Top 5 stories:
1️⃣ OpenAI $1.4 Trillion investments. Backstop?
2️⃣ A DeepSeek 2.0 moment ?
3️⃣ Layoffs Spike, AI Cited
4️⃣ Apple Siri by Google?
5️⃣ Google’s Space Data Centers

+ Video summary in < 5 minutes + Video summary of top 5 disucssions

Leaders should ask two simple questions. If abundance arrives, who captures it and on what terms. If it does not, what is your plan when prices fall faster than demand rises.

But right now it feels like there are no adults in the room influencing who pays, who benefits, and what happens if the capex story stalls.

Optimists argue the opposite. Cheaper models, custom chips, and agentic apps can expand demand faster than prices fall. Productivity gains could finally show up in profit and loss statements. Maybe they are right.

Put it together and you get a world where model prices drift toward zero, infrastructure prices trend lower as supply catches up, and margins compress across the stack unless new, sticky applications create clear willingness to pay. That requires real product market fit, not hype.

Custom silicon plus massive capacity buildouts can pull pricing power away from any single supplier over time. If supply overshoots demand, chip and cloud prices will grind down.

Service businesses that lived on billable hours feel it as AI strips time from workflows and pushes fees down.

Up the stack, the moat is shifting to infrastructure. Microsoft is rolling Maia accelerators. Amazon is pushing Trainium and Inferentia. Google is advancing its next TPU.

That implies about thirty gigawatts of compute capacity. The spend dwarfs today’s profits and assumes scale economies show up fast.

The labor signal is flashing. U.S. employers announced the highest October layoffs since 2003. Cost cutting and AI are cited among the drivers.

If the best model is free or near free, paid model pricing faces real pressure.

At the same time, OpenAI is said to be near twenty billion dollars in annual revenue while lining up about one point four trillion dollars in data center and chip commitments over roughly eight years.

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Top 5 stories:
1️⃣ OpenAI Pivot to Porn?
2️⃣ AI helping solve cancer?
3️⃣ Will the AI circle grow or pop?
4️⃣ Google Gemini assault on enterprises
5️⃣ Gemini 3 coming this week?

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+ SOLD OUT (More tix coming) AI Forum in SF with AI 50 (alum-lead)‼️

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Top 5 stories of the week:
1️⃣ Nano-Banana Fever - the Facts
2️⃣ AI Cuts 13% of Entry-Level Roles?
3️⃣ The $3 Trillion AI Infrastructure Boom
4️⃣ Nvidia Smashes Sales Records
5️⃣ Alibaba AI Chip to Bypass U.S. Silicon

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What single commitment will your organization make in the next 60 days to close this gap and who owns it?

Link to Kyla's full Substack post : kyla.substack.com/p/ai-that-wo...

➡️ Entertainment/Film & TV and Design/Creative report the least training, with roughly 95% and 80% saying “no.”

➡️ Tech, consulting, and cybersecurity are better prepared, yet even leaders top out near 55–60% trained.

➡️ Low training tracks low trust, which makes adoption harder and outcomes worse.

The result is predictable: rushed rollouts, uneven skills, and avoidable risk.

Source: Kyla Scanlon’s Aug 28, 2025 reader survey (~1,200 anonymous responses), an exploratory snapshot likely skewed to U.S. professionals; not a representative sample but directionally useful.

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