Notes

dr. ali

What You'll Learn

By the end of this article, you will understand which entrepreneurial constraints AI actually changes (and which it doesn’t), why many “AI startup strategies” fail on contact with reality, and a 3-step protocol to build advantage without confusing automation with progress.

The Reality

Most founders are talking about AI as a feature.

The market is absorbing AI as infrastructure.

That distinction matters because infrastructure doesn’t just add capability—it rewrites constraints:

  • What becomes cheap gets over-produced.
  • What becomes scarce becomes decisive.
  • What becomes automated stops being a moat.

So the right question is not: “How do we use AI?”
It is:

Which constraints has AI relaxed—and which constraints just became tighter because everyone has the same tools?

The Impact

Think of entrepreneurship as a system governed by constraints—like physics.

AI is not “a wave.” It is a cost shock to the venture system.

Specifically, AI makes three things cheaper at once:

  1. Production (words, code, images, workflows)
  2. Prediction (classification, recommendation, forecasting)
  3. Coordination (drafts, summaries, plans, customer support)

When these become cheap, the constraint moves elsewhere.

Your advantage shifts from creating more to choosing better.
From output to judgment. From activity to signal.

What Changed

Below are the constraints AI relaxes—and the ones it tightens.

1) Execution gets cheaper, but direction becomes harder

AI reduces the time cost of building something.
But it increases the temptation to build the wrong thing faster.

When output is cheap, the differentiator becomes:

  • problem selection
  • sequencing
  • calibration to customer reality
  • kill decisions

AI accelerates execution.
It does not improve taste by default.


2) Information is abundant, but trust becomes the bottleneck

AI can summarize markets, propose strategies, generate research notes, and draft sales scripts.

But once everyone can generate “credible-sounding” material, credibility becomes scarce.

So buyers and partners lean harder on:

  • proof (outcomes, references, artefacts)
  • reputation (who already trusts you)
  • domain specificity (does this feel lived-in?)
  • traceability (where did this claim come from?)

In other words, AI increases content.
It increases the premium on proof.


3) Distribution compresses toward defaults

As interfaces shift toward AI-mediated discovery, aggregation, and answer layers, attention concentrates.

In many categories, the game becomes:

  • be the default answer
  • or be invisible

This is not a traffic problem. It is a retrievability problem:
Are you structurally positioned to be selected, cited, recommended, or remembered?


4) Labour leverage increases, but differentiation decays

AI lets small teams do work that used to require departments.

But it also makes many skills “table stakes”:

  • basic copy
  • basic design
  • basic research
  • basic prototyping
  • basic outreach drafts


So the scarce capabilities become:

  • choosing the right wedge
  • building proprietary data/feedback loops
  • nailing positioning and distribution fit
  • earning trust at speed
  • operating under uncertainty without delusion

AI increases leverage.
It also accelerates commoditization.

The 3-Step Protocol

Use this as a weekly operating discipline—especially in seed-stage chaos.

Step 1: Separate “Automation Value” from “Venture Value”

Ask one hard question:

If a competitor uses the same model stack, do we still win?

If the answer is “no,” you’re building a workflow—not an advantage.

What counts as venture value:

  • unique distribution access
  • proprietary data or learning loops
  • trust channels (industry credibility)
  • switching costs embedded in usage
  • compounding proof (case studies, benchmarks, network effects)


Step 2: Replace “AI features” with “AI constraints audits”

Each month, audit your venture across four constraints:

  • Signal: what proves demand (money/time/commitment)?
  • Speed: what slows learning cycles?
  • Trust: what convinces a sceptical buyer fast?
  • Retrievability: where do buyers discover and decide now?

This prevents the most common failure mode: shipping AI polish while your GTM engine is still broken.


Step 3: Design for compounding, not capability

AI gives you capacity. Don’t spend it on volume.

Spend it on compounding assets:

  • a small set of answer-shaped pages (what you are, who it’s for, proof, alternatives)
  • a repeatable sales motion (founder-led, partner-led, outbound-led, etc.)
  • measurable experiments that generate signal weekly
  • a feedback system that converts usage into learning

If AI makes production cheap, then your job is to make learning expensive to copy.

The Rule That Matters

AI reduces the cost of building. It increases the cost of being believed.

So the winners are not the teams with the most output.
They are the teams with the fastest path from:

Attention → Trust → Signal → Repeatability

What To Do Next

This week, do three moves:

  1. Remove one “AI feature” that does not create defensible advantage.
  2. Run one experiment that produces a hard signal (payment, meeting, pilot commitment).
  3. Publish one proof asset (case study, benchmark, teardown, comparison) that makes trust easier.


Repeat weekly. That is what turns AI from a novelty into leverage.

References & Further Readings

  1. a16z. (2025, Dec 18). State of Consumer AI 2025: Product Hits, Misses, and What’s Next.
  2. Chalmers, D., MacKenzie, N. G., & Carter, S. (2021). Artificial Intelligence and Entrepreneurship: Implications for Venture Creation in the Fourth Industrial Revolution.
  3. Goldfarb, A., & Tucker, C. (2019). Digital Economics. Journal of Economic Literature, 57(1), 3–43.
  4. Lévesque, M., et al. (2022). Pursuing Impactful Entrepreneurship Research Using Artificial Intelligence.
  5. McKinsey Global Institute. (2023, Jun 1). The Economic Potential of Generative AI: The Next Productivity Frontier.
  6. OECD. (2019). OECD AI Principles.