Writing and Building Intelligence Economy | Grew Startups from zero to $1M+ | Fighting Cyber Crime | Join https://teapaw.org/t.co/mMbDcbJxbp
👇 Intelligence Economy
Joined April 2008
- Tweets9.1K
- Following12.4K
- Followers13.1K
- Likes1.2K
Pinned Tweet
What Everyone Is Missing About The SpaceX IPO teapaw.org/i/broadcasts/1XxyggLED…
Sameer Khan retweeted
Most enterprises treat chip export controls as the geopolitical risk in AI. The exposed layer sits lower, in the power equipment that has to arrive before any chip can run. Most AI risk registers do not list it.
Sameer Khan retweeted
Most leaders negotiate automation contracts on price and uptime. The clauses that matter cover who owns the data and site models, and what continues after the contract ends. Who reviews those? intelligenceeconomy.co/p/toy…
Sameer Khan retweeted
Who owns the live record of your plant? The ERP holds commitments and payments, but operations and vendors hold the live picture. When two records disagree on a maintenance call, who decides? intelligenceeconomy.co/p/toy…
Sameer Khan retweeted
A Coalition for a Prosperous America analysis: US utilities imported 8,000+ high-power transformers from China in 2025, up from under 1,500 in 2022. Chip rules got the headlines. Which of your AI timelines depends on equipment you have never traced?
Most leaders negotiate automation contracts on price and uptime. The clauses that matter cover who owns the data and site models, and what continues after the contract ends. Who reviews those? intelligenceeconomy.co/p/toy…
Sameer Khan retweeted
1/4 Your AI policy is probably not where your AI behavior lives.
The approved document sets intent. Day-to-day outcomes are shaped lower in the stack—by instructions, retrieved knowledge, permissions, workflow defaults, and escalation habits.
ALT The Shadow Policy Layer infographic. An iceberg shows approved policy above the waterline and five hidden layers below it: system instructions, retrieved knowledge, tool permissions, workflow defaults, and escalation habits. The takeaway is to review where policy becomes behavior.
Sameer Khan retweeted
Gartner forecasts $2.59 trillion in AI spending for 2026, up 47%. Most people read that as adoption. But more than 45% is vendor infrastructure, which is capacity bought ahead of results. What share of your AI budget is tied to a named outcome?
A Coalition for a Prosperous America analysis: US utilities imported 8,000+ high-power transformers from China in 2025, up from under 1,500 in 2022. Chip rules got the headlines. Which of your AI timelines depends on equipment you have never traced?
Sameer Khan retweeted
Most manufacturers blame the model when AI stalls. The Manufacturing Leadership Council found 40% need days, and 17% need weeks, to make operational data actionable. An agent working from last week's record makes last week's decision.
Most enterprises treat chip export controls as the geopolitical risk in AI. The exposed layer sits lower, in the power equipment that has to arrive before any chip can run. Most AI risk registers do not list it.
Sameer Khan retweeted
Quick poll for enterprise AI leaders. When an AI project slips past its date, where does the delay usually start? A. Data and records. B. Power and site readiness. C. Vendor delivery and logistics. D. Internal approvals. Reply with your letter and why.
Who owns the live record of your plant? The ERP holds commitments and payments, but operations and vendors hold the live picture. When two records disagree on a maintenance call, who decides? intelligenceeconomy.co/p/toy…
Sameer Khan retweeted
1/4 The safest place to start with AI is not always the simplest task.
Many roadmaps rank work by technical feasibility. That misses two operational questions: How quickly will we see when the system is wrong? How easily can we undo what it did?
ALT The Reversibility Map, a 2x2 executive decision framework. Outcome visibility runs from immediate to delayed; reversibility runs from easy to hard to undo. The quadrants are Delegate, Pilot, Assist, and Human-led. The decision rule is to start where consequences are visible and reversible.
1/4 Your AI policy is probably not where your AI behavior lives.
The approved document sets intent. Day-to-day outcomes are shaped lower in the stack—by instructions, retrieved knowledge, permissions, workflow defaults, and escalation habits.
ALT The Shadow Policy Layer infographic. An iceberg shows approved policy above the waterline and five hidden layers below it: system instructions, retrieved knowledge, tool permissions, workflow defaults, and escalation habits. The takeaway is to review where policy becomes behavior.
3/4 Treat policy as a behavior stack, not a document.
For consequential workflows, trace the full path from instruction to action. Ask where a rule becomes a default, where a permission becomes authority, and where an exception becomes someone else’s problem.
Sameer Khan retweeted
Reuters reports UPS CEO Carol Tome sees a $15 billion annual revenue opportunity in the AI data center boom. Chips get the headlines, but the build also runs on freight. Who else is competing for the same logistics capacity your AI project needs?
Gartner forecasts $2.59 trillion in AI spending for 2026, up 47%. Most people read that as adoption. But more than 45% is vendor infrastructure, which is capacity bought ahead of results. What share of your AI budget is tied to a named outcome?