Owning, not renting your AI, is the only way to secure access.
That's why back in March, Cursor built its own coding model.
It was not just about the quality and cost gains; Cursor claimed better quality than GPT/Opus at 1/10 of the cost.
It was also about AI sovereignty.
We’re sorry to see that OpenAI put out a note saying they plan to block Cursor users from accessing OpenAI models in three months.
OpenAI models serve about 5% of Cursor user traffic, and we’re speaking with the OpenAI team to resolve this.
Cursor was one of the very first users of OpenAI, we’ve worked closely with their team for years, and we’ve trusted their platform to be neutral infrastructure for our business.
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JUST IN: Nvidia agreed to buy Hugging Face for $12.9B.
That is at 86x the annualized revenue ($150M).
Why so high? This strategic for NVIDIA.
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Enterprise AI is in a wildly paradoxical state 🤔, and we’re reaching the inflection point that will resolve it. 💥
Enterprises want to differentiate with AI, yet rent the same intelligence as their competitors.
Their workflows and expertise are highly specialized, yet they rely on generic models built to be good at everything.
They worry about AI costs, yet pay premium prices for massive models where only a fraction (1%) of the intelligence is relevant to their task. 💸
And they demand control and sovereignty, yet rent the intelligence becoming core to their business.
This is not a sustainable equilibrium.
The next era of enterprise AI is specialized intelligence companies build, own, and compound. And we are at the inflection point of this transition.
That’s the bet we made when we started @oumi_ai two years ago.
Today we’re closing the loop: Oumi can now not only automatically build your specialized AI models, but also deploy them into production, learn from their production experience, and continuously improve them.
The intelligence that your business runs on, becomes your differentiator. Your compounding advantage.
The winners of the next AI era will turn their own data, expertise, and experience into specialized intelligence that nobody else can rent.
Don’t rent your AI. Build it. Own it. Compound it.
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AI ownership alone isn’t enough.
The bigger opportunity is compounding intelligence.
Every production interaction creates something valuable:
Failures.
Edge cases.
Corrections.
Expert feedback.
New proprietary data.
Today, most of that experience disappears while the underlying model stays essentially static.
That’s backwards.
Production should be a learning loop.
Your operations create experience → experience improves the model → the better model creates more experience.
That’s how AI becomes a compounding asset.
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My prediction: 🔮
In a year, renting all of your intelligence from a frontier lab, or hiring someone else to build your proprietary AI for you, will look as strange as outsourcing all of your proprietary software.
The frontier labs will build extraordinary general intelligence.
Enterprises will build the intelligence unique to them.
And the biggest competitive advantages will come from intelligence your competitors cannot simply rent.
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Manos Koukoumidis retweeted
Open-weight models put American AI innovation directly into the hands of developers, researchers, and startups. This is exactly the kind of leadership @POTUS's AI Action Plan calls for, and we're excited to see labs across the U.S. ecosystem continuing to invest in open, accessible AI.
Today we're also opening the weights for Muse Glimmer, a great 30B parameter dense model that can run locally. Soon we'll also release the weights for Muse Spark 1.2, our latest foundation model. Meta is a strong supporter of open source and I'm proud of these releases. Congrats to @alexandr_wang and the MSL team for all your great work on these models.
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The biggest strategic mistake in AI would be to let the future of intelligence become proprietary.
That's why we at @oumi_ai signed the letter in support of open-weight AI.
We've long believed that AI's future won't be defined by a handful of closed, generic models. It will be defined by an open ecosystem where organizations can build, own, and continuously improve specialized intelligence tailored to their needs.
Open weights don't just increase access. They enable competition, sovereignty, lower costs, and innovation at every layer of the AI stack—giving enterprises the ability to own the intelligence that differentiates them instead of renting it.
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We are at the inflection point of the biggest shift in enterprise AI. 📈
Since Nov 2022, the CEOs of frontier AI labs had been working hard to brainwash 🧠🔨 enterprise leaders and C-level execs that they need to use the AI they offer them and that was the only way for enterprises to “avoid falling behind.” As a result, telling enterprises to build and own their specialized AI instead of renting generic AI got blank stares.
On the other side, neo clouds all advocated to their customers about the use of (generic) open models. To get high “frontier” quality, you need to use the large (generic) models that we offer you, they claimed. This served them well; the bigger the model the customer used, the more they earned. And to be “competitive” they worked hard to make their inference 5% or 10% faster than their competitors.
Both strategies were shortsighted. They missed the inevitable; enterprise maturity would come and enterprise leaders would sooner or later realize that “Specialized Intelligence” they own is the only viable strategy; higher quality/differentiation, full control and 5x - 10x cheaper, not just 5% or 10%.
This month the CEO of Microsoft said renting generic intelligence "makes no economic sense." He made it clear that all enterprises should build, own and compound their own specialized intelligence.
The bet we made when we left Google to start Oumi is now the consensus. When neclouds are now also arguing for “Specialized Intelligence” and even big tech CEOs (Satya Nadella, Alex Karp) start making your argument for you, you're not early anymore. You're right.
Good week to be building.
Join the launch → events.oumi.ai/register/summ…
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"It can't be that there are only two companies in the world with token capital, and everybody else is renting it. It makes no economic sense." (Satya Nadella, via CNBC)
He's describing a trap. When you rent intelligence from two or three frontier labs, every production run makes their model smarter and yours smarter by zero. Costs scale with usage. The learning walks out the door. We call it the Rented Intelligence Trap.
The way out is Compounding AI: a model you own that gets smarter about your business every time it runs. The token capital stays yours.
When the CEO of Microsoft is making your case on the record, it's not a trend anymore. It's the shift.
See how in 5 minutes → oumi.ai/demos/product-overvi…
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*The future isn't rented intelligence. It's specialized intelligence you own.*
Enterprises are coming to the realization that they need to own and control their AI, the technology that will increasingly define their competitive advantage. Renting that intelligence quickly proving not to be a viable strategy.
Last week, Satya Nadella (Microsoft CEO) made the case for enterprise-owned AI. This week, Palantir CEO Alex Karp argues that frontier AI providers alone cannot be the solution for regulated industries—and, by extension, many other enterprise use cases.
The direction is becoming increasingly clear: enterprises will use frontier models where they make sense and for quick prototyping, but they'll increasingly build and own specialized AI for the workflows that matter most.
lnkd.in/dc-sq-pX
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Why should you own AI rather than rent it? This Wednesday, July 1, @stefan_webb is giving a live webinar not to be missed!
There'll be 2 live model building demos plus a bonus interview with @MuraliMinnah from @wiredinfo, who put an SLM for word-sense disambiguation built with @Oumi_PBC into production, slashing costs, latency, while improving accuracy and other metrics!
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Enterprises using Opus 4.8 to do classification, entity extraction, document analysis or pretty much 90% of their AI use cases....
Wildly wasteful at enterprise scale.
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Manos Koukoumidis retweeted
Η startup του @Koukoumidis βοήθησε τους New York Times να εντοπίσουν τα τυφλά σημεία των AI Overviews της Google. Ήταν πολλά περισσότερα απ’ότι φανταζόμαστε.
Όμως το μεγαλύτερο στοίχημα της Oumi βρίσκεται αλλού: στην αυτοματοποίηση της ίδιας της μηχανικής της τεχνητής νοημοσύνης με στόχο πιο ακριβή, πιο οικονομικά και πιο προσαρμοσμένα μοντέλα για κάθε οργανισμό.
Legendary stuff από έναν από τους κορυφαίους έλληνες στο ΑΙ σήμερα.
wired.com.gr/article/oumi-to…
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Prompts for a generic model that everyone shares are not a moat.
The moat is specialized intelligence:
- trained on your workflows
- adapted to your edge cases
- continuously improving from production usage
- fully owned and controlled by you
OpenAI announced that it is deprecating fine-tuning, taking away the only true way their customers had to build their moat. Take your data and bring it to Oumi. In just hours you can build, evaluate and deploy your specialized model.
The future enterprise AI stack is:
your business → your intelligence → your competitive advantage
This is why we built Oumi to make enterprise-owned intelligence operational in hours instead of months.
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Manos Koukoumidis retweeted
@Oumi_PBC OSS just dropped a local MCP for building custom ML workflows in minutes!
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I just met @AndrewYNg at AI Dev—and something about the interaction stuck with me.
Despite everything he’s achieved, he was humble and helpful connecting me with his team in the same pleasant, calm, and soothing voice he is also known for.
Being here at AI Dev, you can feel that shift. The conversations aren’t just hype—they’re getting more practical, more grounded.
If you’re around, come by booth #511—we’ve been demoing Oumi’s VibeML and the response has been great.
Also, tomorrow at 5pm, Stefan and I are presenting:
“VibeML: Build your specialized AI model from a prompt – in hours, not months”
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