🇪🇺

Europe

Stark raises $540M

Stark, the German maker of weaponized drones, raised €500M at a €3.5B valuation, with Sequoia, Founders Fund, the NATO Innovation Fund, and Peter Thiel among the backers. More than 80% of the capital goes straight into R&D and manufacturing, funding electronic warfare research and production scale-up. Founded in 2024, Stark has become the poster child for a European defense tech surge driven by governments ramping up military spend.

Nearfield Instruments raises $380M Series D

Nearfield Instruments raised $380M in a Series D at a $1.6B valuation, led by Fidelity with Temasek, QIA, Walden Catalyst, and others, the largest deeptech round in Dutch history. The Rotterdam-based TNO spinout makes metrology and inspection tools that chipmakers use to control next-generation semiconductor production, and will spend the funding on R&D, innovation, and expanded manufacturing capacity.

Alan raises $518M Series G

Alan raised €480M in a Series G at a €5.5bn valuation, led by new investor Prosus and coming just months after a €100M round at €5bn. The Paris-based health insurer crossed €800M in ARR in Q1 2026 with 1.1 million customers across France, Spain, Belgium, and Canada. The pace tells the story: European insurtech that pairs coverage with care navigation and AI health tooling is now compounding fast enough to raise twice in a single year, and Alan plans to spend the money on new countries and acquisitions.

Tissium raises $65M

Tissium raised €60M to bring its sutureless nerve repair system, the only such device cleared by the FDA, into US operating rooms. The platform uses a 3D-printed chamber and a light-cured biodegradable polymer to hold severed nerve ends together without piercing them, an approach the Paris medtech argues avoids the scarring that limits stitched repairs to roughly 54% functional recovery.

JUPUS raises $14M Series A

JUPUS raised €13M in a Series A led by Semapa Next to build AI secretarial software for law firms. The pitch tracks a structural gap: over the past three decades, newly trained legal assistants in Germany fell more than 70%, while practising lawyers tripled and admin workloads kept climbing, hitting the small and mid-sized firms that make up most of Europe’s legal market. JUPUS automates calls, intake, case prep, and drafting for more than 2,000 lawyers and 2,000 daily cases, and quadrupled ARR in 2025 as it sells the staffing shortfall as the wedge.

🇺🇸

North America

Upscale AI raises $190M Series A

Upscale AI raised $190M in a Series A-1 extension led by Premji Invest, bringing total funding to $500M at a $2B valuation, with Nvidia, Salesforce Ventures, and Temasek joining the round. The startup is building Skyhammer, a purpose-built scale-up switch ASIC to link GPUs and XPUs at high speed, positioning itself as a pure-play backend-networking supplier to hyperscalers and neoclouds.

Kalshi raises $1.0B

Kalshi is in talks to raise at a roughly $40B valuation that could close as soon as Q3, nearly doubling the $22B mark it hit just a month earlier when it raised $1B in May. The jump tracks a broader bet that prediction markets are becoming real financial infrastructure, not a novelty, with election and event contracts pulling serious volume. For founders, the signal is that capital is concentrating fast in the category leaders, and the gap between the top platform and everyone else is widening with every round.

Runpod raises $100M Series A

Runpod raised $100M in a Series A led by Summit Partners, reaching a $1B valuation and bringing total funding to $122M. The company pitches itself as an end-to-end “AI developer cloud” spanning experimentation, training, fine-tuning, and deployment, and says it has drawn over one million developers and served more than 20 billion inference requests since launch.

Aseon Labs raises $10M Seed

Aseon Labs raised $10M in a seed round led by Crane Venture Partners to build parking-space-sized pods that autonomously charge, clean, and inspect robotaxis. The startup targets deadhead miles, the empty trips robotaxis make to distant depots that account for roughly 44% of Waymo’s California fleet mileage, by scattering pods across parking lots, gas stations, and charging hubs.

Groq raises $650M

Groq raised $650M led by Disruptive, the firm run by its chairman Alex Davis, alongside hedge fund Infinitum. The round comes six months after Nvidia licensed Groq’s chip IP and poached founder Jonathan Ross and other execs in a not-acqui-hire deal. With Nvidia now shipping its own LPU inference hardware, Groq has pivoted to its neocloud business, now 13 data centers serving over five million developers, and re-staffed with a new COO, CTO, and CPO. It was last valued at $6.9B in September and didn’t disclose a new figure.

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For years, the dream career move in tech was simple: get hired by the best company you could.

You polished the CV. You ground through the interviews. You waited for the offer.

The harder the company was to get into, the more it meant.

OpenAI and Anthropic sit at the top of that pile now. The labs everyone wants in. But the smartest people I know aren’t actually applying for the roles.

They found a different door to get in.

They’re building the thing the labs can’t help but buy.

Not to scale. Not to IPO. Built, very deliberately, to get acquired by the exact companies they’d happily work for. Inside a year or two, ideally.

Sounds backwards. Then you see how the labs actually buy.

Because it turns out there’s three things a lab acquires startups for:

📊 Data

Are you sitting on something the labs can’t get anywhere else?

Proprietary, high signal, hard to replicate. The kind of dataset that takes years and real-world access to build, not a scrape.

It’s why Anthropic bought Humanloop. For the evaluation data and the enterprise eval workflows that came with it.

🏆 Talent

Have you assembled a team so good it’s worth billions to just absorb. Not a good team. A team where every single person would clear the bar at the lab on their own.

That’s rare. And genuinely hard to fake.

It’s why Anthropic bought Vercept, a two-year-old dev startup.

They’re not buying the product at that point. They’re investing in people and saving eighteen months of recruiting (and probably a significant sum of money as well).

🤝 Distribution

Do you have enterprise customers who trust you, or millions of people who’ll follow you wherever you go?

The model layer is commoditising fast. The hard part now isn’t building the AI. It’s getting it inside the messy, specific workflows companies actually run on.

It’s why OpenAI agreed to buy Tomoro, an applied AI consultancy out of Edinburgh with 150 engineers already embedded inside Tesco, Virgin Atlantic and Red Bull.

Relationships generate switching costs that no competing model can erode.

Hit two of those three and you’re a serious target. Nail all three and they come to you.

OpenAI’s acquisitions in 2026:

January 2026

• Convogo, a GenAI consulting firm.

• Torch, a healthcare app for unifying medical records.

• Crixet, a LaTeX editing and collaboration tool

February 2026

• OpenClaw, an open-source AI agent.

March 2026

• Promptfoo, a cybersecurity startup for testing AI applications.

• Astral, the open-source Python toolmaker,.

April 2026

• TBPN, the new-media company behind a popular long-form daily tech show.

• Hiro Finance, an AI personal-CFO platform for financial planning.

May 2026

• Tomoro, an applied AI consulting firm with around 150 engineers, founding piece of the new $4B Deployment Company

Anthropic’s acquisitions in 2026:

February 2026

• Vercept, an AI computer-use startup acquired to push Claude’s computer-use capabilities.

April 2026

• Coefficient Bio, a stealth biotech AI startup.

May 2026

• Stainless, the SDK generation platform that builds developer libraries for OpenAI, Google and Cloudflare.

• Andrej Karpathy (worth 20 startups).

Same underlying bet across the board: the fastest way to get a capability is to buy the people who already have it.

OpenAI made nearly as many acquisitions in the first five months of 2026 as it did in all of last year. Anthropic has told its bankers it’s gearing up to buy more.

The old career ladder said: be the best applicant.

The new one says: be the best acquisition.

This is just the tip of the iceberg. There’s a much bigger story hidden underneath.

How do founders deliberately build the data, the team and the distribution that makes a lab reach for its chequebook?

I’m turning this into a deep dive series. One company acquisition at a time, pulled apart with real examples.

Tomoro’s breakdown dropping new week. Subscribe not to miss it.

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Some of these are niche, you’d need to dig to find them organically. So whether you’re unsure what to do with your life or just need something to read on the train home - I’ve got you.

Some essays you read. Some essays read you.

These are the second kind.

1. How to be remarkable by Seth Godin

Remarkable doesn’t mean remarkable to you. It means remarkable to me. Am I going to make a remark about it? If not, then you’re average, and average is for losers.

Being noticed is not the same as being remarkable. Running down the street naked will get you noticed, but it won’t accomplish much. It’s easy to pull off a stunt, but not useful.

2. When to do what you love by Paul Graham

When you can't decide which path to take, it's almost always due to ignorance. In fact you're usually suffering from three kinds simultaneously: you don't know what makes you happy, what the various kinds of work are really like, or how well you could do them.

What if you're not sure what you want? What if you're attracted to the idea of making money and more attracted to some kinds of work than others, but neither attraction predominates? How do you break ties?

3. How to fix your entire life in 1 day by Dan Koe

Most people set a surface-level goal, hype themselves up to remain disciplined for the first few weeks, then go back to their old ways without much struggle, because they were trying to build a great life on a rotting foundation.

The bodybuilder has to grind to eat unhealthily. The CEO has to force themself to lie in bed past their alarm clock, and they hate every second of it.

4. Tail end by Tim Urban

It turns out that when I graduated from high school, I had already used up 93% of my in-person parent time. I’m now enjoying the last 5% of that time. We’re in the tail end.

When you look at that reality, you realize that despite not being at the end of your life, you may very well be nearing the end of your time with some of the most important people in your life.

5. 1000 true fans by Kevin Kelly

If you lived in any of the 2 million small towns on Earth you might be the only one in your town to crave death metal music, or get turned on by whispering, or want a left-handed fishing reel. Before the web you’d never be able to satisfy that desire.

Whatever your interests as a creator are, your 1,000 true fans are one click from you. There is nothing — no product, no idea, no desire — without a fan base on the internet.

6. No yes. Either hell yeah! or no by Derek Sivers

Every event you get invited to. Every request to start a new project. If you’re not saying ‘HELL YEAH!’ about it, say no.

We’re all busy. We’ve all taken on too much. Saying yes to less is the way out.

7. This is water by David Foster Wallace

Learning how to think really means learning how to exercise some control over how and what you think. It means being conscious and aware enough to choose what you pay attention to and to choose how you construct meaning from experience.

Because if you cannot exercise this kind of choice in adult life, you will be totally hosed.

8. No one can teach you to have conviction by Ben Kuhn

If you join a company where someone else is already making those decisions well, you’ll never get the type of practice that you need in order to build your own models and heuristics.

You’ll end up with a good, but not perfect, model of ‘what would my boss do?’ — a model that can make the 95% of easy decisions, but not the 5% of hard ones that add the most value.

9. How to be successful by Sam Altman

Most people are primarily externally driven; they do what they do because they want to impress other people.

The most successful people I know are primarily internally driven; they do what they do to impress themselves and because they feel compelled to make something happen in the world.

10. You and your research by Richard Hamming

If you do not work on an important problem, it’s unlikely you’ll do important work. It’s perfectly obvious.

The average scientist, so far as I can make out, spends almost all his time working on problems which he believes will not be important and he also doesn’t believe that they will lead to important problems.

11. Solitude and leadership by William Deresiewicz

What we have now are the greatest technocrats the world has ever seen, people who have been trained to be incredibly good at one specific thing, but who have no interest in anything beyond their area of expertise. What we don’t have are leaders.

What we don’t have, in other words, are thinkers. People who can think for themselves. People who can formulate a new direction.

12. The days are long but the decades are short by Sam Altman

Do new things often. Not only does doing new things seem to slow down the perception of time, increase happiness, and keep life interesting, but it seems to prevent people from calcifying in the ways that they think.

Aim to do something big, new, and risky every year in your personal and professional life.

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Must be something about it. So naturally I went, gave Claude Code a chance and almost build a full startup in 4 days.

Here’s how the first 3 days looked, including setup, niche recommendations and more.

Have a look at Landing Page, or Waitlist, or the Product I’m building. Synthetic data generation is not yet connected so you don’t cost me $10T in api calls, cheeky.

This is where UPDF comes in. It’s what I use to handle specs, reports, research, and PDFs without switching apps or paying Adobe’s overpriced subscription.

This AI is useful for my research agents; it summarises a 100-page investor report in seconds, translate a competitor filing, turn a dense doc into a mindmap. One license covers all your devices. Edit a contract at your desk, sign it on your phone on the way to the gym.

If your document workflow is still Adobe + 3 other tools, you’re carrying weight you don’t need.

Download UPDF and automate your document workflow today!

What’s Claude Code?

Anthropic’s command-line agent. You open your terminal in a project folder, type claude, and talk to it. It reads your entire codebase, edits files, runs commands, writes commits — no copy-pasting snippets into a chat window.

Think of it as pair programming with an AI that already has your repo open.

20 Minutes into coding I realised I need something called tmux (terminal multiplexer). Two features matter here:

1. Split panels. I’m currently running 4 agents in unison - Coder, Tester, Researcher and Designer.

2. You can Detach and walk away: I give Claude Code a big task, press Ctrl+B then D, close your laptop, go hit the gym. Come back, type tmux attach, and Claude has finished the work.

If you don’t have tmux your SSH/Wi-fi drops results in your laptop sleeping.

Quick Setup (5 minutes)

Installation:

1. npm install -g @anthropic-ai/claude-code
   or

   curl -fsSL https://claude.ai/install.sh | bash

2. brew install tmux

Starting a session:

1. tmux new -s project

2. cd your-project

3. claude

Detach anytime with Ctrl+B, D. Reattach with tmux attach -t project.

Split your terminal: Ctrl+B, % for a vertical split, Ctrl+B, “ for horizontal. Switch between panes with Ctrl+B, arrow key.

My current workflow

I open a tmux session with 4 panels. I had claude code write a script to launch all agents in the given way (iterated maybe 10 times over these 3 days to adjust it).

It includes running researcher and designer first to pick up on tasks, communications from previous sessions. 3 minutes later tester and coder start. Their backlog gets populated by the first two agents.

When I want a manual change I type something like: “Add a billing page that pulls the user’s Stripe subscription status and shows their current plan.”

Claude reads my existing auth setup, checks how I’ve structured other pages, creates the component, wires up the API call, updates the router.

20 minutes into the first coding session I realised this is not optimal and to have the guy commit like he does now.

I had to make it all more autonomous.

Current 4-Agent Setup

Let me break down what each agent actually does and why I landed on this split.

- Researcher: Its job is to read documentation, check for breaking changes in dependencies, look up best practices, and dump findings into a shared markdown file. Before I added this agent, I kept running into situations where the Coder would implement something using an outdated API or miss a better approach entirely. The Researcher always runs first.

- Designer: It reviews the existing UI, checks for consistency, and produces component specifications. It writes these specs into task files that the Coder picks up. Early on I made the mistake of letting the Coder make design decisions on the fly. The result was functional but visually inconsistent. Having a dedicated agent that only thinks about design patterns solved that.

- Coder: The workhorse. It reads task files from the Researcher and Designer, picks the highest-priority one, and implements it. It has access to the full repo, runs the dev server, and commits when a task is complete.

- Reviewer: Runs alongside the Coder. Every time a new commit lands, he pulls the changes, runs the test suite, and if something breaks, writes a bug report into the task backlog.

How They Communicate

I landed on something a dead simple shared protocol: a shared directory of markdown files.

Just files on disk that every agent can read and write. Git handles the versioning. If two agents try to write at the same time, one commit wins and the other retries — which in practice almost never happens because they’re operating on different files.

Each task file looks something like this:

id: TASK-042
priority: high
status: pending
created_by: designer
assigned_to: coder

## Add billing page
### Context

User needs to see their current Stripe subscription status.

### Requirements

- Pull subscription data from /api/billing/status
- Show current plan name, renewal date, payment method
- Follow the card layout pattern used in /settings pages
- Handle loading, error, and empty states

### References

- See /coordination/specs/billing-api-research.md
- Follow component structure in src/components/settings/

The Coder picks this up, implements it, marks the status as done, and moves on. If the Tester catches an issue, it creates a new task referencing the original.

The Launch Script

I mentioned I had Claude Code write the launch script. Here’s the logic behind it:

1. Phase 1 (t=0): Researcher and Designer start. They scan for any leftover tasks from the last session, check git history for what changed, and populate the backlog.

2. Phase 2 (t=3min): Coder and Tester start. By now there are tasks waiting. The delay is important — without it, the Coder would start with an empty backlog and either idle or pick up stale work.

Each agent gets launched in its own tmux panel with a system prompt tailored to its role. The system prompt includes:

- Which directories to read from and write to
- What its priorities are
- When to stop and wait vs. when to keep going
- How to format its outputs so other agents can parse them

The script also includes a health check — if an agent crashes or gets stuck in a loop, it restarts with context about what happened.

What Surprised Me

- The agents get better over time: It’s because the codebase grows with consistent patterns. The more code that exists in your style, the more context Claude has for matching it. By day 2, the Coder was producing components that looked like I wrote them.

- The Researcher pays for itself immediately: Originally I was sceptical about dedicating a whole agent to “just reading docs.” But the number of times it caught a deprecation or found a simpler API before the Coder went down the wrong path made it the highest-ROI agent in the setup.

- You still need to architect: The agents are excellent at implementing well-defined tasks. They’re not great at deciding what the product should be. Building redundant features without a higher endgoal in mind is coder’s motto I feel like.

What I Need to Change

- I’ve added a ‘night-agent’. I try to overflow backlog with ~100 features before I go to bed and pass it to night agent to code. Problem is he churns out after an hour. Need to fix for now.

- Researcher and Designer are rarely needed by the day 3. There’s only so much needed to research. I’ll switch to running two coders, frontend & backend + feature designer on launch

- I set up an imsg notification each time coder commits but it’s mental. Need to make it more decluttered

- Sometimes I find the agents just fucking around. Today I had a broken synthetic data generation method (the key functionality) and let the agents fix it on its own. Checked up on them 3 hours later, it was still unfixed + they added ?!64?! items on a navigation bar. HAVE YOU SEEN A PRODUCT WITH 64 ITEMS ON A SIDEBAR?! Got it down to 11.

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The last couple of months have been... let's say chaotic. Wait — chaotic? I don’t mean disordered and confusing.

The latter definition seems closer to what I’ve been trying to say. The property of a system whose behaviour is so unpredictable, as to appear random.

Emphasis on random. That’s the word.

I'll use chaos interchangeably with randomness, timing, luck, and other hidden constraints. Because that's the problem that made me write this dive.

What would my day today look like if I'd made one less misstep last year? Would anything change? Would the butterfly effect screw me over?

People keep assuming outcomes come from variety of shaping factors: skill, planning, intelligence, grit. This leaves this often overlooked variable: chaos.

It affects absolutely everything.

Where you were born. How your brain functions. What personalities your closest people had growing up. What your early ambitions became and how you chose to execute them.

If you took two random p…

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Some of you liked it, so here’s the longer version with justifications why I think these predictions are going to happen.

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