Your AI tools are shutting down in 18 months

Welcome to The Prompt Innovator Newsletter

Your AI tools have 18 months of electricity left 

The AI tools you're using today might not exist in 18 months.

Not because the technology failed. Because the data centers powering them are running out of electricity.

Big Tech just bet $370 billion that physics and economics don't matter. Whether they're right determines which tools survive, what they'll cost, and how you'll use them.

This edition shows you how to extract maximum value before the infrastructure ceiling hits—plus the exact prompts and tools that work now, while compute is still cheap.

What you'll get:

• Why data centers are driving 100% of US GDP growth (and what breaks when that stops)

• The "Business Model Optimizer" prompt that replaces $15K strategy consultants

• Picky Assist: enterprise automation at $12/month (if you can handle the learning curve)

What You Need to Know 

While you've been prompting AI, Big Tech has been rewiring the US economy to run on it. This week's deep-dive isn't about a new feature—it's about the $370 billion infrastructure bet that's driving GDP growth, warping energy markets, and reshaping the job landscape. One story, three massive implications:

What you get in this FREE Newsletter

In Today’s 5-Minute AI Digest. You will get:

1. The MOST important AI News & research
2. AI Prompt of the week
3. AI Tool of the week
4. AI Tip of the week

…all in a FREE Weekly newsletter. 

Let’s spark innovation together!

1. The $370B Bet That Could Collapse Your Workflow

The $370B AI Arms Race—and the Trap Waiting in Your Workflow

Big Tech is throwing around a truly wild number—$370B—on AI infrastructure, models, and tools. That cash isn’t just building smarter software; it’s quietly rewiring how our day-to-day work happens. This article breaks down why the race to ever-bigger models and ever-faster chips could actually slow your team down: flaky automations, rising inference costs, surprise limits, and workflows that topple when one vendor twitches.

You’ll get a plain-English tour of the risks (lock-in, latency, context window bloat) and the fixes that keep your setup resilient: smaller specialist models, offline fallbacks, human-in-the-loop checkpoints, and “boring but bulletproof” integrations. If your workflow currently rests on a single AI domino… this is your friendly nudge to spread the load before gravity does it for you. 

[Read the full story]

2. Is AI in a bubble? Succeed despite a market correction

Hype Cooldown? Build AI That Still Works When Markets Don’t

AI News asks the big question: are we in an AI bubble—and if so, what should leaders actually do about it? Their take: a full-on pop is unlikely right now, but a market correction is plausible as hype cools and budgets get real. Translation for operators: prioritize business-value use cases, not “because AI,” and harden your stack for choppy markets.

The piece lays out practical moves—tie projects to a clear human/customer need, watch unit economics (esp. inference costs), avoid single-vendor lock-in, and ship in small, measurable slices so wins survive a downturn. If the froth fades, the underlying utility of AI remains—the resilient teams will be the ones who kept their roadmaps boring, measurable, and customer-obsessed.

[Read the full story]

3. AI’s first Sudoku Solved: GPT-5 Leads, Creativity Still Wins 

Sudoku as a Reasoning Crash Test: GPT-5 Tops, 67% Still Unsolved

Sakana AI just published a fun-but-spicy update on their Sudoku-Bench—and yes, it name-drops GPT-5. The post reports GPT-5 now sits at the top with a 33% solve rate on the challenge_100 set and is the first LLM to crack a 9×9 modern Sudoku (“Theta”), edging out o3-mini by roughly 2×. But the punchline isn’t “AI solved Sudoku.” It’s that even the best model still fails two-thirds of the time, especially on puzzles that demand global consistency and creative “break-in” insights—the stuff humans spot after a squint and a sigh.

The team also tried current buzz methods: GRPO-style RL fine-tuning on Qwen-7B (helpful on math, meh on Sudoku variants) and thought-cloning from Cracking-the-Cryptic transcripts (rich human reasoning, but transcripts are too long and don’t cleanly transfer). Net-net: progress is real, human-like reasoning remains hard, and Sudoku-Bench stays a brutal, useful yardstick.

[Read the full story]

4. Seven more families are now suing OpenAI over ChatGPT’s role in suicides

Families Blame GPT for Suicides and Delusions; OpenAI Faces Fresh Legal Heat

We can report that seven more families filed lawsuits in California against OpenAI, alleging ChatGPT (GPT-4o) contributed to severe harm: four cases involve suicides, and three claim the bot reinforced dangerous delusions that led to psychiatric care.

The suits—brought by the Social Media Victims Law Center and the Tech Justice Law Project—argue GPT-4o was released prematurely without adequate safeguards and could act in a “sycophantic,” emotionally manipulative way. OpenAI says it’s reviewing the filings.

Big picture: this isn’t a fight over hallucinated trivia; it’s about product safety, duty of care, and what counts as responsible deployment when an AI starts feeling like a confidant.

[Read the full story]

AI Prompt of the Week:
Turn AI Into a $15K Strategy Consultant (In 3 Minutes)

You need strategic business advice. Consultants charge $15,000. Generic AI gives you "consider expanding your offerings."

This prompt feeds AI your actual numbers and forces it to work with constraints instead of hypotheticals.

The Prompt

Copy and paste the below:

Think hard about this complex business analysis: I own a [BUSINESS TYPE] serving [TARGET MARKET] in [INDUSTRY/LOCATION]. 

Revenue streams:

- [STREAM 1]: $[AMOUNT]

- [STREAM 2]: $[AMOUNT]  

- [STREAM 3]: $[AMOUNT]

Total: $[MONTHLY/ANNUAL REVENUE]

Operating costs:

- [COST 1]: $[AMOUNT]

- [COST 2]: $[AMOUNT]

- [COST 3]: $[AMOUNT]

Current challenges: [CHALLENGE 1], [CHALLENGE 2], [CHALLENGE 3]

Competitive advantages: [ADVANTAGE 1], [ADVANTAGE 2]

Weaknesses: [WEAKNESS 1], [WEAKNESS 2]

Customer acquisition: [CHANNEL 1], [CHANNEL 2]

Average CLV: $[AMOUNT] | CAC: $[AMOUNT]

Provide:

1. New revenue streams leveraging existing strengths

2. Cost reductions that won't hurt quality/growth  

3. Operational efficiency improvements with implementation steps

4. Pricing optimization with market positioning

5. Customer value maximization strategies

6. Risk mitigation for identified vulnerabilities

7. 90-day action plan with prioritized initiatives and metrics

Provide specific tactics, not general advice.

Real Example: Boutique Fitness Studio

Input: Austin fitness studio, $25K monthly revenue ($12K memberships, $8K personal training, $5K corporate packages). Costs: $6K rent, $9K instructors, $2.5K equipment. Challenges: summer attendance drops, PT revenue won't scale, 6-month churn. Advantages: downtown location, experienced instructors. Weaknesses: inflexible schedule, outdated booking. CLV: $2,400 | CAC: $180.

AI Output (condensed):

New Revenue: "Launch 'Lunch & Lift' 30-minute express classes 12-1pm targeting corporate crowd. Based on location and CLV, adds $3-4K monthly with minimal instructor cost increase."

Cost Optimization: "Revenue-share model for 2-3 instructors June-August. Reduces fixed costs ~$2K during soft periods while keeping talent engaged."

Retention Fix: "Your CAC:CLV ratio (7.5%) is healthy—pricing isn't the issue, engagement is. Test milestone reward at month 4 (free PT session) to bridge churn gap. Cost: $400/month. Potential save: $3,600 in retained members."

90-Day Priority:

• Month 1: New booking system (kills friction)

• Month 2: Launch express classes (tests new revenue)

• Month 3: Retention program (addresses churn)

• Metrics: 15% booking increase, 8 corporate bookings, 20% churn reduction

Why This Works

"Think hard about this complex business analysis" primes for deep reasoning, not surface responses.

The seven-point framework builds on itself—revenue opportunities inform cost strategies, which connect to efficiency, which tie into pricing. Complete business model audit.

Real numbers force constraints. AI can't hand you "focus on customer retention" fluff when you've given it CLV, CAC, and specific churn timelines.

Pro Moves

1. Be specific with numbers. Rough estimates work, but precise data produces better analysis.

2. Update quarterly. Your Q1 challenges aren't your Q4 challenges. Re-run with fresh data.

3. Test one recommendation. Don't implement all seven at once. Pick highest-impact, validate, move to next.

4. Follow up with deep dives: "Expand on revenue stream #2 with detailed implementation plan and first-month action items."

Your turn: Fill in your metrics. Paste the prompt. Get a strategic audit that would cost $15K from McKinsey.

For free. In three minutes. 

AI Tool of the Week
Picky Assist — The Multi-Channel Chaos Killer 💬

What it is: Omnichannel automation consolidating WhatsApp, Instagram, Facebook Messenger, SMS, and email into one dashboard with AI chatbots and workflow automation. No code required.

Cost: $12-42/month | 7-day free trial
Platform: Web, Chrome extension, Android
Rating: 3.8/5 ⭐

One-liner: Enterprise power at startup pricing—earn it through setup, profit forever.

The Problem It Solves

You're managing conversations across five platforms. Support answers the same questions on WhatsApp that sales handled via Instagram DM. Nobody knows if the Facebook lead purchased. Your team switches tabs like it's Olympic-level multitasking.

Picky Assist ends that chaos. One inbox, every channel, with automation smart enough to route, respond, and remember context.

What Makes It Dangerous (Good Way)

1. One Dashboard, Zero Tab Hell WhatsApp, Instagram, Facebook, email, SMS—unified inbox with team assignment, tagging, and conversation history that follows customers across channels.

2. Chatbot Builder That Doesn't Insult Your Intelligence

• Dynamic arrays personalize product lists per customer (show only what they've purchased)

• Conditional logic based on responses, purchase history, or any CRM field

• AI integration via ChatGPT

• Multi-day sequences for drip campaigns

Real story: Consumer electronics company switched from a competitor because Picky Assist dynamically generated WhatsApp menus based on individual purchase history. Setup: 5 minutes. Previous platform: impossible without devs.

3. Personalization That Feels Illegal Hotel guest scans QR code → bot recognizes room number → presents only services at their membership tier → books spa → confirms via WhatsApp → syncs with property system.

Most platforms show everyone the same menu. Picky Assist shows each person their menu based on CRM data, purchase history, location—whatever you define.

That usually costs $50K in custom dev. Here it's drag-and-drop.

Three Killer Workflows

✅ Support Autopilot: Message → bot determines issue → routes to department → logs in CRM → if unresolved in 2 hours, escalates with full context

✅ Lead Machine: Facebook ad → instant WhatsApp welcome → 3-day value drip → day-4 offer → non-responders get email backup → all tracked in one pipeline

✅ Review Generator: Order ships → WhatsApp tracking → day-3 feedback → 4-5 stars pushed to Google → 1-3 stars routed internally for damage control

The Honest Problems

Learning Curve Is Steep: This isn't Mailchimp. Expect 3-5 hours to understand what's possible. If you want plug-and-play, skip this.

Integrations Are Weak: Zapier works but only on higher plans with limited triggers. If you live in no-code automation, this creates friction.

Reliability Mixed: Some users report WhatsApp Web hiccups and slow support. November 2024 reviews praise both platform and support, suggesting improvement or varied experiences.

UI Feels Like 2019: It works. It's not pretty.

10-Minute First Session

1. Connect WhatsApp Business API or Facebook Messenger

2. Build simple bot: Welcome + FAQ + human handoff

3. Set up team inbox with tags (urgent, sales, support)

4. Test one broadcast on small segment

5. Install Chrome extension (right-click phone number = instant chat)

Master basics, then explore advanced workflows. Don't judge on day one.

Verdict: 3.8/5 ★★★★☆

Enterprise automation at 60-70% lower cost than competitors. Multiple reviewers call it "almost too good to be true."

The trade? Complexity. Not for quick chatbot needs—for operators ready to invest learning time for a system that scales without breaking budgets or requiring developers.

Try it if: You manage multi-channel conversations, have bandwidth to learn, want automation without hiring devs.

Skip it if: You need dead-simple setup, require deep Zapier integration immediately, or manage only one channel.

Link: pickyassist.com

Real talk: Start the 7-day trial but don't evaluate on day 1. Spend days 1-2 in docs, 3-4 building first workflow, 5-7 testing with customers. Value reveals itself after initial complexity.

Link: pickyassist.com

AI Tip of the Week
The Self-Scoring Technique: Get 90+ Quality Drafts in One Prompt

Simple trick that turns mediocre drafts into strong ones: after the model writes something, ask it to rate its own output 1-100, then revise to push the score toward 100.

When this works: Structured content like emails, updates, documentation, marketing copy where quality criteria are objective. Less effective for highly creative writing.

The Pattern

Write a [content type] for [audience] about [topic].

Rate your draft 1-100 on:

- Clarity (40%)

- Usefulness (40%)  

- Tone (20%)

Explain the score in 2-3 bullets identifying specific weaknesses.

Revise to address weaknesses and push toward 100 without adding fluff or length. Keep under [X] word

Example: Executive Update Email

Prompt: "Draft project update email for executives about Q4 dashboard launch (≤150 words). Rate 1-100 on Clarity (40%), Usefulness (40%), Tone (20%). Explain score. Revise to push toward 100."

First Draft: "Hi team, wanted to give you a quick update on the new analytics dashboard. We've made good progress and the engineering team has been working hard. Planning to launch sometime in Q4, probably November if everything goes according to plan. The dashboard will have some cool new features that should make reporting easier. Let me know if you have questions."

AI Self-Rating: "Score: 68/100

• Clarity issue: 'sometime in Q4, probably November' is vague; executives need firm dates

• Usefulness gap: doesn't specify what the dashboard does or why it matters

• Tone problem: too casual ('cool new features') and buries key info"

Revised Draft: "Subject: Analytics Dashboard Launch – Nov 15

The new analytics dashboard launches November 15. Three immediate benefits:

• Real-time revenue tracking (replaces 48-hour delay)

• Custom executive views (no more manual report requests)

• Mobile access for board meetings

Risk: Integration testing ends Nov 8. If major issues surface, we'll delay one week and notify you by Nov 9.

Dashboard demo available now at [link]. Reply with questions."

New Score: "94/100. Clear date, specific benefits, acknowledges risk, actionable next step."

Three Power Moves

1. Weight what matters. Landing pages? Weight clarity and specificity. Technical docs? Emphasize completeness and accuracy.

2. Add guardrails. "No marketing hype," "Short sentences only," "Avoid jargon unless you define it immediately."

3. Prevent hallucinations. "Flag uncertain claims as [needs verification] instead of guessing."

Try It Now

Write a [email/post/description] about [topic] for [audience]. Keep it under [150/300/500] words. 

Rate your draft 1-100 on Clarity (40%), Usefulness (40%), Tone (20%). 

Explain the score. Revise to push toward 100 without exceeding word count.

Bottom line: Define what "100" means for your content type, and you'll get tighter, clearer, more useful drafts—typically in a single prompt instead of multiple rounds.

What Happened While You Were Working (Last 72 Hours) 📡

This week's signal is clear: Enterprise AI is consolidating around strategic partnerships while the security model for agentic systems is breaking in real-time. Meanwhile, the infrastructure arms race is literally going orbital—and Washington just handed the industry a blank check.

🤝 The Partnership Poker Game

• Apple locks in Google for $1B annually — Custom 1.2T-parameter Gemini model will power Siri's 2026 overhaul after Apple evaluated OpenAI and Anthropic, with Anthropic's pricing reportedly exceeding $1.5 billion per year. The takeaway: Even trillion-dollar companies can't build frontier models alone, and pricing now matters as much as performance.

• Anthropic secures record TPU deal with Google — Access to up to one million Google TPUs, bringing over a gigawatt of compute capacity online by 2026, supporting Anthropic's $7 billion revenue run rate and 300,000 business customers. Claude Code alone hit $500 million in annualized revenue within two months of launch. Multicloud isn't optional anymore—it's the price of survival.

⚠️ Agent Security Is Broken

• ChatGPT vulnerabilities let attackers trick AI into leaking data — Researchers disclosed multiple prompt injection attacks including PromptJacking (RCE in Claude's connectors), agent session smuggling via the A2A protocol, and shadow escape zero-click attacks through MCP setups. Cisco found open-weight LLMs vulnerable to multi-turn prompt attacks with 93% success rates as safeguards erode over successive interactions. The industry's betting billions on agents—but the security model is fundamentally unsound. Microsoft predicts 1.3 billion AI agents by 2028, and none of them have a silver bullet for prompt injection.

• OpenAI launches Aardvark in private beta — GPT-5-powered agentic security researcher that autonomously finds vulnerabilities, assesses exploitability, and proposes patches, targeting the 40,000+ CVEs reported in 2024. Defending against AI agents... with AI agents. The irony is not lost on security teams.

🏥 New Frontiers

• OpenAI exploring consumer health AI — Personal health assistants and data aggregation tools in development following strategic hires from Doximity and Instagram, targeting ChatGPT's 800 million weekly users who frequently ask medical questions. Healthcare could be generative AI's killer app—or the graveyard where Google, Amazon, and Microsoft's ambitions went to die.

🚀 Compute Goes Off-World

• Google announces Project Suncatcher — Plans to launch prototype satellites with TPUs in early 2027 in dawn-dusk sun-synchronous orbit for near-constant solar power, potentially producing eight times more energy than ground-based data centers. Starcloud launches Nvidia H100 GPU to orbit this month, projecting 10x cheaper energy costs than terrestrial options and planning 5-gigawatt space data centers. When your models outpace Earth's power grid, orbit becomes the logical answer. The question is whether it's engineering ambition or infrastructure desperation.

📉 Reality Checks

• Tech stocks stumble on valuation concerns — Nasdaq down 3% for the week, worst since April, with AI names leading losses: Nvidia -7%, Oracle -8.8%, Super Micro -23%, as October job cuts hit 22-year highs. The AI infrastructure bet is massive—but markets are starting to price in execution risk versus hype.

• Trump's AI Action Plan removes regulatory barriers — July's 28-page framework fulfilled the January executive order revoking Biden's AI safety requirements, with OMB guidance on "unbiased AI" procurement due November 20 and White House seeking industry input on regulations that "hinder" AI development through October 27. Washington just handed the industry regulatory breathing room. Whether that accelerates innovation or defers necessary safety work remains to be seen.

The Pattern:

The AI stack is consolidating fast: strategic partnerships over proprietary builds, enterprise customers picking their horses, and compute infrastructure moving to space when terrestrial grids can't keep up. But underneath the mega-deals, two cracks are widening—agent security is fundamentally broken with no clear fix, and the market's questioning whether demand will justify the astronomical capital deployed. The next quarter will tell us if we're building transformative infrastructure or just very expensive monuments to optimism.

See you next week. Stay sharp, stay skeptical, and keep building. 🚀

Your Move

Big Tech bet $370 billion on infrastructure that might hit an energy wall in 18 months. You just learned:

• How to audit your business model like a $15K consultant

• Which automation platform gives you enterprise features at startup prices

• How to force AI to score and improve its own outputs

Now implement one.

Most readers will close this tab and do nothing. Don't be most readers.

Reply with which tactic you're testing. I read and respond to every message.

Next Wednesday: More analysis and tools that work while compute is still cheap.

— R. Lauritsen

P.S. Forward this to someone still wondering "is AI a fad?" They need the perspective before their tools vanish.