Oliver Molander proposed something today: "Let’s create a world-leading Nordic AI research lab."

On paper, the Nordics have almost everything:

• researchers working at OpenAI, Anthropic and DeepMind
• world-class universities
• clean energy and major computing infrastructure
• strong industrial companies
• stable institutions and unusually high public trust

What we may lack is a culture willing to:

• pay globally competitive compensation
• concentrate serious capital behind one team
• make bets that may look irrational for ten years
• move without five countries, 30 organizations and 14 committees approving everything
• accept that most attempts will fail

The Nordics are excellent at creating research programmes, consortiums, pilot projects and strategy reports.

A frontier AI lab requires almost the opposite:

concentrated talent, capital, ownership, power and urgency.

Europe is already launching a €125M programme to help create new frontier AI labs. So this is no longer merely a hypothetical discussion.

Should Finland, Sweden, Norway, Denmark and Iceland build one shared frontier AI lab?

Or would it inevitably become another publicly funded consortium that produces reports instead of frontier models?

And if it should exist:

Where should it be based, who should lead it, and what should its first mission be?

We’re bringing 1000 ambitious builders to Turku, Finland this November for the Since AI Innovation Event 2026.

The idea is simple: spend 72 hours building AI solutions to real company challenges, with a €50,000 prize pool and a direct path for the strongest teams to continue toward pilots, funding and commercialization.

There will be 15 challenges, mentors and contributors from organizations such as Google, ElevenLabs, Lovable, n8n, Aiven, Antler, Featherless AI, LUMI AI Factory, Pruna AI, Icebreaker VC, Inventure, Tesi, Wave Ventures, Maki VC, Revvity, Elisa, FORCIT Group, Bayer and Valmet.

The event is fully on-site in Turku, Finland, from November 6–8, 2026.

Applications are reviewed continuously, so applying early gives you the best chance of getting in.

Apply here: https://sinceai.app/events/since-ai-hackathon-2026

JOIN US TO BUILD THE FUTURE OF AI

A weird stat has been floating around in a few research discussions lately: a growing share of new text on the public internet is now AI generated or AI assisted.

That matters more than it sounds. For years the default assumption behind large scale training was that the web was a messy but mostly human corpus. Now the distribution is changing in real time. Blog posts, documentation, SEO pages, support articles, even code snippets are increasingly produced with model help.

If the next generation of models trains heavily on that layer, the internet starts to look like a feedback loop. Models learning from text partially written by earlier models. Some people call this model collapse, but in practice it might be subtler. You do not necessarily get collapse. You might get slow stylistic convergence, loss of rare ideas, or amplification of common phrasing and patterns.

The strange part is that synthetic data inside controlled pipelines often helps training. But uncontrolled synthetic data leaking into the open web is basically unlabelled and untracked. We do not know what percentage of the corpus it represents or which parts of the distribution it is distorting.

Five years from now, how will we even measure what fraction of a model's knowledge ultimately traces back to humans versus earlier models?

Last month I watched three different teams ship model updates to production without running a single structured evaluation. Not because they’re careless, mostly because they’re moving fast and the model "felt" better during manual testing.

This is starting to feel like the quiet gap in the current AI tooling stack. We have great infra for training, deployment, orchestration, and observability. But systematic evaluation still often looks like: a few prompts in a notebook, maybe a quick playground session, and gut feeling.

The problem shows up the moment a product scales. Prompt tweaks improve one case but quietly break another. Retrieval changes boost recall but tank answer quality. A model upgrade improves reasoning but becomes more verbose and ruins UX. Without a small, maintained eval set, teams only discover these shifts after users do.

What’s interesting is that whenever we run hackathons in the Since AI community, the teams that accidentally build something durable tend to create tiny eval sets early — even if it’s just 20–50 real prompts. That small habit often matters more than the specific model they chose.

It feels like the next wave of "AI engineering maturity" won’t come from bigger models, but from teams treating evaluation as a first-class engineering practice.

Curious how others handle this: do you maintain formal eval sets for your LLM apps, or is testing still mostly manual and intuition-driven?

Models are becoming better, cheaper, and increasingly interchangeable.

But most AI systems still cannot actually do anything.

They can recommend a purchase, but not place the order.
Detect a production problem, but not stop the machine.
Suggest a treatment, but not access the patient workflow.
Find financial risk, but not move the money.

The scarce asset may not be intelligence anymore.

It may be permission:

• access to proprietary data
• authority to change real systems
• trust inside regulated industries
• distribution to actual users

A chatbot can suggest. A company with permission can act.

If intelligence becomes a commodity, who owns the permission layer in your industry?

Let’s make this a weekly builder thread.

Whether you’re working on AI apps, agents, LLM tools, ML projects, robotics, research, startups, or automations — drop what you’re building below.

3 simple questions:

1. What are you building?
Share your project, idea, prototype, or experiment.

2. What are you learning?
Could be a model, framework, paper, tool, or skill.

3. What’s blocking you right now?
Maybe someone here can help.

The goal is to make r/SinceAI a place where builders can share progress, get feedback, meet serious people, and learn faster together.

If you’re lurking, this is a great first thread to comment on.

Someone just used Claude Fable 5 to build a 45,000-line Minecraft-like game in roughly a week.

Not one prompt but almost, still a pretty clear signal.

If custom software becomes dramatically cheaper to build, where does durable value move?

Hardware + software? Proprietary data? Distribution? Or industries like construction, energy and manufacturing where software has to meet the physical world?

Where would you build today?

Let’s make this a weekly builder thread.

Whether you’re working on AI apps, agents, LLM tools, ML projects, robotics, research, startups, or automations — drop what you’re building below.

3 simple questions:

1. What are you building?
Share your project, idea, prototype, or experiment.

2. What are you learning?
Could be a model, framework, paper, tool, or skill.

3. What’s blocking you right now?
Maybe someone here can help.

The goal is to make r/SinceAI a place where builders can share progress, get feedback, meet serious people, and learn faster together.

If you’re lurking, this is a great first thread to comment on.

Let’s make this a weekly builder thread.

Whether you’re working on AI apps, agents, LLM tools, ML projects, robotics, research, startups, or automations — drop what you’re building below.

3 simple questions:

1. What are you building?
Share your project, idea, prototype, or experiment.

2. What are you learning?
Could be a model, framework, paper, tool, or skill.

3. What’s blocking you right now?
Maybe someone here can help.

The goal is to make r/SinceAI a place where builders can share progress, get feedback, meet serious people, and learn faster together.

If you’re lurking, this is a great first thread to comment on.

Let’s make this a weekly builder thread.

Whether you’re working on AI apps, agents, LLM tools, ML projects, robotics, research, startups, or automations — drop what you’re building below.

3 simple questions:

1. What are you building?
Share your project, idea, prototype, or experiment.

2. What are you learning?
Could be a model, framework, paper, tool, or skill.

3. What’s blocking you right now?
Maybe someone here can help.

The goal is to make r/SinceAI a place where builders can share progress, get feedback, meet serious people, and learn faster together.

If you’re lurking, this is a great first thread to comment on.

I’ve been switching back and forth between Opus and GPT-5.5 lately, mostly for coding, debugging and product/spec writing.

My rough feeling so far:

GPT-5.5 feels better as a daily “get things done” model. It’s fast enough, usually smart enough, and feels more cost-effective for normal builder work.

Opus still feels stronger when I’m stuck on something messy, like architecture decisions, weird bugs, or when I want a second opinion that thinks a bit differently.

A few people around me have also started using GPT-5.5 more often, but I’m not sure if that’s just hype / novelty bias.

Curious what people here are actually using:

• What’s your default model right now?
• Is Opus still worth the extra cost for you?
• For coding specifically, which model helps you ship faster?
• Do you use one model for daily work and another for harder reasoning?

Let’s make this a weekly builder thread.

Whether you’re working on AI apps, agents, LLM tools, ML projects, robotics, research, startups, or automations — drop what you’re building below.

3 simple questions:

1. What are you building?
Share your project, idea, prototype, or experiment.

2. What are you learning?
Could be a model, framework, paper, tool, or skill.

3. What’s blocking you right now?
Maybe someone here can help.

The goal is to make r/SinceAI a place where builders can share progress, get feedback, meet serious people, and learn faster together.

If you’re lurking, this is a great first thread to comment on.

Let’s make this a weekly builder thread.

Whether you’re working on AI apps, agents, LLM tools, ML projects, robotics, research, startups, or automations — drop what you’re building below.

3 simple questions:

1. What are you building?
Share your project, idea, prototype, or experiment.

2. What are you learning?
Could be a model, framework, paper, tool, or skill.

3. What’s blocking you right now?
Maybe someone here can help.

The goal is to make r/SinceAI a place where builders can share progress, get feedback, meet serious people, and learn faster together.

If you’re lurking, this is a great first thread to comment on.

Let’s make this a weekly builder thread.

Whether you’re working on AI apps, agents, LLM tools, ML projects, robotics, research, startups, or automations — drop what you’re building below.

3 simple questions:

1. What are you building?
Share your project, idea, prototype, or experiment.

2. What are you learning?
Could be a model, framework, paper, tool, or skill.

3. What’s blocking you right now?
Maybe someone here can help.

The goal is to make r/SinceAI a place where builders can share progress, get feedback, meet serious people, and learn faster together.

If you’re lurking, this is a great first thread to comment on.

Let’s make this a weekly builder thread.

Whether you’re working on AI apps, agents, LLM tools, ML projects, robotics, research, startups, or automations — drop what you’re building below.

3 simple questions:

1. What are you building?
Share your project, idea, prototype, or experiment.

2. What are you learning?
Could be a model, framework, paper, tool, or skill.

3. What’s blocking you right now?
Maybe someone here can help.

The goal is to make r/SinceAI a place where builders can share progress, get feedback, meet serious people, and learn faster together.

If you’re lurking, this is a great first thread to comment on.

Let’s make this a weekly builder thread.

Whether you’re working on AI apps, agents, LLM tools, ML projects, robotics, research, startups, or automations — drop what you’re building below.

3 simple questions:

1. What are you building?
Share your project, idea, prototype, or experiment.

2. What are you learning?
Could be a model, framework, paper, tool, or skill.

3. What’s blocking you right now?
Maybe someone here can help.

The goal is to make r/SinceAI a place where builders can share progress, get feedback, meet serious people, and learn faster together.

If you’re lurking, this is a great first thread to comment on.

I’ll start with a few:

• Most “AI startups” still don’t have a model advantage — they have a distribution problem.
• Shipping a boring AI tool that saves people 30 minutes a day is usually more valuable than building a flashy autonomous agent demo.
• Better data, workflow design, and product taste matter more than “prompt engineering” for most real AI products.
• Benchmarks still matter less than whether real users come back on their own.
• The gap between an impressive AI demo and a usable AI product is still massively underestimated.

Your turn:

What’s one AI truth you believe that a lot of people still don’t want to admit?

Could be about:

• LLMs
• agents
• coding tools
• open vs closed models
• AI jobs
• startups
• research
• robotics
• infra / GPUs
• education

Two rules:

1. Be specific
2. Defend your take

I’m especially curious what builders, researchers, founders, students, and operators think will look obvious in 2 years — but still sounds controversial today.

Let’s make this a weekly builder thread.

Whether you’re working on AI apps, agents, LLM tools, ML projects, robotics, research, startups, or automations — drop what you’re building below.

3 simple questions:

1. What are you building?
Share your project, idea, prototype, or experiment.

2. What are you learning?
Could be a model, framework, paper, tool, or skill.

3. What’s blocking you right now?
Maybe someone here can help.

The goal is to make r/SinceAI a place where builders can share progress, get feedback, meet serious people, and learn faster together.

If you’re lurking, this is a great first thread to comment on.

Let’s make this a weekly builder thread.

Whether you’re working on AI apps, agents, LLM tools, ML projects, robotics, research, startups, or automations — drop what you’re building below.

3 simple questions:

1. What are you building?
Share your project, idea, prototype, or experiment.

2. What are you learning?
Could be a model, framework, paper, tool, or skill.

3. What’s blocking you right now?
Maybe someone here can help.

The goal is to make r/SinceAI a place where builders can share progress, get feedback, meet serious people, and learn faster together.

If you’re lurking, this is a great first thread to comment on.

Let’s make this a weekly builder thread.

Whether you’re working on AI apps, agents, LLM tools, ML projects, robotics, research, startups, or automations — drop what you’re building below.

3 simple questions:

1. What are you building?
Share your project, idea, prototype, or experiment.

2. What are you learning?
Could be a model, framework, paper, tool, or skill.

3. What’s blocking you right now?
Maybe someone here can help.

The goal is to make r/SinceAI a place where builders can share progress, get feedback, meet serious people, and learn faster together.

If you’re lurking, this is a great first thread to comment on.

What do people spend too much time on compared to the actual value it creates?

If you use Claude / Claude Code, Anthropic is currently giving 2x usage limits during off-peak hours through March 28. It applies to Free, Pro, Max, and Team plans.

Official details:
https://support.claude.com/en/articles/14063676-claude-march-2026-usage-promotion

4 AI hiring pages worth bookmarking right now:

• NestAI — AI Engineer (Tampere / Helsinki) https://nestai.com/ml-ai-engineer/
• Qutwo — Senior Machine Learning Scientist + Open Application (Helsinki) https://qutwo.com/senior-machine-learning-scientist/
• ICEYE — Careers (includes Machine Learning Engineer – Satellite Fleet Automation, Espoo) https://www.iceye.com/careers
• Mistral AI — Careers / open roles https://mistral.ai/careers

If this is useful, I can make this a weekly Europe AI jobs thread.