that run on people’s own computers. but i think these open source projects have to break out of the assumption that their users are developers and they can just throw a repo at them and ask them to launch a terminal
a well polished, fool proof, community maintained, vendor agnostic, free personal agent that everyone can run by themselves without locking into a SaaS - that’s what i think many people will need
Timmy Ghiurau retweeted
a16z's Olivia Moore thinks consumer AI has its business model backwards:
"Substantially all of the consumer AI revenue thus far has come from direct subscriptions. On our web list, 85% monetize via subscriptions, another 62% via credits or extra usage payments."
"Only 13% had ads or other options where you are the product instead of paying for the product."
"If you look back at the history of the consumer internet, that's a pretty unnatural inversion. Almost all of the really big consumer technology companies we have now make the vast majority of their revenue from ads or transaction fees, not subscriptions."
"The truth is... most people don't have the funds, or don't want to spend their funds, on software."
"If you look at the top 20 consumer subscription products globally, ChatGPT is already on there, which is crazy in three and a half years. Almost all the others are media companies or big platforms like Amazon or Uber, where you have very frequent purchase behavior."
"So I'm a big believer: we have transcended the need for everyone to buy a subscription to AI, and we need to see these other business models come back."
@omooretweets
The seventh edition of our Top 100 Consumer AI Apps is here, including a new dimension this time: what consumers are actually paying for.
a16z's Elena Burger sits down with Olivia Moore and Josh Elman to unpack the current state of consumer AI:
The data reveals a striking power-user economy. Only a small share of consumers currently pay for AI, but among those who do, spending is heavily concentrated at the top. Olivia and Josh discuss why developers, creators, and other power users dominate spending today, and why subscriptions may not be the business model that ultimately brings consumer AI to everyone.
They also dig into the rise of personal agents, the different trajectories of ChatGPT, Claude, and Gemini, how ads could reshape AI economics, and the enormous amount of consumer white space still left to build, from shopping and entertainment to social, dating, and marketplaces.
00:56 Inside the 7th edition
05:55 Muse and Instinct's launch numbers
09:14 The privacy wall for personal agents
14:39 The AI spending power law
20:20 Why subscription is the wrong model
23:51 OpenAI's $1B ad run rate
29:21 ChatGPT vs Claude vs Gemini
32:30 Where creative tools still win
45:12 The white space: dating, shopping, entertainment
YouTube: youtu.be/aCvrzhwUxg0
@omooretweets @JoshElman @VirtualElena
i'm in bucharest this week for @how_to_web .
thursday 15:20, startup stage: "enabling ai sales in enterprise" what actually closes deals when the buyer wants the upside without the risk.
me and @carlosaddsub are around all three days with midbrain. if you're building with agents, selling into enterprise, or investing in either - come find us. dm and we'll grab 20 minutes.
always-on should describe the agent, not me.
i'd keep the work alive and stop the check-ins. less reassurance, less babysitting.but silence can hide a missed deadline. i'd let those break through.
quiet isn't enough. i still need to know when i'm needed.
i'd want an agent to remember why i said no.
otherwise a rejected option returns as a fresh suggestion.
keeping every doubt adds noise. i'd keep the reason and what would change my mind.
that reason can go stale too. memory needs a way to stop being true.
Timmy Ghiurau retweeted
Some thoughts on human + agent collaboration:
1. Slack has become the default place where humans work with agents and each other. Yet many people I talk to don't actually enjoy using it. Monitoring dozens of channels and DMs is overwhelming.
2. You can of course have your agent monitor all those threads for you, but that creates a new problem. I recently gave feedback to someone at an AI lab and got a quick reply. But it turns out it was their agent that responded, not them. If agents keep multiplying in Slack channels, will it only get harder to reach the actual human? And if only the agent replies, how do I know my feedback is getting prioritized?
3. Meanwhile, Grok Bot, ChatGPT, and others are starting to look more and more like Slack as they expand from human-agent to human-human collaboration. But going back to my first point, Slack is already overwhelming to use.
4. Maybe the solution is an agent that handles 80% of your threads and escalates the other 20% to you. But I doubt it'll pick the right 20% most of the time. I personally don't like it if I know something I manually wrote is getting filtered by an agent instead of going straight to a human.
