For almost a decade, a startup’s journey seemed clear, marked out by the principles of Lean Startup, namely to identify a real problem, quickly launch a MVP, validate market interest and iterer until finding a product-market Fit. A proven recipe, where the execution capacity often prevails over the technical sophistication of the product itself.
But in 2025, the landscape changedly changed. The maturity of the generative AI, the explosion of No-Code/Low-Code tools, the rise of autonomous agents and the spectacular fall in the prototyping cost radically upset the first weeks of a young shoot. The traditional path “problem → solution → product” has reversed.
The product before the problem
From now on, entrepreneurs no longer start their reflection with the validation of a specific problem. They start by exploring what technology allows them to do. Design an AI agent in a few hours, test a personalized assistant for a weekend or deploy an API integration from the first day: the speed of technological execution transforms the initial step of a project into a real experimentation laboratory.
This methodological reversal is not only a marginal adjustment, but a deep change in logic. Today, we are experimenting to discover the field, even before having defined it precisely.
Prototyper becomes easy, distributing becomes critical
Technical development is no longer the main obstacle, the major challenge is now in direct access to users. While the tools to create are abundant, distribution remains the real limiting factor. Attracting users, ensuring their activation, then retaining them became the central issues from the first days.
This change of balance brings back strategic fundamentals in the foreground, too often neglected in recent years:
- A strong brand identity, immediately recognizable;
- Native integrations in essential business tools such as Slack, Concept or Google Drive;
- A canal strategy clearly thought upstream, combining targeted influence, structured SEO, and professional ecosystems.
Distribution is no longer a distinct step of the product. It is an integral part of it from the design.
AI requires a return to immediate profitability
The era of “Scale at all costs” is over. The AI models, despite their democratization, remain expensive on a large scale, imposing increased financial discipline from the launch. If the Freemium model persists, it is now framed by very concrete constraints, so the free offer must immediately demonstrate a tangible value, while naturally pushing the user to a paid version.
From the first weeks, the founders are thus led to ask themselves crucial economic questions:
- What is the exact cost per user?
- From what limit should the offer switch to a paid model?
- What element, beyond simple access to an LLM, justifies the subscription?
The end of the “all AI” model, the victory of verticalization
Creating a simple interface around an open-source model or a GPT API is no longer enough to constitute a durable competitive barrier. In 2025, the real competitive advantage comes from very concrete elements that surround the AI:
- The impeccable quality of the user experience;
- Deep and natural integration into business tools and workflows;
- A real sectoral expertise.
This return to a vertical logic promotes startups which intimately include their users, as proven by companies such as Dust (Co -pilot for operational teams), Giskard (quality tools for AI) or Legora (IA solutions dedicated to lawyers).
Towards a new product culture
This transformation does not stop at methods, and shapes a new product culture. The most efficient teams are no longer distinguished only by their speed of pure execution, but above all by their ability to combine three key assets:
- Rapid prototyping centered on AI coupled with a solid product vision;
- An exemplary mastery of experience design, capable of making complex technologies simple;
- A rigorous management of profitability from the first user interactions.
Building a product has never been so technically accessible, but obtaining its adoption, succeeding in monetizing it and retaining users now requires increased strategic accuracy and accuracy.
| Criteria | Playbook 2015 (Lean Startup) | Playbook 2025 (IA-STIF) |
|---|---|---|
| Starting point | Prior identification of a market problem | Immediate product experiment (before the problem) |
| Product prototyping | Fast, often minimalist and “tinkered” | Functional AI demonstrator from the start |
| Main challenge | Technique, development, rapid execution | Distribution, acquisition, activation and user loyalty |
| Economic strategy | Privileged rapid growth, long -term profitability | Profitability and management of the margin from the first stages |
| Differentiating element | Execution speed and ability to iterate quickly | Quality of quality, deep business integrations, strong verticalization |
| User approach | Progressive validation in the field after design | Direct and immediate access to the user, an integrated product from the design |
| Freemium model | Generalized free use, often late monetization | Limited free use, demonstrable value to convert quickly |
| Dominant technologies | Internal development, often expensive | GENERATIVE, NO-CODE/LOW-CODE, API, open-source models |
| Reference examples | Dropbox, Airbnb, Uber | Dust (IA Co -pilot), Giskard (IA quality), Legora (AI for lawyers) |
Dust: Concrete illustration of the new Playbook 2025
Dust perfectly embodies this new logic. Launched by Stanislas Polu (ex-Openai, Stripe), this startup was focused on the rapid creation of an operational IA demonstrator integrated into Slack and concept, immediately targeting the Tech and Product teams. Its early distribution strategy allowed it to quickly attract users to companies like Alan, where Dust generates a demonstrable value from the first days of use. (see our case study at the end of the article)
AI does not simplify anything, it moves complexity
The Playbook Startup 2025 is not a brutal break with previous approaches. Rather, this is a fundamental update of what a business is means to launch today. Fewer theories beforehand, more direct experimentation, less rudimentary MVP, more successful IA demonstrators, less excess of massive levees, and an immediate return to financial discipline.
The AI has not facilitated the work of the founders, it imposes an increased requirement on distribution, verticalization, and profitability from the first days. The challenge for startups is no longer to prove what they can build but why, how, and especially for whom they build it.
Case study: Dust, AI at the heart of the teams
Founded by Stanislas Polu (formerly at Openai and Stripe), Dust offers an AI assistant directly integrated into the daily workflows of the teams, making it possible to transform internal data into levers of operational efficiency.
The starting point: a product above all
Dust is not part of a long prior reflection on a specific problem. On the contrary, his founding team started by exploring what it was possible to do directly by combining the capacities of large language models (LLM) with the internal data of a company. The market validation only came after, from a functional demonstrator from the first days, presented to real users.
Product strategy: verticalization and native integration
Dust’s great strength lies in the quality of direct integration with daily business tools such as Slack, Concept, Github or Google Drive. Instead of offering a generalist product based solely on a standard LLM API, Dust builds extremely specific integrations, with an intuitive UX which masks all the underlying complexity.
Distribution: an immediate strategy
Distribution at Dust has never been a second time in product development. From the start, Dust adopted a quick access strategy to its users thanks to:
- A very clear positioning with a specific audience: the operational and technical teams of tech companies.
- An approach combining targeted influence with technical teams (in particular by the personal reputation of the founder) and native integrations.
- Marketing based above all on the concrete demonstration of the immediate operational results obtained by the first users.
The economic model: immediate return to the margins
If Dust does not offer a completely free and limitless offer, the startup very early joined the logic of a limited freemium model. The user can easily test the product on a sample of data or features, but full access is quickly paid. This logic allows Dust to control its cost of use, high by nature because of the generative AI.
Concrete results:
- Quick adoption with technological teams and produced in companies like Alan or Qonto.
- Demonstrated ability to quickly justify a paid model by offering measurable value on specific jobflows.
- Rapid construction of a competitive advantage (“Moat”) centered on business integration depth and user experience, rather than gross access to AI models.
“Dust allows us to use our internal data directly in our daily tools. We immediately gain in efficiency without having to manage the technical complexity ourselves, ”explains Kevin Bourgeois, Head of Product Engineering at Alan (Source: Dust, Customer Story Alan).
