AI productivity has a pricing problem.

AI is entering a strange new phase: it is no longer just about who has the smartest model. In medicine, researchers are warning that early AI use could stop trainees from building core judgment. In search, Google is showing that distribution may matter more than chatbot hype. And inside companies, Microsoft and Uber are exposing a harder truth: AI can improve work while making costs harder to control.

In today’s post:

• AI may be more expensive than people

• Google might be winning AI quietly

• Doctors may be losing a skill they never built

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RESEARCH

The real AI bottleneck might not be intelligence

Microsoft is reportedly cutting back some Claude Code licenses. Uber already burned through its 2026 AI coding budget in four months. That should make every company pause. AI was supposed to reduce costs. But heavy usage is creating a new problem. The more useful AI becomes, the more expensive it gets to run.

• Microsoft reportedly moved engineers away from Claude Code after internal usage grew too quickly.

• Uber pushed teams to use AI coding tools, then exhausted its annual budget early.

• The hidden cost is compute. Every AI request consumes tokens, and tokens cost money.

• Better AI agents may not lower bills. They may use far more tokens per task.

• Gartner predicts token prices will fall, but enterprise AI costs may still rise.

• This creates a strange paradox. Cheaper AI can become more expensive at scale.

• The question is no longer whether AI improves productivity. It is whether the gains justify the bill.

Most companies are still treating AI like magic. They measure adoption before they measure economics. That is dangerous. A tool can be impressive and still be financially weak. The winners will not be the companies using the most AI. They will be the ones that know exactly where AI pays for itself.

PROFITS

The AI race may not be won by the best chatbot

Google looked late to AI two years ago. Its first AI answers were messy, strange, and easy to mock. People joked about it telling users to eat rocks. But something changed. Google stopped treating AI like a separate product. It started putting Gemini everywhere people already spend time.

• Gemini now has 900 million regular users, putting it near ChatGPT’s reported scale.

• Google’s real advantage is not just the model. It is distribution through Search, Gmail, Docs, Maps, Android, and soon Siri.

• OpenAI and Anthropic still face huge infrastructure costs. Google already has a profit engine through ads.

• AI can make Google’s advertising business stronger by helping marketers understand users better.

• Gemini is becoming useful inside everyday tasks, like drafting documents, planning travel, and shopping online.

• Google’s mistake-prone AI answers still create trust problems. Accuracy remains the weak point.

• The bigger lesson is simple: the most-used AI may be the one people barely notice adopting.

Google may not need people to choose Gemini. It only needs Gemini to appear where people already are. That is a very different kind of power. Most AI companies are fighting for attention. Google is fighting from inside existing habits. That does not guarantee it wins. But it makes the race much harder for everyone else.

AI MEDICARE

AI in medicine is moving faster than medical education can adapt

Nature Medicine published a new perspective on “AI-induced never-skilling.” The idea is simple, but uncomfortable. Medical trainees may rely on AI so early that they never build the clinical reasoning skills they need later. This is not deskilling. Deskilling happens when experts lose abilities they once had. Never-skilling happens when beginners never develop those abilities at all.

• AI can help doctors work faster, but timing matters. Introduced too early, it may replace the struggle that builds judgment.

• Medical training depends on hard, repeated reasoning. Students need to make sense of uncertainty before a tool does it for them.

• The authors separate never-skilling from mis-skilling. Mis-skilling happens when trainees absorb AI mistakes as clinical truth.

• The concern is not that AI is bad. The concern is careless use during the most fragile learning years.

• A trainee who always checks AI first may stop forming their own diagnostic instincts.

• The paper argues for AI-independent baseline competency before supervised AI use becomes normal.

• The future doctor should not be anti-AI. They should be able to think clearly before, during, and after using it.

AI will probably become part of every doctor’s work. That is not the real question. The real question is what kind of doctor we create before AI enters the room. Tools are safest in the hands of people who can function without them. Medicine should not train students to compete with AI. It should train them to know when AI is wrong.

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