I remember sitting in a boardroom as we debated which AI projects to keep and which roles to cut. It felt like watching the tide pull out before a storm—calm at my ankles while a wall of water raced in elsewhere. That image stuck with me. In this mini-guide I share the exact playbook I use in those meetings: simple checks, a clear map of where money flows in AI, and the mindset to survive and thrive.
1) The AI Tsunami: Why the Panic Feels Real
In boardrooms, I feel the tension—like the tide pulling back before a tsunami. People whisper about AI trends 2026 and that the “AI bubble is about to burst,” but the disruption is already here: last year, 650000000 jobs disappeared. I’ve seen this cycle in real estate and crypto; AI just moves faster—like sensors showing a wall of water racing in at 500 m/hour. Early positioning wins, crash or not, because AI jobs automation keeps spreading.
Le, co-founder of Google's Deep Mind, says: "If your done on a laptop, you're competing with AI that costs pennies."
I feel both inevitability and agency: I can still Futureproof career by moving to high ground.
2) How to Tell If You're Standing in the Flood Zone (RAIL Checklist)
I use RAIL—four questions in ~50 seconds—to spot risky AI experimentation real ROI.
- R—Revenue: Are real customers paying now? “We’re still piloting” is a red flag.
- A—Acceleration: Can we ship meaningful value in two weeks? This speed is a proxy for competitiveness in AI workflows automation productivity.
- I—Internal: If it’s internal, is it in real users’ hands, gathering data, not sitting on a shelf?
- L—Learning: Are customers breaking it and teaching us fast?
If you are leading a project with no clear revenue path, you're going to be the first one on the chopping block.
Rule: No to 2+ = flood zone.
3) The Map: Three Layers Where the Money Lives
I ignore orchestration noise and track three layers.
AI infrastructure efficiency
Layer 1 is infrastructure: fabs, tensor chips, cloud, and energy. It’s a massive-capital game; without Nvidia-level scale or sovereign backing, newcomers struggle.
Frontier versus efficient models
Layer 2 is frontier models (GPT, Claude, Gemini, xAI) plus tooling. It’s consolidating fast, and AI buyer's market commoditization is real: price per token fell 98% in 18 months, with cheap open-source close behind.
Apps & Services
Layer 3 is the interface where value is captured—like PayPal on the dotcom rails.
"This layer is where the value is captured. If you're starting something on your own, this is where you should focus."
4) Where to Build: Horizontal Apps, Vertical Apps, and AI Services
Horizontal AI apps
Horizontal AI apps serve everyone (Gamma, Perplexity). The market is huge, but I’m fighting distribution and monetization: I need millions of users to win.
Vertical AI apps
Vertical AI apps go deep (Cursor for developers, OpenEvidence in medicine—used by ~40% of US doctors daily). Smaller markets, but defensibility comes from domain rigor.
AI services implementation
AI services implementation can pay fast: human-in-the-loop work (Scale AI) and firms charging ~$50,000 to plug AI into workflows.
“Companies plug AI into their workflows. And they're not building software. They're just implementing it.”
5) Avoid the Potholes: Layoffs, Early Adopters, and What Gets Cut First
In the AI workforce transformation, the first layoffs often come from early adopters—Microsoft, Amazon, Meta, Google—cutting non-AI roles to fund an AI talent shortage upskilling push. If I’m paid to keep the status quo, I’m in the flood zone.
- Audit my week: scripted (data processing, meeting summaries, boilerplate emails, basic code) vs strategic judgment.
- AI workflows automation productivity: templates, macros, AI assistants; ship one automation every two-week sprint.
- Use the saved time for deep, undistracted problem-solving.
“Automate all of it before your boss does it for you.”
6) The Three Rs: Rigor, Relationships, Resilience
Rigor in AI + AI governance regulatory compliance
Hundreds of healthcare AI startups exist, but OpenEvidence stands out: worth $3.5B and used by 40% of US doctors daily because it was built with peer-reviewed rigor and edge-case testing. BCG/Harvard found GPT-4 users with domain depth got 40% better results vs 12% without.
Relationships
Late-night talks with a colleague helped him later become CEO of a $150M company—proof networks beat resumes.
Resilience + Change fitness organizational resilience
I train for setbacks like Jobs and Mandela: adapt, pivot, stay in the game.
7) My Three-Step Playbook (Audit, Automate, Daydream)
AI workflows automation productivity
Audit: I list daily tasks and tag them scripted (data processing, meeting notes, standard emails) or strategic (new problems, edge cases, client architecture). If 2+ RAIL answers are “no,” I’m in the flood zone.
Automate: I “fire myself” from scripted work using AI assistants, macros, and prompt templates, then run two-week sprints to prove AI experimentation real ROI.
Daydream: I block a weekly no-meeting afternoon and take long walks—deep silence that builds Change fitness organizational resilience.
“I work less on execution now and more on daydreaming—deliberate deep work that produces strategic insight.”
8) Wild Cards, Analogies, and Thought Experiments
When AI feels like a wave, I remember my teacher:
"You still don't understand, do you? ... You're water. You are the ocean."
Small language models SLMs: 60% by 2026?
If SLMs do 60% of tasks, my edge is relationships, rigor, and resilience.
Models won't matter anymore?
If models commoditize, systems and integration win—like dotcom apps built on shaky fiber.
Hybrid computing advancement wild card
What if quantum-hybrid compute and robotics arrive early?
Daydreaming as work: I sketch one weird workflow daily.
Run a two-week customer pilot to test RAIL.
9) Conclusion: A Personal Commitment and Practical Checklist
AI is changing fast, but the substance of good work stays: rigor, relationships, resilience. For my Futureproof career, I’ll automate meeting notes and reports, call two trusted peers about Enterprise AI adoption this week, and block three hours of deep work every Friday to guide my AI workforce transformation. Immediate experiments beat perfect predictions, so I’m moving to high ground now.
- Run RAIL
- Pick app/service path
- Automate 30% scripted tasks
- 3 hours/week deep work
“There’s still time—start with one two-week experiment and build from there.”
Share one experiment, and subscribe so others may find us. Let the tsunami come; we’ll survive and thrive.
