When was the last time you read a headline about Microsoft Copilot? Probably not recently. The media space is wall-to-wall GPT-5, Claude 4.7, Gemini Ultra. Copilot looks like the boring corporate tool that only gets written up on LinkedIn.
Now look at who is actually paying for what.
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In November 2025, Stanford data surfaced an unexpected fact: Americans were using AI more at home than at work. We unpacked that in a mysummit.school piece here – and the takeaway then was blunt: work AI was still catching up to personal AI.
Four months later, Epoch AI teamed up with Ipsos for new research – and the picture has shifted.
Every team has a ‘good enough’ model. Maybe it’s the one your company approved. Maybe it’s the one that runs locally because legal won’t let data leave the building. Maybe it’s the only option your country’s regulations allow.
In Russia, this is about to become very literal. In March 2026, the Ministry of Digital Development published a draft law on “trusted AI models.” If passed – enforcement is planned from September 2027 – government agencies and critical infrastructure will be limited to models from an approved registry. ChatGPT, Claude, and Gemini, which route data through foreign servers, could be restricted. Russian-made AI assistants would be pre-installed on devices by default.
The problem is that Russian models lag behind. In our study of 54 models, GigaChat scored 3.26 out of 5. GPT-5.4 scored 4.80. That’s a 32% gap.
If GigaChat is all Russian managers have, can smart prompting pull acceptable quality out of it?
In March 2025 at SXSW EDU, strategic foresight advisor Sinead Bovell delivered a talk on AI and the future of education. No hype, no panic. But with two studies that change how you should think about AI’s role in learning.
First: a group of students who used ChatGPT without restrictions scored 17% worse than the control group working from a textbook. Second: a different group, where AI was deployed within a fully redesigned instructional system, outperformed a traditional lecture by a factor of two.
Same tool. Opposite outcomes. The difference is in the approach.
A Gallup and Walton Family Foundation survey (2024–2025, representative sample of US teachers) produced an impressive number: teachers who regularly use AI save an average of 5.9 hours per week – the equivalent of six full work weeks per school year. Sounds like a solved problem.
But a parallel Royal Society of Chemistry survey (2024, UK) paints a different picture: 44% of teachers tried AI, yet only 3% reported a real reduction in workload. A maths teacher from Ireland explained the gap more precisely than any statistic: “AI generates worksheets quickly, but they need thorough checking – and the time savings turn out smaller than expected.”
Who is right? We previously examined the AI crisis in education from the student side – 86% of students use AI, yet critical thinking is declining. Now – the instructor side. Over the past two years, enough experimental data has accumulated to answer this question with numbers, not opinions.
A year and a half ago, I wrote a note on my personal blog about something I was noticing in my colleagues’ work and in my own: the more you trust AI, the less often you ask yourself “is this actually right?” I was drawing on a Microsoft study at the time – it showed that trust in AI suppresses critical evaluation of the answers it produces. The argument felt strong to me, but it had an obvious flaw: correlation, not causation.
In February 2026, Anthropic researchers Judy Shen and Alex Tamkin published an experiment that closed that gap. Randomized control. Concrete data. And a conclusion that, I think, most people who’ve read about it have misunderstood.
Because this isn’t a story about AI making us dumber. It’s a story about how exactly we use it.
On 11 February 2026, at a government meeting, President Tokayev publicly criticised KazLLM. The model, launched with great fanfare in December 2024, has just 600,000 users – 3% of the country’s population. For comparison: 2.6 million people in Kazakhstan use ChatGPT. The president was blunt: KazLLM “cannot compete with ChatGPT.”
This statement cuts to the heart of the matter. Why does Kazakhstan need its own language model if global solutions work better? And if sovereign AI is necessary – why is it losing?
The answer is more complicated than it seems. Because KazLLM is not “Kazakhstan’s ChatGPT.” It’s a fundamentally different tool with a different mission. Comparing them is like comparing a national power plant with an imported household appliance.
In February 2026, the investment research newsletter Citrini Research published a scenario that flips the usual logic on its head. Bears typically predict that AI will underdeliver. Citrini asks a different question: what if AI delivers on every promise – and that’s exactly what causes the problem?
Their piece «The 2028 Global Intelligence Crisis» is a fictional memo dated June 2028. Not a forecast, but a stress test: what happens to the economy if machine intelligence really does replace white-collar workers as fast as the developers claim?
“Why would I need your course when I can just ask ChatGPT?”
This question shows up in the comments under every post about AI education. The logic is bulletproof: the internet is free, ChatGPT will explain anything, YouTube is overflowing with tutorials. Why pay for a course when all the information is already out there?
It’s a great question. The answer is not the one people expect to hear.
Because between “having access to information” and “knowing how to work with it” – there is a chasm. Access to medical textbooks doesn’t make you a doctor. Owning a kitchen knife doesn’t make you a chef. And the ability to ask ChatGPT a question doesn’t make you an AI specialist. As MIT’s research on choice architecture shows, a specialist’s real strength lies not in having the tool, but in the ability to critically evaluate what the tool suggests.
And this isn’t just a neat metaphor – it’s a research-confirmed fact.
Companies are worried about getting employees to use AI. The promise is seductive: AI will handle the drudgery – document drafts, information summarisation, code debugging – freeing up time for higher-value work.
But are companies ready for what happens if they actually succeed?
Researchers at Stanford conducted an 8-month observational study of roughly 200 employees at an American tech company that had rolled out generative AI. The company didn’t mandate AI use – it simply provided corporate subscriptions to commercial tools. Employees decided for themselves whether to adopt them.
The result was paradoxical. AI didn’t reduce work. It intensified it. Workers moved faster, took on more tasks, spread their work across more hours in the day – often without any explicit external pressure. AI made “doing more” possible, accessible, and in many cases internally rewarding.
Strikingly, the same pattern shows up in other research. Microsoft found that 62% of product managers use Gen AI daily, yet while 81% say AI saves time, 56% deny that effort has decreased. A paradox? No – a pattern.