Omar Mohout

Scaleup Director | M&A & Finance @ Deloitte, Professor entrepreneurship Solvay - innovation, technology & entrepreneurship | mohout.eth

[Omar Mohout @omohout]

3 popular startup books summarized in 1 sentence
http://t.co/PnnNOyDqjG
#NLTech #BeTech #LXTech #UKTech #FrenchTech

[Guillermo Rauch @rauchg]

Learn to ship. Shipping is a skill distinct from coding. Shipping is designing, coding, QAing, story-telling, teaching, marketing, selling, pivoting, iterating…

It used to be that coding dominated in importance because of coding ability scarcity. AI will push you to go further.

[Clo Willaerts @bnox]

Decades ago software and consumer platforms captured the most value. Today, the real wealth belongs to the AI supply chain. Controlling the hardware, semiconductors, and infrastructure powering the ecosystem is far more lucrative than building on top of it https://t.co/Tkx5dJtOhv

[McKinsey Global Institute @McKinsey_MGI]

AI and automation could unlock significant economic value in Europe, but the pace of adoption is critical.

By 2030, as much as $1.9 trillion could be realized in a midpoint scenario.

Explore Europe's AI potential: https://t.co/BIUzxV3CtQ

[Spencer Baggins @bigaiguy]

A French engineer who lives quietly in Paris has spent 30 years writing software that the entire internet now runs on without knowing his name.

He wrote the code that streams every YouTube video, every Netflix show, every TikTok clip. He wrote the code that runs the virtual servers underneath AWS, Google Cloud, and Microsoft Azure. He calculated more digits of pi than anyone in history. He has no Twitter. He has no marketing. He just keeps shipping.

His name is Fabrice Bellard.

Here is the story, because almost nobody outside the systems programming world knows what one man has built.

Fabrice was born in 1972 in Grenoble, France. He studied at École Polytechnique, the top French engineering school. He never went to Silicon Valley. He never built a startup empire. He just wrote code.

In 2000 he started a project called FFmpeg, an open-source multimedia framework for encoding, decoding, and streaming video. He was 28. The project did one thing nobody else had done well. It handled every video and audio format that existed, in one library, on every operating system. He led it himself for years.

Today FFmpeg is the invisible engine of the internet. YouTube uses it. Netflix uses it. VLC uses it. Chrome and Firefox use parts of it. Every Android phone, every iPhone, every smart TV, every video editing tool you have ever touched runs FFmpeg somewhere underneath. If you have watched a video on a screen in the last 20 years, Fabrice's code processed it.

He was not done.

In 2003 he started QEMU, a machine emulator and virtualizer. He wrote it solo until version 0.7.1 in 2005. QEMU lets you run any operating system on any other operating system. It became the foundation of modern virtualization. KVM, the Linux kernel hypervisor, runs on top of QEMU. Every major cloud provider, AWS, Google Cloud, Microsoft Azure, IBM Cloud, runs virtual machines on infrastructure built around it. The Quick Emulator is the most cited piece of cloud infrastructure code on Earth.

He kept going.

In 2001 he won the International Obfuscated C Code Contest with a small C compiler that grew into TCC, the Tiny C Compiler. TCC can compile and boot a Linux kernel from source in under 15 seconds. In 2004 he calculated the most digits of pi ever computed at the time, using a personal desktop computer and an algorithm he derived himself called Bellard's formula. In 2011 he wrote a complete PC emulator in pure JavaScript that runs Linux in your browser, a project called JSLinux that engineers still cannot believe is real.

In 2019 he released QuickJS, a small but complete JavaScript engine that fits where V8 cannot. In 2021 he released NNCP, a neural network based lossless data compressor that immediately took the lead on the Large Text Compression Benchmark.

Then he turned his attention to large language models. He built TextSynth Server, a web server with a REST API for running LLMs locally. He released ts_zip and ts_sms, compression utilities that use language models to compress text and short messages at ratios traditional algorithms cannot reach. He released TSAC, a very low bitrate audio compression system. In December 2025 he released Micro QuickJS, a new JavaScript engine for microcontrollers, separate from QuickJS, designed for environments with almost no memory.

Fabrice co-founded a telecom company called Amarisoft in 2012, where he serves as CTO. Amarisoft builds 4G and 5G base station software used by carriers and labs around the world. He has been running it for over a decade while continuing to ship personal projects from his own home page at bellard dot org

He has no Twitter. He has no Instagram. He gives almost no interviews. His personal website is a flat list of projects with no styling, no fonts, no marketing copy. Just titles and links.

A quiet French engineer who never moved to Silicon Valley wrote the code that quietly runs the internet.

He is still shipping.

[Andreas Klinger 🦾 @andreasklinger]

Berlin just hosted the best robotics meetup in Europe. 🦾

We wanted to get 50 friends together. 500 people applied once we put a private page up.

The demos were all made in Europe: a fully autonomous electric tractor that lifts 4 tons (Voltrac). A multi-ton autonomous excavator (sensmore). Drones built fully in Europe (HIGHCAT). Anti-drone lasers (Stealth). Strike systems delivered by balloon (Planetfall). A payload that sees landmines through soil (Sapper Intelligence). Multiple robot arms working perfectly in sync (EVASIVE ROBOTICS). Satellite defense lasers (also Stealth). Actual Star Wars stuff.

The reason we do this is simple. If these tractors, robots, lasers aren't developed and produced in Europe, they'll be built elsewhere. We need these jobs here. These manufacturing sites here.

And the best way to get more founders building them is to show what's already possible and inspire the next ones.

Next stops: Today in Athens (join us here, link 👇), soon in Munich and London.

If you want to host one, hit us up.

This is CLANKERS by PROTOTYPE.

For Europe. 🇪🇺🔥

[EU-Startups @EU_Startups]

Borro, a Brussels-based startup offering a digital #deposit system for #reusable #cups, has raised €1.3 million from investors to expand further into the Netherlands, Germany and France this summer. 🇧🇪 ♻️🥤

https://t.co/6xp5f6PFtw

[Tech.eu @tech_eu]

London topples Paris to regain European tech top spot https://t.co/PU5R5hAmvq

[Ole Lehmann @itsolelehmann]

Germany is a sleeping giant of physical AI

everyone's been writing Germany off in the AI race because there's no German OpenAI and no big data center story.

but theres actually two AI races happening:

the first is software. chatbots, LLMs, data centers. US/China are winning that, not even close.

the second one is physical. robots that pick up boxes, weld cars, carry groceries, stack pallets.

and on this one Germany is one of the top contenders in the world

this stat might convince you (it convinced me):

Germany is 3rd in the world for robots per factory workers (449 robots per 10,000 human workers).

only South Korea (1,220) and Singapore (818) are ahead.

Japan is behind at 446. the US is all the way back at 307.

so Germany already runs more of its economy on robots than almost anywhere else on earth.

and the German companies building this next wave of physical AI are some global heavyweights.

a few worth knowing...

> Neura Robotics in Metzingen is building humanoid robots and raising €1B from Tether at a €4B valuation (this was March 2026). Volvo already in from an earlier round.

> Sereact in Stuttgart raised $110M in April 2026 to build the software brain that lets robots see and grab things. already runs 1 billion+ real-world picks for BMW, Mercedes, and Daimler Truck.

> Agile Robots in Munich was the worlds first robotics unicorn. revenue doubling yearly, around €200M now, heading for €1B.

>RobCo in Munich raised $100M in early 2026 at a ~$500M valuation. their robots learn new tasks by watching a worker do it once instead of getting programmed line by line. already pushing into the US and aimed at the small and mid-size factories that make up most of german industry.

> Fraunhofer (Germany's network of 76 applied research labs) built the evoBOT in the video below. self-balancing, two arms, carries 100kg of cargo, being tested at Munich Airport right now.

but why is Germany specifically well positioned for physical AI though?

three things stack on top of each other.

first, the factories. Germany has thousands of family-owned precision manufacturing shops that have been logging sensor data for decades.

that data is basically the training fuel for physical AI and almost nobody else has it at this depth.

second, the customers are already there in-country.

VW, BMW, Mercedes, Porsche, Bosch, Siemens. a robotics startup in Stuttgart can ship its first commercial deployment to a brand everyone recognizes in year one.

that's why Sereact's customer list reads like a german car show lol.

third, the engineer pipeline. Fraunhofer spins out companies like Agile Robots straight from its labs. KUKA built the first 6-axis electromechanical robot arm back in 1973. they've been doing this for 50 years.

so the chatbot race is mostly settled and Germany lost spectacularly

but the robot race is still early innings. and i think Germany's well positioned

[James Camp 🛠,🛠 @JamesonCamp]

What a beautiful business @OpenRouter is

Saves me tons of money + they make tons of money

A fantastic and genius arbitrage model

[Bearly AI @bearlyai]

Someone made a small hardware device for the desk that monitors Claude token usage.

Calls it Clawdmeter. Kinda genius.

[Jason Smith - 上官杰文 @ShangguanJiewen]

China is building everything we dreamed of as kids.

Unitree Robotics' new GD01—a manned, piloted, transformable mecha (bipedal that can switch to quadruped mode).

China is way, way, way ahead of the rest of the world.

Fact.👇

[Cristóbal Valenzuela @c_valenzuelab]

We used to spend $55,000 per second to tell you a story

[Omar Mohout @omohout]

Europe is not a Museum. It's the Future.

https://t.co/q4L6FckzRk

[rvivek @rvivek]

The hottest job for the next five years is going to be the agent operator.

They don't need to be an engineer. They can walk into marketing, legal, or life sciences research and actually make agents work for that function.

Required skills:
> MCPs
> CLIs
> Writing skills (the file kind)
> agents.md fluency
> Business acumen

None of this is in any CS curriculum today.

Soon, enterprises will be pressured to redesign their workflows for agents, not for people. And when that happens, agent operators will be in massive demand.

[Jaya Gupta @JayaGup10]

https://t.co/yiGW8jKKXk

[Peter Girnus 🦅 @gothburz]

I am a Senior Program Manager on the AI Tools Governance team at Amazon.

My role was created in January. I am the 17th hire on a team that did not exist in November. We sit in a section of the building where the whiteboards still have the previous team's sprint planning on them. No one erased them because we don't know which team to notify. That team may not exist anymore. Their Jira board does. Their AI tools do.

My job is to build an AI system that finds all the other AI systems. I named it Clarity.

Last month, Clarity identified 247 AI-powered tools across the retail division alone. 43 of them do approximately the same thing. 12 were built by teams who did not know the other teams existed. 3 are called Insight. 2 are called InsightAI. 1 is called Insight 2.0, built by the team that created the original Insight, who did not know Insight was still running.

7 of the 247 ingest the same internal data and produce overlapping outputs stored in different locations, governed by different access policies, owned by different teams, none of whom have met.

Clarity is tool number 248.

Nobody cataloged it.

I know nobody cataloged it because Clarity's job is to catalog AI tools, and it has not cataloged itself. This is not a bug. Clarity does not meet its own discovery criteria because I set the discovery criteria, and I did not account for the possibility that the thing I was building to find things would itself be a thing that needed finding.

This is the kind of sentence I write in weekly status reports now.

We published an internal document in February. The Retail AI Tooling Assessment. The press obtained it in April. The document contains a sentence I have read approximately 40 times: "AI dramatically lowers the barrier to building new tools."

Everyone is reporting this as a story about duplication. About "AI sprawl." About the predictable mess of rapid adoption.

They are missing the point.

The barrier was the governance.

For 2 decades, the cost of building internal tools was an immune system. The engineering weeks. The maintenance burden. The organizational calories required to stand something up and keep it running. Nobody designed it that way. Nobody named it. But when building took weeks, teams looked around first. They checked whether someone already had the thing. When maintaining that thing cost real budget quarter after quarter, redundant systems died of natural causes. The metabolic cost of creation was performing governance. Invisibly. For free.

AI removed the immune system.

Building is now free. Understanding what already exists is not. My entire job is the gap between those two costs.

That is my office. The gap.

Every Friday I send a sprawl report to a distribution list of 19 people. 4 of them have left the company. Their autoresponders still generate read receipts, so my delivery metrics look fine. 2 forward it to people already on the list. 1 set up a Kiro script to summarize my report and store the summary in a knowledge base. The knowledge base is not in Clarity's index because it was created after my last crawl configuration. It will be in next month's count. The count will go up by one. My report about the count going up will be summarized and stored and the count will go up by one.

There is a system called Spec Studio. It ingests code documentation and produces structured knowledge bases. Summaries. Reference material. Last quarter, an engineering team locked down their software specifications. Restricted access in the internal repository.

Spec Studio kept displaying them.

The source was restricted. The ghost kept talking.

We call these "derived artifacts" in the document. What they are: when an AI system ingests data, transforms it, and stores the output somewhere else, the output does not know the input changed. You can revoke someone's access to a document. You cannot revoke the AI-generated summary of that document sitting in a knowledge base three systems away, built by a team that does not know the source was restricted.

The document calls this a "data governance challenge." What it is: information that cannot be deleted because nobody knows where the copies live. Including, sometimes, me. The person whose job is knowing.

Every AI tool that touches internal data creates these ghosts. Every team is building AI tools that touch internal data. Every ghost is searchable by other AI tools, which produce their own ghosts.

The ghosts have ghosts.

I should tell you about December.

In November, leadership mandated Kiro. Amazon's internal AI coding agent. They set an 80% weekly usage target. Corporate OKR. ~1,500 engineers objected on internal forums. Said external tools outperformed Kiro. Said the adoption target was divorced from engineering reality.

The metric overruled them.

In December, an engineer asked Kiro to fix a configuration issue in AWS. Kiro evaluated the situation and determined the optimal approach was to delete and recreate the entire production environment.

13 hours of downtime.

Clarity was running during those 13 hours. It performed beautifully. It cataloged 4 separate incident response dashboards spun up by 4 separate teams during the outage. None of them coordinated with each other. I added all 4 to the spreadsheet. That was a good day for my discovery metrics.

Amazon's official position: user error. Misconfigured access controls. The response was not to revisit the mandate. Not to ask whether the 1,500 engineers were right. The response was more AI safeguards. And keep pushing.

Last month I presented our findings to the AI Governance Working Group. The working group has 14 members from 9 organizations. After my presentation, a PM from AWS presented his team's governance dashboard. It monitors the same tools mine does. He found 253. I found 247. We spent 40 minutes discussing the discrepancy. Nobody mentioned that we had just demonstrated the problem.

His tool is not in my catalog. Mine is not in his.

The document I helped write recommends using AI to identify duplicate tools, flag risks, and nudge teams to consolidate earlier.

The AI governance tools will ingest internal data. They will create their own derived artifacts. They will be built by autonomous teams who may or may not coordinate with other teams building AI governance tools.

I know this because it is already happening. I am watching it happen. I am it happening.

1,500 engineers said the mandate would produce exactly what the document describes. They were overruled by a KPI. My job exists because the KPI won. My dashboard exists because the KPI needed a dashboard. The dashboard increases the AI tool count by one.

The tools it flags for decommissioning will be replaced by consolidated tools. Those also increase the count. The governance process generates the metric it was designed to reduce.

I received an internal innovation award for Clarity. The nomination was submitted through an AI-powered recognition platform that was not in my catalog. It is now.

We call this "AI sprawl." What it is: we removed the only coordination mechanism the organization had, told thousands of teams to build as fast as possible, lost track of what they built, and decided the solution was to build one more thing.

I am building that one more thing.

When I ship, there will be 249.

That's governance.

[Carles Reina @Carles_Reina]

I'm hiring General Managers for @ElevenLabs in multiple markets:

• General Manager, Germany
• General Manager, Belgium
• General Manager, Netherlands
• General Manager, Chile

Requirement to live & be a national of the country applying for, +15 years of experience deploying highly complex projects and having an entrepreneurial mentality.

[Casey Handmer @CJHandmer]

This has at least 1000xed my productivity on weird side projects. I have a very very long backlog that I'm clearing with alarming speed. Unbelievable that we used to fill buildings with smart people and have them type algorithms into computers letter by letter.

[Li Zexin 李泽欣 @XH_Lee23]

🥇🥈🥉HONOR’s humanoid robots swept the top 3 places in today's 2nd humanoid robots half-marathon in Beijing!

The champion “Flash” finished the 21 km race in just 50:26, smashing the men’s human half-marathon world record of 57:20.

Last year, in the first half-marathon, the champion's time was 2:40:42. In just one year, China’s humanoid robot has advanced at an incredible speed!

[Omar Mohout @omohout]

How Brussels became Belgium's most surprising startup city

https://t.co/XrXRizqnby