TJT @thijos331 - Twitter Profile

AI speed vs org. Capabilities are there but bigger organizations won’t benefits due to internal politics. Check below presentation I generate using https://t.co/JaSFzVitFO

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TJT

@thijos331

8 days ago

The Org Chart Is Winning https://t.co/RQ3fZklJJN

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thijos331 retweeted

Carlos E. Perez

@IntuitMachine

9 days ago

1/ AI isn’t being slowed by a lack of intelligence. It’s being slowed by org charts. A model can improve overnight. A company may need 9 committees, 4 budgets, 3 lawyers, and one terrified executive to actually use it. 2/ AI diffuses at the speed of software—until it hits an organization. Then it diffuses at the speed of politics. 3/ Most companies say they’re “using AI.” What they often mean: • employees summarize emails • someone bought licenses • a pilot is trapped in legal review That’s not transformation. That’s AI-themed office furniture. 4/ The real unit of AI adoption isn’t the model. It’s the workflow. A workflow includes data, permissions, handoffs, incentives, liability, review—and someone whose career gets damaged when the system fails. 5/ Make one task 10x faster and you may improve the whole process by 10%. Why? Because the bottleneck simply moves downstream—to approval, verification, integration, or the one manager who insists on reviewing everything. 6/ AI creates output faster than organizations can create trust. More code. More reports. More proposals. More decisions. But the same number of qualified humans must verify them. The result can be output inflation, not productivity. 7/ Here’s the uncomfortable truth: The people who know enough to help automate a process are often the people most threatened by its automation. Why would they eagerly document the knowledge that makes them valuable? 8/ “Resistance to AI” is often misdiagnosed. It’s not fear of technology. It’s fear of getting: • more work • less status • more surveillance • more liability • none of the gains 9/ Management says: “Use AI to save time.” Employees hear: “Show us how much of your job we can remove.” Until that incentive problem is solved, organizations will get concealment, quiet resistance, and fake adoption. 10/ Pilots are popular because they let leaders look innovative without changing anything important. A pilot tests the technology. Scaling tests the organization. That’s why so many AI pilots never escape. 11/ Deep AI adoption reopens every question a stable organization worked hard to settle: Who decides? Who knows? Who owns the data? Who gets the savings? Who takes the blame? Whose job disappears? 12/ This is why procurement is not a side issue. Neither are compliance, training, legacy systems, labor rules, or data governance. They are the actual terrain over which AI must travel. 13/ Big companies have capital, data, and technical teams—but also legacy systems, veto points, and internal empires. Small companies can move faster—but often lack money, talent, data, and legal capacity. Everyone is blocked differently. 14/ The winners won’t just have the best models. They’ll have the highest institutional absorption rate: The ability to turn new capability into redesigned work before competitors finish forming a steering committee. 15/ The dangerous mistake is treating all friction as bad. Some friction is waste. Some is how we preserve safety, due process, privacy, worker voice, and accountability. The goal isn’t maximum diffusion. It’s maximum beneficial diffusion. 16/ The AI race is not just a race to build intelligence. It’s a race to reorganize around it. The frontier advances through breakthroughs. The economy advances through implementation. And right now, the org chart is winning.

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