Big companies spent decades turning size into an advantage.
AI raises a more uncomfortable question: what happens when smaller companies can coordinate like giants? A thread.
Now imagine a smaller competitor with agents handling routine reconciliation, reporting, routing and internal handoffs.
It doesn't need to replicate your management structure to challenge your execution speed.
This doesn't mean large firms lose. They still have capital, trust, relationships and distribution.
But it means 'we are bigger' may stop being an answer to 'why will we win?'
Which of your advantages survives if coordination gets cheap? #Strategy
Europe could win the AI model race. And still lose the AI economy.
@arthurmensch, CEO of Mistral AI, argues that Europe must build its own frontier models to avoid technological dependence.
I respect his ambition. But I challenge the premise.
๐ง๐ต๐ฒ ๐ฟ๐ฒ๐ฎ๐น ๐๐ ๐๐ฎ๐ฟ ๐บ๐ฎ๐ ๐ป๐ผ๐ ๐ฏ๐ฒ ๐ฎ๐ฏ๐ผ๐๐ ๐บ๐ผ๐ฑ๐ฒ๐น๐ ๐ฎ๐ ๐ฎ๐น๐น.
Models are becoming increasingly interchangeable. Value is shifting toward orchestration, enterprise data, business processes and the decisions AI can actually execute.
Two companies. Same AI model.
One builds a chatbot.
The other eliminates half the steps in a critical business process.
Who wins?
Meanwhile, Europe debates sovereign models while struggling with expensive energy, fragmented markets and excessive regulation.
And what if todayโs LLM architecture turns out to be transitional?
We risk spending billions catching up in a race whose rules may change.
Sovereignty matters. But sovereignty without competitiveness is an expensive illusion.
๐๐๐ฟ๐ผ๐ฝ๐ฒ ๐ฑ๐ผ๐ฒ๐๐ปโ๐ ๐ป๐ฒ๐ฒ๐ฑ ๐๐ผ ๐ผ๐๐ป ๐ฒ๐๐ฒ๐ฟ๐ ๐น๐ฎ๐๐ฒ๐ฟ ๐ผ๐ณ ๐๐.
๐๐ ๐ป๐ฒ๐ฒ๐ฑ๐ ๐๐ผ ๐ฐ๐ฎ๐ฝ๐๐๐ฟ๐ฒ ๐บ๐ผ๐ฟ๐ฒ ๐ผ๐ณ ๐ถ๐๐ ๐ฒ๐ฐ๐ผ๐ป๐ผ๐บ๐ถ๐ฐ ๐๐ฎ๐น๐๐ฒ.
Otherwise, weโll end up with sovereign models and an economy dependent on everyone elseโs innovation.
#AI #Europe
๐๐ฏ๐ด๐ฑ๐ช๐ณ๐ฆ๐ฅ ๐ฃ๐บ ๐๐ณ๐ต๐ฉ๐ถ๐ณ ๐๐ฆ๐ฏ๐ด๐ค๐ฉโ๐ด ๐ข๐ณ๐ต๐ช๐ค๐ญ๐ฆ โ๐๐ถ๐ช๐ญ๐ฅ ๐ฐ๐ณ ๐ด๐ถ๐ณ๐ณ๐ฆ๐ฏ๐ฅ๐ฆ๐ณ.โ
I once watched someone struggle through a workshop and dismissed their thinking.
Then came a brilliant follow-up email.
I had confused presentation with judgment. AI makes that mistake even easier: polished output can hide weak thinking. #Leadership
๐๐ ๐๐ต๐ผ๐๐น๐ฑ ๐บ๐ฎ๐ธ๐ฒ ๐ฐ๐ผ๐ฟ๐ฝ๐ผ๐ฟ๐ฎ๐๐ฒ ๐ต๐ฒ๐ฎ๐ฑ๐พ๐๐ฎ๐ฟ๐๐ฒ๐ฟ๐ ๐น๐ฒ๐๐ ๐ฝ๐ผ๐๐ฒ๐ฟ๐ณ๐๐น. ๐ช๐ฒโ๐ฟ๐ฒ ๐๐๐ถ๐ป๐ด ๐ถ๐ ๐๐ผ ๐ฑ๐ผ ๐๐ต๐ฒ ๐ผ๐ฝ๐ฝ๐ผ๐๐ถ๐๐ฒ.
Watch this extraordinary artwork. A human figure breaks free from its frame.
I wish more companies would let their people do the same.
Having worked at Accenture, IBM and Deloitte, Iโve seen how differently global organizations operate.
One thing Iโve learned: global scale and local autonomy arenโt opposites. When designed well, they reinforce each other.
Yet in many corporations, someone sitting thousands of miles away has more authority than the person actually facing the customer.
Another global policy. Another approval. Another decision made without understanding the local reality.
And now weโre adding AI.
๐๐ฟ๐ฒ ๐๐ฒ ๐ฎ๐ฏ๐ผ๐๐ ๐๐ผ ๐ฎ๐๐๐ผ๐บ๐ฎ๐๐ฒ ๐๐ต๐ฒ ๐ฏ๐๐ฟ๐ฒ๐ฎ๐๐ฐ๐ฟ๐ฎ๐ฐ๐ ๐๐ฒ ๐๐ต๐ผ๐๐น๐ฑ ๐ฏ๐ฒ ๐ฒ๐น๐ถ๐บ๐ถ๐ป๐ฎ๐๐ถ๐ป๐ด?
In 1945, Friedrich Hayek explained why central planning struggles.
Knowledge is scattered across people. Much of it is local, constantly changing and never documented.
No central authority can capture it all.
Eighty years later, we risk using AI to concentrate even more decisions at the centre.
But imagine the opposite.
AI empowers local teams to understand markets, assess risks and make decisions that previously required three layers of approval.
Shared technology. Common standards. Strong governance.
But decisions made where the knowledge actually exists.
๐๐ฒ๐ฟ๐ฒโ๐ ๐๐ต๐ฒ ๐ถ๐ฟ๐ผ๐ป๐.
Weโre spending billions teaching machines to make decisions while refusing to trust our own people to make them.
๐ฆ๐๐ผ๐ฝ ๐๐๐ถ๐ป๐ด ๐๐ ๐๐ผ ๐ฟ๐ฒ๐ถ๐ป๐ณ๐ผ๐ฟ๐ฐ๐ฒ ๐๐ต๐ฒ ๐ณ๐ฟ๐ฎ๐บ๐ฒ.
๐จ๐๐ฒ ๐ถ๐ ๐๐ผ ๐น๐ฒ๐ ๐ฝ๐ฒ๐ผ๐ฝ๐น๐ฒ ๐ฏ๐ฟ๐ฒ๐ฎ๐ธ ๐ผ๐๐.
๐๐ฏ๐ด๐ฑ๐ช๐ณ๐ฆ๐ฅ ๐ฃ๐บ ๐๐ณ๐ช๐ฆ๐ฅ๐ณ๐ช๐ค๐ฉ ๐๐ข๐บ๐ฆ๐ฌโ๐ด ๐๐ฉ๐ฆ ๐๐ด๐ฆ ๐ฐ๐ง ๐๐ฏ๐ฐ๐ธ๐ญ๐ฆ๐ฅ๐จ๐ฆ ๐ช๐ฏ ๐๐ฐ๐ค๐ช๐ฆ๐ต๐บ ๐ข๐ฏ๐ฅ ๐๐ญ๐ช๐ท๐ฆ๐ณ ๐ก๐ช๐ฎ๐ฎ๐ฆ๐ณโ๐ด ๐ธ๐ณ๐ช๐ต๐ช๐ฏ๐จ.
๐๐ณ๐ต ๐ค๐ณ๐ฆ๐ฅ๐ช๐ต: ๐๐ข๐ฏ๐บ๐ข ๐๐ฐ๐ฎ๐ฆ๐ญ๐ด๐ฌ๐ข๐บ๐ข (@tanya)
#ArtificialIntelligence #Leadership #Transformation
Your AI teams are learning in weeks. Your investment committee still funds them once a year.
This may be the most expensive mismatch in your AI strategy. A thread.
Picture a team discovering a breakthrough workflow in February. They need new integration capacity in March. The budget window opens in November.
By then, another team may have built a workaroundโor a competitor may have moved.
The answer isn't unlimited AI spending. It's smaller funding tranches, explicit stop rules and decisions based on evidenceโnot annual promises.
If AI learns monthly while capital moves annually, which one is really setting your strategy? #AITransformation
๐๐บ๐ฒ๐ฟ๐ถ๐ฐ๐ฎ ๐ถ๐ ๐๐ฟ๐๐ถ๐ป๐ด ๐๐ผ ๐บ๐ฎ๐ธ๐ฒ ๐ฐ๐ฎ๐ฟ๐ ๐ณ๐น๐. ๐๐๐ฟ๐ผ๐ฝ๐ฒ ๐ถ๐ ๐ฏ๐ฟ๐ถ๐ป๐ด๐ถ๐ป๐ด ๐ฏ๐ฎ๐ฐ๐ธ ๐ญ๐ต๐ฐ๐ด.
One is experimenting with the limits of engineering. The other is reinventing nostalgia.
Different products, different purposes. But the contrast made me think about something much bigger.
Imagine a 50-year-old European man.
Married. Two children. Successful career.
Yet he still lives in his fatherโs house.
He pays most of the bills. But his father makes the decisions.
Who gets a key to the house. How the family spends its savings. Which risks are acceptable. Whether the old heating system should be replaced.
When things go wrong, his father explains why every decision was necessary.
His mother reassures everyone that there was never a better alternative.
Meanwhile, the bills keep rising. The house deteriorates. The children wonder what kind of future awaits them.
The son complains endlessly.
๐๐๐ ๐ต๐ฒ ๐ป๐ฒ๐๐ฒ๐ฟ ๐บ๐ผ๐๐ฒ๐ ๐ผ๐๐.
Because leaving means making decisions. Taking risks. Accepting failure. And losing the luxury of blaming someone else.
Does this sound familiar?
After decades working with European companies on technology and transformation, Iโve seen a similar contradiction.
We celebrate innovation. Until someone proposes something genuinely disruptive.
Then come the committees, approvals, risk assessments and requests for guarantees.
Everyone wants progress. Few want to own the consequences.
Nearly 200 years ago, Alexis de Tocqueville warned about democracies that could keep their citizens in a permanent state of childhood.
๐ฃ๐ฒ๐ฟ๐ต๐ฎ๐ฝ๐ ๐๐๐ฟ๐ผ๐ฝ๐ฒโ๐ ๐ฟ๐ฒ๐ฎ๐น ๐ฝ๐ฟ๐ผ๐ฏ๐น๐ฒ๐บ ๐ถ๐๐ปโ๐ ๐๐ต๐ฎ๐ ๐ผ๐๐ฟ ๐น๐ฒ๐ฎ๐ฑ๐ฒ๐ฟ๐ ๐๐ฟ๐ฒ๐ฎ๐ ๐๐ ๐น๐ถ๐ธ๐ฒ ๐ฐ๐ต๐ถ๐น๐ฑ๐ฟ๐ฒ๐ป.
๐๐โ๐ ๐๐ต๐ฎ๐ ๐๐ฒโ๐๐ฒ ๐ฏ๐ฒ๐ฐ๐ผ๐บ๐ฒ ๐ฐ๐ผ๐บ๐ณ๐ผ๐ฟ๐๐ฎ๐ฏ๐น๐ฒ ๐ฏ๐ฒ๐ถ๐ป๐ด ๐ผ๐ป๐ฒ.
We demand freedom, then ask governments to protect us from every consequence of exercising it.
And perhaps thatโs why the future increasingly feels like something happening elsewhere.
๐ช๐ต๐ฎ๐ ๐ถ๐ณ ๐๐๐ฟ๐ผ๐ฝ๐ฒ ๐ฑ๐ผ๐ฒ๐๐ปโ๐ ๐ป๐ฒ๐ฒ๐ฑ ๐บ๐ผ๐ฟ๐ฒ ๐ฝ๐ฟ๐ผ๐๐ฒ๐ฐ๐๐ถ๐ผ๐ป, ๐ฏ๐๐ ๐บ๐ผ๐ฟ๐ฒ ๐ฎ๐ฑ๐๐น๐๐ ๐๐ถ๐น๐น๐ถ๐ป๐ด ๐๐ผ ๐๐ฎ๐ธ๐ฒ ๐ฟ๐ฒ๐๐ฝ๐ผ๐ป๐๐ถ๐ฏ๐ถ๐น๐ถ๐๐?
#Europe #Innovation
๐๐ช๐ฅ๐ฆ๐ฐ ๐ฆ๐ฅ๐ช๐ต: @๐๐ฑ๐ณ๐ฆ๐ฆ๐ฃ๐ข๐ฃ๐บ๐ญ๐ฐ๐ฏ (๐). ๐๐ฐ๐ฐ๐ต๐ข๐จ๐ฆ: ๐๐ฆ๐ด๐ญ๐ข ๐๐ฐ๐ข๐ฅ๐ด๐ต๐ฆ๐ณ & ๐๐ช๐ต๐ณ๐ฐ๐ฆฬ๐ฏ ๐ค๐๐.
Your next AI strategy may be constrained by a data-centre decision made years ago.
Boards discuss models and agents. Far fewer ask where those systems can run, at what cost and under whose control.
That's a strategic blind spot. A thread. #AIInfrastructure
The answer isn't 'move everything on-premises' or 'put everything in the cloud.'
It's deliberate placement of workloads based on latency, economics, resilience, sovereignty and flexibility.
Hybrid-by-design beats hybrid-by-accident.
For decades, infrastructure was expected to follow business strategy.
In the AI era, infrastructure may determine which strategies are possible.
Why is that still treated as a technical conversation rather than a board decision? #Strategy
Consulting has an uncomfortable AI problem.
If clients can generate research, slides and first-draft strategies themselves, why should they keep paying us the same way?
The answer isn't 'because our prompts are better.' A thread. #Consulting
The valuable work moves closer to decisions and execution: redesigning workflows, changing incentives, building capabilities and standing behind outcomes.
A recommendation the client cannot implement is becoming harder to sell as expertise.
The real threat to consulting isn't that AI gets better at writing slides.
It's that clients start asking why they paid for slides in the first place.
Would your firm still win if every client had access to the same models? #Leadership
