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Both things can be true. The outcome depends on whether per-token cost compression (driven by on-device AI and hyperscaler competition) outpaces token consumption growth (driven by agentic use cases). We will know by Q4 2026 when enterprise budgets get renewed or slashed.
Microsoft cancelled most internal AI coding licenses after 6 months. One Nvidia exec explained: "the cost of compute was far beyond the cost of the employees." This is Microsoft - the company that owns 49% of OpenAI. Even they could not make the math work.
Agentic AI is still early. As models improve reasoning efficiency, task completion could require fewer tokens. A task burning 10,000 tokens today might only need 2,000 tokens in 2027. Better models could solve the consumption problem before it becomes systemic.
The big question: do token costs compress faster than consumption grows, or is the enterprise AI market about to hit an ROI wall? The Microsoft and Uber cancellations suggest we are closer to the wall than the industry wants to admit. Watch Q4 2026 budget announcements.
Full piece: https://t.co/MPDpdLsVvO #AI #Strategy #Tech
SaaS companies are shifting to consumption-based pricing - charging per AI task instead of per user seat. This is not margin expansion. It is cost-shifting. They are passing unpredictable token costs to customers to avoid being crushed by usage spikes.
Personal opinion: if you are getting one of the IIT's join it and put the effort there instead of dealing with uncertainty for the next year.
At different points in life you will feel you made the wrong or the right choice.
10 years down the line either way wont matter if you continue to work hard.
PS: I took a drop
AI just created a new attack vector called slopsquatting.
AI hallucinates a package name.
The attacker registers it.
The developer installs it on the AI's recommendation. Malware ships.
1 in 5 AI-generated code suggestions references a package that does not exist.
@_fernando_rosas It has been post trained on the responses that humans would like so its goal should be to add value to the person inputting the tokens
The Solow Paradox redux: In the 1970s, massive IT investment failed to boost productivity because companies automated the wrong work.
Now: 95% of AI pilots fail for the same reason. They make broken processes faster instead of fixing them first.