We conducted a large-scale study: 52 models, evaluations from two independent LLM judges, across 8 categories of management tasks. This is the most comprehensive Russian-language AI ranking for managers available today.
The question remains the same: which AI actually works for a manager in Russia – without VPN, without workarounds?
Read more
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.
Thousands of managers google “what is AI in simple terms” every day – not because they aren’t smart enough, but because textbook explanations are useless for making real decisions. The problem isn’t the complexity of the topic. The problem is that most explanations were written by engineers for engineers.
Let’s cut through the jargon and formulas – and focus on what a manager actually needs to know.
By September 2026 the generative AI market holds dozens of tools. Every vendor claims the lead, and the marketing gets louder with each launch. So how does a manager pick a tool that actually gets the work done?
By February 2026, generative AI had moved well beyond text. It now produces video, images, music and slide decks – and the output is often good enough that you can’t tell it from a professional’s work.
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.
Imagine you’re choosing a company car for your team. One dealer says: “Our car is the fastest.” Another: “We have the best fuel economy.” A third: “We lead in safety.” They’re all right – but each is measuring something different. Without understanding what exactly is being measured and how, you can’t compare the options objectively.
While managers pay for ChatGPT Plus and Claude Pro, Alibaba has built one of the largest AI ecosystems in the world – a free chat app, open models and API prices that undercut the competition several times over. Qwen (pronounced “chwen”, from 通义千问 – “A Thousand Questions”) had overtaken every Western rival in downloads by mid-2026 and became a tool every manager should know. In the spring it looked as if the ecosystem was drifting toward the OpenAI and Anthropic playbook: the 3.7 flagships shipped closed, and key developers walked out. In August, Alibaba released three models and turned back. The flagship Qwen3.8-Max comes with a promise of open weights, and Qwen3.8-27B, under Apache 2.0, runs on a single consumer GPU.
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.
DeepSeek (often misspelled “Deep Seek”) is a Chinese AI startup that took two years to become one of the industry’s front-runners. It shipped V4 in April 2026. On 13 August V4-Pro came out of beta, and on 16 August the company rewrote its pricing.
The previous version of this review called DeepSeek the budget flagship and promised prices 10–50 times below the competition. The first claim still holds. The second needs redoing: DeepSeek raised its rates, introduced peak-hour windows, and pushed the price of a cache hit up by as much as 1,100% over the first preview pricing. The company frames this as becoming a sustainable compute provider. Whatever the reason, a budget built on spring prices no longer adds up.