Twelve hours a week. That’s how much time a typical project manager spends on reports, plan updates, stakeholder correspondence, and risk tracking. Nearly a third of their working time goes not to making decisions, but to documenting them.
With AI, that time drops to three hours. But only under one condition: AI must be embedded in the operational rhythm of work, not used episodically – “when you remember.”
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320,000 stars on GitHub. A physical conference in Manhattan. Government subsidies in Shenzhen. In six weeks, OpenClaw has gone from a repository with an interesting README to a geopolitical phenomenon discussed by Bernstein analysts, officials at China’s Ministry of Industry, and users on Xianyu posting consulting ads with the headline “will help you uninstall.”
This is the sixth part of the series. In the previous part, we recorded 265,000 stars, the creator joining OpenAI, the first enterprise deployments via Sundae Bar, and three new CVEs. That was 12 days ago.
Can an open-source Chinese model compete with the closed flagships from OpenAI and Anthropic? Based on our independent testing – yes. On January 27, 2026, Beijing-based Moonshot AI released Kimi K2.5, and it immediately joined the elite group of the world’s best models – competing directly with GPT-5.4 and Claude Sonnet 4.5. And it’s completely free for basic use.
On February 6, 2026, an anonymous model called “Pony Alpha” appeared on OpenRouter – free, with zero details about its creators. The AI community immediately set about identifying it. Its coding abilities came remarkably close to Claude Opus 4.5. When asked “who are you?”, the model responded: “I am GLM.” But when prompted to write a web page describing itself – it wrote: “I am Claude, created by Anthropic.”
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?
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 March 2026, the generative AI market has dozens of tools. Every vendor claims to be the leader, and marketing materials compete in loudness. How does a manager choose a tool that actually solves real problems?
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.