^{AI MARKETPULSE}
Texas Gov. Greg Abbott Says AI Data Centers Deserve the Backlash

Texas Gov. Greg Abbott says the AI data center industry has “dug their own grave” by moving too aggressively without earning support from the communities affected. Abbott was initially a strong supporter of expanding data center infrastructure in Texas, but growing concerns over electricity costs, water use, noise, and local disruption have changed his position. He has since pushed new standards, floated restrictions on rural developments, and said his administration has halted roughly 1,800 AI data center projects. The political pressure is broader than Texas: Gallup found 70% of Americans oppose AI data centers being built in their local area.

Key Takeaways:

• Abbott says his administration has halted roughly 1,800 AI data center projects in Texas

• Gallup found 70% of Americans oppose AI data center construction in their local area

• Growing public resistance could force AI companies to rethink where and how aggressively they build infrastructure

👉️ Read the full article here

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^{AI SPOTLIGHT}
America’s Largest Power Grid Is Telling AI Data Centers to Bring Their Own Energy

PJM Interconnection, which operates the largest U.S. power grid across 13 states and Washington D.C., is proposing new rules that would force large data centers to bring their own power or risk being curtailed first during grid emergencies. The pressure is already showing up in prices: wholesale power costs across PJM jumped 75.5% year over year, while its latest capacity auction came up 6.8 GW short of its reliability requirement. Under the proposed framework, new facilities above 50 MW would need qualifying generation to receive firm service starting in June 2027.

Key Takeaways:

• New data centers above 50 MW could face priority curtailment unless they secure their own power

• PJM projects roughly 70 GW of new large-load demand, mostly from data centers, by 2038

• If approved, PJM's approach could become a model for other U.S. grids dealing with rapidly rising AI power demand

👉️ Read the full article here

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^{TODAY’S PROMPT}
Build an AI-Powered Service You Can Actually Sell

This mega-prompt acts like a business strategist, turning your skills, experience, and available AI tools into a done-for-you service people could realistically pay for. It builds the offer, target customer, delivery system, pricing logic, business plan, and implementation roadmap so you can go from vague idea to something you can actually test.

👉️ See the example inputs and outputs here

Act as an AI service business strategist.

INPUTS
Skills: [List]
Experience: [Describe]
Industries: [List]
Tools: [List]
Preferred work: [List]
Avoid: [List]
Customers: [Describe]
Problems: [List]
Time: [Hours/week]
Budget: [Budget]
Model: [Project / Retainer / Either]
Network: [Describe]
Goal: [Describe]

TASK
Build a specific AI-powered service I can
realistically sell.

Avoid vague AI consulting ideas. Focus on a real
customer problem, clear deliverable, and practical
AI leverage.

CREATE:
1. OFFER

Give 3 service ideas. Choose the best.

Define the customer, problem, outcome,
deliverables, and why they would pay.

Write a one-sentence offer.

2. DELIVERY
Show:

CLIENT INPUT
↓
AI WORK
↓
HUMAN REVIEW
↓
DELIVERABLE

Explain what AI handles, what I handle, and the
minimum tool stack.

3. BUSINESS MODEL
Recommend pricing, delivery time, costs, and
client capacity.

Give conservative, base, and strong monthly
revenue scenarios.

Label all figures as estimates.

4. CLIENT ACQUISITION
Explain where to find prospects, who to target,
and how to get my first 5 clients.

Write a cold email, LinkedIn message, and
follow-up.

5. SAMPLE
Show what the client actually receives.

6. 30-DAY PLAN
Week 1: Validate offer
Week 2: Build delivery system
Week 3: Prospect
Week 4: Sell, deliver, improve

7. FINAL PLAN
Summarize the offer, customer, tools, pricing,
acquisition, revenue potential, risks, and
30-day goal.

Finish with the ONE action I should take next
to validate demand.
Keep it concise, realistic, and practical.
Do not inflate revenue or assume demand.
Prefer simple workflows.
If information is missing, make a reasonable
assumption.

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^{AI QUICK HITS}

🤖 Apple is reportedly preparing a new iMac with its 2nm M6 chip and refreshed color options for later this year, as the company accelerates its Mac silicon roadmap and pushes more AI-focused computing onto its own hardware.

🚕 Waymo’s parked robotaxis in Nashville are reportedly still capturing 360-degree video and ambient audio even when nobody is inside, raising new privacy questions about how autonomous vehicles could quietly become rolling surveillance infrastructure.

📹 Flock Safety CEO Garrett Langley is calling for a “compromise” between privacy and safety as bipartisan backlash grows around the company’s license plate readers, cameras, and drones, including documented cases of police officers allegedly misusing its technology.

🗑️ LinkedIn users have clicked the platform’s “Seems like AI slop” button more than 1 million times since its July launch, while LinkedIn says changes to its detection systems have already reduced views of content it classifies as AI slop by 40%.

⚡️ The AI data center boom is running into a surprisingly physical bottleneck: gas turbines, with GE Vernova reportedly taking reservations for deliveries as far out as 2031 while U.S. data center power demand is projected to surge over the next several years.

^{AI TOOL STACK}

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🤔 ^{Which image is real?}

One of these images is real, and one was created with AI. Vote for which one you think is real, and tell us why you voted that way.

The last edition’s results—55% were right!

Did you vote in the poll from August 12th? The bottom image was real! Go back and see the photos here. So what do you think? Are you getting better at spotting AI vs. not?