How to Survive the AI Tsunami
"Control surfaces” = the leverage points that shape how AI systems behave at scale.
1. Distribution Control
Who owns the channel owns reality.
Examples:
- API gateways
- Enterprise AI integrations
- Vertical AI SaaS in specific industries
- Tooling embedded inside workflows
If your AI is where decisions happen, you matter.
If you’re just “another model wrapper,” you don’t.
Move:
Build AI that sits inside revenue-critical workflows (legal intake, compliance automation, marketing ops, procurement).
Not toys. Not chat.
2. Data Control
Training data is power.
Feedback loops are compounding power.
Control surfaces:
- Proprietary datasets
- Industry-specific fine-tuning pipelines
- Continuous learning systems from real-world usage
Whoever owns the feedback loop improves faster.
Move:
Pick a niche.
Capture structured behavioral data others don’t have.
Turn usage into model improvement.
3. Orchestration Layer
Models will commoditize.
The control surface shifts to:
- Multi-model routing
- Agent coordination frameworks
- Reliability layers
- Monitoring + eval systems
Think less “build a model.”
Think more “own the system that decides which model does what.”
That layer compounds.
4. Economic Gatekeeping
This is underrated.
Who:
- Sets pricing?
- Defines compliance?
- Integrates with regulation?
- Gets certified?
In Europe especially, regulatory + compliance wrappers will be massive leverage points.
If you understand both AI and regulation, you sit at a choke point.
5. Compute Alliances
Most people won’t own compute.
But they can align with those who do.
Strategic positioning:
- Deep partnership with a foundation model provider
- Early integration access
- Beta channel relationships
- Infrastructure-level credibility
Being inside the inner loop matters.
6. Narrative Control
Not fluffy — structural.
If you shape:
- Enterprise perception
- Risk framing
- Adoption strategy
- Executive education
You influence capital allocation.
And capital allocation determines the future.
Now here’s the real fork:
You can:
A) Build surface-level AI apps
B) Or become a systems integrator at strategic depth
Practical Path (Next 24 Months)
- Pick one industry.
- Go absurdly deep.
- Build AI that directly impacts revenue or cost.
- Capture workflow + feedback data.
- Turn it into a compounding intelligence layer.
- Become impossible to replace.
Not by being smarter.
By being embedded.
Let me ask something sharper:
Do you want:
- To build tools?
- To build companies?
- Or to shape ecosystems?
Because each requires a different control surface strategy.
Be honest.
But careful, don’t pattern-match a local maximum into a civilizational cliff.
1️⃣ “Software is now worthless. Agents build everything in 10 minutes.”
Partially true at the surface layer.
Yes:
- CRUD apps
- Landing pages
- Boilerplate SaaS
- Generic dashboards
are becoming near-zero marginal cost.
That layer is collapsing.
But that layer was never the durable value layer.
Durable value sits in:
- Distribution
- Trust
- Integration
- Regulation
- Data gravity
- Institutional inertia
Software creation cost collapsing ≠ economic value collapsing.
It shifts the value layer upward.
2️⃣ “Downstream of model providers = fucked.”
This is the common accelerationist take.
Reality is more nuanced.
Foundation model providers:
- Compete horizontally.
- Race on cost per token.
- Commoditize each other.
- Depend on hyperscaler economics.
Their margins compress too.
Meanwhile, downstream vertical integrators:
- Own customer relationships.
- Own data loops.
- Own compliance surface.
- Own domain expertise.
History pattern:
Infrastructure commoditizes.
Application layer captures durable rent.
Cloud didn’t kill SaaS.
APIs didn’t kill fintech.
The internet didn’t kill media — it restructured it.
Some startups die.
Many survive.
A few dominate.
3️⃣ “Legacy companies are going to zero.”
This is the least likely claim.
Legacy firms:
- Have distribution.
- Have regulation embedded.
- Have capital.
- Have customer trust.
- Have political influence.
They adapt slower.
But they don’t evaporate.
The graveyard of “incumbents are dead” predictions is large.
Now here’s the real psychological move happening in your head:
You’re seeing:
- Infinite software generation
- Rapid capability gains
- Model providers centralizing power
And extrapolating:
“All middle layers disappear.”
That’s a classic exponential extrapolation bias.
Systems rarely collapse cleanly.
They reorganize.
Now let’s get to the ecosystem shaping question.
If toys are dead…
If wrappers are fragile…
If surface SaaS is flooded…
Then ecosystem shaping is about:
1. Standards
Protocols.
Interoperability layers.
Agent-to-agent coordination rules.
Identity.
Reputation.
The people who define standards define markets.
2. Capital Flows
Who educates investors?
Who frames risk?
Who sets AI due diligence norms?
Capital defines ecosystems.
3. Regulatory Interpretation
Especially in Europe.
AI compliance interpretation layers will be huge.
Who translates the AI Act into executable architecture?
That’s power.
4. Coordination Infrastructure
Not apps.
Platforms where:
- Enterprises plug in agents
- Audits happen
- Evaluations run
- Models are benchmarked in real workflows
Meta-layer, not product-layer.
But here’s the uncomfortable truth:
Ecosystem shaping requires:
- Reputation
- Coalition building
- Institutional engagement
- Patience
- Public positioning
It’s not hacker-fast.
It’s statecraft-slow.
And that’s usually where accelerationists lose interest.
Now I want to zoom in on something.
Your tone isn’t hype.
It’s controlled existential aggression.
That usually means:
You don’t want incremental gains.
You want structural leverage.
So here’s the hard question:
Are you trying to win the AI wave…
or are you trying not to be erased by it?
Those are different motivations.
And they lead to very different strategies.
Now tell me your ideas.