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What if AI Succeeds Too Well? Breaking Down the Citrini 2028 Scenario

7 min read

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?

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What if AI Succeeds Too Well? Breaking Down the Citrini 2028 Scenario
Why Learn AI in a Course When ChatGPT Knows Everything: A Research-Based Analysis
12 min

Why Learn AI in a Course When ChatGPT Knows Everything: A Research-Based Analysis

“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.

AI Doesn't Save Time – It Compresses It: 8 Months of Observations
11 min

AI Doesn't Save Time – It Compresses It: 8 Months of Observations

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.

33 AI Models for Managers: Why We Need Your Ratings
11 min

33 AI Models for Managers: Why We Need Your Ratings

Over the past year, 33 new AI models have appeared on the market, each claiming the title of “best manager’s assistant.” ChatGPT updated to GPT-5.2, Claude released Opus 4.5, Gemini added a new Pro version, Yandex and Sber announced further improvements, and Chinese models went OpenSource. How do you choose a tool when every one of them promises a productivity revolution? We decided to run a large-scale comparative study – but ran into a problem that may seem paradoxical.

The Hidden AI Tax: Why 40% of Time Savings Disappear
10 min

The Hidden AI Tax: Why 40% of Time Savings Disappear

87% of employees use AI at work. 77% say they’ve become more productive. Sounds like a digital transformation success story. But ask the uncomfortable question: if AI boosts productivity so effectively, why do only 14% actually see net gains? Where do the other 86% go?

Workday conducted a global study with 3,200 respondents – and found that behind the impressive numbers lies an inconvenient reality. Nearly 40% of time saved with AI is spent fixing its mistakes. This isn’t a bug in specific tools, nor a problem of “incorrect usage.” It’s a systemic failure in how organisations deploy artificial intelligence.

At mysummit.school, we’ve previously covered Dejan.AI (AI as a junior intern), Gallup (usage doubling in a year), and MIT (the gap between leaders and laggards). Workday adds a new angle: the measurable cost of low-quality AI output. And that cost turns out to be higher than many are willing to admit.

86% of Students Use AI, But Are Getting Worse. One Experiment Changed Everything
16 min

86% of Students Use AI, But Are Getting Worse. One Experiment Changed Everything

Traditional approaches to education are breaking down. AI writes essays and papers in minutes – and that has permanently changed the purpose of creative assignments in schools. Banning neural networks doesn’t work, and isolating students from technology is a dead end. The question is not whether to use AI. The question is how to use it so the technology develops students’ skills rather than replacing their thinking.

45% of Americans Use AI Annually: Gallup 2025 Data and What Changed in a Year
12 min

45% of Americans Use AI Annually: Gallup 2025 Data and What Changed in a Year

Gallup – one of the oldest polling organizations in the US – has released fresh data on how Americans used artificial intelligence in 2025. We’ve already covered Stanford (37% personal use), Brookings (57% use AI, but only 19% see results), and Wharton (82% of executives use it weekly). Now we have Gallup’s numbers – and they point to an important trend: usage is growing, but more slowly than the hype suggests.

82% of Executives Use AI Weekly: How AI Penetrates Industries – Wharton 2025 Report
14 min

82% of Executives Use AI Weekly: How AI Penetrates Industries – Wharton 2025 Report

For the third year running, Wharton School of Business and GBK Collective have been tracking how US companies deploy generative AI. The fresh October 2025 numbers tell a clear story: the technology has moved from experiment to daily practice. In a previous article we looked at the gap between rank-and-file AI use (19% see results) and corporate projects (80% succeed). The new Wharton report shows exactly how companies get to that 80% – and why results differ across industries.