I'm hiring Transformation Partners at The Thinking Company. We're building a company that combines our own software, Runstate, with services delivered by small teams of senior people and our AI agents. You'll use both the product and the agents daily, taking a CEO's business problem through to a deployed result. We work with CEOs, boards and private equity operating partners on how AI changes the economics of their businesses. You'll start with our existing client relationships and take responsibility for winning and growing your own. Building TTC is part of the job. I'm looking for someone who has led whole engagements at a top consulting firm and can explain the decisions they made. You should understand technology well enough to challenge technical choices, and use AI enough to know where it fails. Warsaw-first, with Poland-based hybrid possible for an exceptional candidate. Fixed pay plus variable compensation for work delivered and sold. DM me or reach out to us (link in the comments) with a short note on why this move makes sense and an example of work you personally owned.

Watermarking AI-generated text sounds like transparency at first, but it's just measuring the wrong thing. AI involvement is not the same as AI authorship. A watermark may appear when a model only proofread, translated, summarized or reformatted human-origin work. At the same time, AI-generated text may lose its detectable mark after heavy editing, paraphrasing or being mixed with other writing. The mark can signal that a tool processed the content but it cannot establish who created the work. Provenance needs two layers: origination and transformation. Who formed the idea, made the consequential decisions and accepts responsibility for the result? Which tools later proofread, translated, formatted or otherwise changed it? The first layer matters for authorship and responsibility and the second matters for traceability. Collapsing them into one AI-generated label confuses the toolchain with the creator and author. The more common AI assistance becomes, the less informative a label based only on AI involvement will be. But also what Ben Thompson said: "What the E.U. is doing is stealing the last thing humans have — creation — and demanding it be bundled with substantiation, effectively giving AI the credit. That the human gave AI the text to proofread — or even the prompt to generate writing — doesn’t matter to the bureaucrats. Everyone is worried about being replaced by AI; the E.U. is mandating it."

An open-weight model is one a company can download and run outside the provider’s service. That sounds like control, but the open versus closed labels tells us little about who actually controls what. A company can run an open model and still be locked in. If a vendor owns the data pipeline, evaluation set, workflow traces and improvement loop, the company has access to the model but does not own the learning around it. The reverse can also be true. A closed model may be replaceable if the company owns those layers and can move them to another provider. Models will keep getting cheaper. A company’s accumulated knowledge of what works in its own business is much harder to reproduce. What matters is what remains with the company after each deployment. If the data, evaluations and improvement loop stay with the vendor, so does the advantage.

The unit of management was the person. SaaS made it the seat. AI makes it the task. For a century, companies paid for their smartest people's best hours by buying all their hours. The analyst who cracks the pricing model on Tuesday morning spends the rest of the week in meetings and status updates. You can't hire another Tuesday morning; you hire the person. With agents you can. A model holds your context and customer history, and you summon it one task at a time: routine work to a small model, the hard problem to a frontier model. That turns a hiring plan into a capital allocation plan. Intern, PhD, small model, frontier model, decided task by task. Most companies can't make those decisions yet. They know their salary bands and meeting cadence. Almost none knows which tasks consume the intelligence they pay for.

Two Poles and a bet that voice could change everything. The @ElevenLabs butterfly effect will be seen for years. The homecoming.

Time to build.

The company org chart itself is becoming a temporary structure, compressed and redrawn faster than any management theory can keep up with. New newsletter is out.

Let’s do this!

For founders who dream harder. pucek.capital/

Yesterday we launched @ShotgunCLI on @ProductHunt and got to the top 10. Shotgun is the context for your software. It's a codebase-aware spec engine that: 🔹 Reads your repo → creates specs for what's next and best 🔹 Orchestrates agents (Researcher, Architect, PM, Spec Writer) for software research and understanding 🔹 Runs local-first (your keys, your data) , CLI 🔹 Exports to @cursor_ai , @claudeai , @lovable The posse's just getting started! If you missed the launch, we're still on Product Hunt: producthunt.com/products/sho…