Productivité & Carrière

AI Entrepreneurship in Practice: The Code and Commands That Really Matter

AI Entrepreneurship: The Essentials in One Article — Real Code, Diagrams and Concrete Steps, Excerpts from a 35-Lesson Course.

REHOUMA Haythem

• 13 min read
AI Entrepreneurship in Practice: The Code and Commands That Really Matter

No endless theory here: open the terminal and practice. Here is the essence of AI Entrepreneurship, extracted directly from a complete 35-lesson course — with real code you can copy-paste right now.

tl;dr
  • Introduction
  • Find the idea
  • AI Business model
  • AI MVP in 30 days
  • First clients
~$ cat ./parcours.md # AI Entrepreneurship — 10 chapters
01
Introduction
→ Course presentation→ Landscape of AI startups in 2026+ 1 more lessons
02
Finding the idea
→ Detecting real problems→ Frameworks to validate an idea+ 1 more lessons
03
AI Business model
→ Business Model Canvas applied to AI→ SaaS, API, marketplace and hybrid models+ 1 more lessons
04
AI MVP in 30 days
→ Defining the MVP scope→ No-code and low-code for prototyping+ 1 more lessons
05
First clients
→ Customer Development — 30 interviews→ B2B sales for AI startups+ 1 more lessons
06
Fundraising
→ VC landscape, business angels and corporate VC→ Building a compelling pitch deck+ 1 more lessons
07
Early-stage team
→ Cofounders and equity split→ Recruiting the first key people+ 1 more lessons
08
Growth
→ Customer acquisition for AI startups→ Product-led growth (PLG)+ 1 more lessons
🏁
Final project (+ 2 chapters along the way)
→ You leave with a concrete and demonstrable project

Create the pitch deck

NOTEObjective — Create the 10-slide pitch deck that will accompany your presentation to investors. The deck is a visual aid, not a book: little text, high impact.

Learning objectives

TIPAt the end of this module — You will have designed a 10-slide deck in 16:9 format, clean design, content consistent with your business plan.

Golden rules of the AI startup deck

NOTE
  • 1 slide = 1 idea — Never overload
  • Max 10 words per slide — Text is a support, not a script
  • 1 visual per slide — Diagram, figure, screenshot
  • “Confidential” mention — In the footer
  • Footer with slide number — For navigation

Structure of the 10 slides

Here is the standard validated by AI VCs in 2026:

#SlideMain message
1CoverName, tagline, your photo, contact
2ProblemThe pain point in 1 sentence + 3 figures
3SolutionWhat you do in 1 image
4Why nowTailwind (AI, regulation, market)
5MarketTAM SAM SOM in 1 visual
6Product2-3 screenshots or demo
7TractionMRR, clients, % growth per month
8TeamPhotos, names, key experience
9Business modelACV, margins, 3-year projections
10AskAmount raised, use of funds, milestones

Slide 1: Cover

First impression. Must make people want to turn the page.

Slide 2: Problem

Must create empathy. The investor must nod.

TIPTemplate — “The [target customer] spend [X hours] per week on [painful task]. It costs [Y k€/year] and creates [Z problems].”

Slide 3: Solution

How you solve the problem, in 1 image and 1 sentence. Ideally an “Before / After” diagram.

NOTETip — If you have a 30-second demo, it is better than a long speech. Prepare a 15-30 sec backup video.

Slide 4: Why now?

This slide is crucial in AI. The VC wants to understand why your startup could not have existed 3 years ago.

TIPExamples of “Why now”
  • Inference cost divided by 50 in 2 years
  • Technological maturity enabling production-grade
  • Regulation (AI Act) creating a need
  • B2B adoption (90% of CIOs have an AI budget in 2026)
  • Competitor or market in transition

Slide 5: Market

TAM SAM SOM in 1 diagram. Present as concentric circles or as a staircase.

WARNINGTo avoid — TAM too broad (“AI in general is $1.5 trillion”). It is false and damages your credibility.

Slide 6: Product

2-3 screenshots of your real product (or designed mockup if you are pre-product). Highlight:

Slide 7: Traction

The most important slide if you are post-product. Show:

Myths and realities of AI entrepreneurship

NOTEObjective — Deconstruct the 10 most dangerous myths about AI entrepreneurship to prevent you from wasting months chasing illusions. You will leave with a more lucid and effective vision.

Learning objectives

TIPAt the end of this module — You will be able to detect the false promises of the dominant AI discourse, make strategic decisions based on market reality, and avoid the classic traps of beginner founders.

Myth 1: “AI will replace all jobs in 2 years”

Reality — AI augments more than it replaces. In 2026, the jobs that have truly disappeared can be counted on one hand. Code copilots have not killed developers, text generators have not killed writers: they have changed the nature of work.

WARNINGConsequence for you — If your pitch is “we replace humans”, you will sell to no one. Customers buy help for their teams, not the elimination of their teams. Rephrase: “we make your team 3 times more productive”.

Myth 2: “All you need is a good idea”

Reality — Good ideas are a commodity. Marc Andreessen, founder of a16z, says it clearly: “Ideas are cheap, execution is everything.” For every startup success, there are 50 founders who had the same idea but did not deliver.

Example: before Perplexity, dozens of teams tried the “search GPT”. Aravind Srinivas won because he shipped fast, iterated faster, and recruited better.

TIPConsequence for you — Do not spend 6 months looking for the perfect idea. Take a “good enough” idea, start talking to customers, and adjust. Execution quality will matter 100 times more than initial idea quality.

Myth 3: “You must train your own model to have a moat”

Reality — False. Training a model costs €50 to 500 M. No beginner founder can do it. The moat of AI startups in 2026 does not come from the model, it comes from:

WARNINGConsequence for you — Build on the APIs of OpenAI, Anthropic or Mistral. Focus your energy on the application layer, the workflow and distribution.

Myth 4: “You can raise easily with the magic word AI”

Reality — In 2023, yes. In 2026, VCs are saturated. They see 20 AI deals per week. To raise, you must prove:

What VCs want to see in 2026

What no longer works

Myth 5: “You must be in San Francisco”

Reality — False. Mistral was founded in Paris. Cohere in Toronto. Stability AI in London. ElevenLabs also in London. Hugging Face is Franco-American. The best European AI startups are worth tens of billions.

The SF advantage is real (talent density, capital) but has a cost: salaries x2, rents x3, fierce competition for recruitment. In Europe, you pay less, you keep more equity, and you access public grants (BPI, Horizon Europe).

TIPConsequence for you — Stay where you are if you have an advantage there (network, talent, customer market). But target the global market from day one: sell in English to US and UK companies even from Paris or Lyon.

Myth 6: “The AI hype will last 10 years”

Reality — Hype cycles typically last 2 to 4 years. The peak of AI hype was 2023-2024. In 2026, we enter the consolidation phase: VCs start asking for profitability, multiples compress, some startups will die.

Stability AI, which was worth $1 B in 2022, almost went bankrupt in 2024 due to lack of revenue. Inflection AI was acquired by Microsoft for its talent in 2024. The sorting is happening.

WARNINGConsequence for you — If you launch in 2026, you must build a real company, not an overvalued proof of concept. Seek profitability early, not growth at all costs.

Myth 7: “Autonomous agents will do everything in 2026”

Co-founders and equity split

NOTEObjective — Learn how to choose the right co-founder(s), split equity fairly and sustainably, and set up protection mechanisms in case of departure.

Learning objectives

TIPAt the end of this module — You will know how to detect a good co-founder, avoid classic traps, negotiate a fair equity split, and structure vesting to protect the company.

Why co-found rather than go solo?

According to data from Y Combinator and CB Insights, startups with multiple co-founders succeed 2 to 3 times more often than solo startups. Three reasons:

WARNINGExcept in rare cases — Some solo founders succeed (Pierre Omidyar for eBay, Tobias Lütke for Shopify) but they are rare. If you are solo, know that seed/A VCs will insist you find a co-founder or a very senior “founding employee”.

The ideal co-founder profile

Look for someone who:

Complementary skills

Value alignment

Character and resilience

Pre-existing relationship

Successful profiles: real examples

AI StartupCo-founder composition
Mistral AI3 co-founders ex-Meta/DeepMind, 3 ML expertises
Anthropic7 co-founders ex-OpenAI: Dario, Daniela Amodei + research
SierraBret Taylor (ex-Salesforce) + Clay Bavor (ex-Google)
Hugging FaceClément Delangue (business) + Julien Chaumond (tech) + Thomas Wolf (ML)
HarveyWinston Weinberg (lawyer) + Gabriel Pereyra (ex-Meta ML)
GranolaChris Pedregal (product) + Sam Stephenson (tech)
TIPObservation — All these startups have at least 2 co-founders with clearly different expertises. The dominant pattern: business/product profile + technical/research profile.

How many co-founders?

2 co-founders

The standard. Shared workload, faster decisions, well-balanced equity. Excellent formula.

3 co-founders

OK if each has a clearly distinct role. Risk: slower decisions, 1 majority vs 2 minorities.

4+ co-founders

Rare and complicated. Except in exceptional cases like Anthropic, avoid. Excessive equity dilution, governance chaos.

The equity split

This is one of the most emotional decisions. A few principles:

WARNINGGolden rule — If the split is very unbalanced (90/10), ask yourself whether the other person is really a co-founder or rather a “founding employee”. If it is an employee, give them 1-5% in options and call things by their name.

The “Slicing Pie” tool for splitting

go-further

This article covers the most useful excerpts — the complete AI Entrepreneurship course (11 chapters, 35 lessons, corrected exercises and final project) takes you all the way.

./access-the-full-course free course: Claude Cowork

FAQ

How long does it take to learn AI Entrepreneurship?
With a structured progression (11 chapters, 35 short and practical lessons), you reach an operational level in a few weeks at 30 to 60 minutes per day. The key is to practice each concept immediately.
Are there any prerequisites?
Basic computer science knowledge is enough. If you can use a terminal and read simple code, you are ready.
Where to start concretely?
Reproduce the commands in this article, then follow the full AI Entrepreneurship course: it chains the 35 lessons in order, with exercises and a final project.
./read-also
→ Get started with Excel Copilot: your first concrete step today→ Essential AI Toolbox: the 9 key steps to go from zero to operational→ AI Career Opportunities explained simply (with diagrams and real code)

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