Generative AI

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.

On February 5, we wrapped up four weeks of OpenClaw hype, reaching a conclusion that seemed obvious at the time: the tool works for personal tasks, but corporate adoption is virtually nonexistent. A month has passed. OpenClaw has surpassed React in GitHub stars, reaching 265,000 versus 228,000 for the most popular JavaScript framework in history – and that is only the beginning of what has changed.

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.

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?

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.