In new generation SaaS companies, CTO responsibility is no longer limited to arbitrating technical choices. It now implies a constant alignment between Applied research,, Product construction And Development rate. The integration of AI bricks – generation models, language treatment, or multimedia synthesis – requires faster, more risky iterative cycles, more demanding in strategic arbitrations.
Build without over-engineering: the logic of the offensive prototype
The first temptation of a CTO from a classic technical culture is to want to make “clean” too early. However, in AI, the opportunity cost of a too long R&D cycle is high. The market evolves quickly, user expectations change in a few weeks, and the models themselves are often obsolete in less than six months.
In this perspective, it is often more profitable to Prototyper quickly with available brickseven if it means reworking architecture once the potential has been validated.
Some ctos testify to having won several months of development By exploring little visible GitHub deposits, forks of research papers or un -docked but functional contributions. This approach, which combines Active watch, opportunistic engineering and pragmatismbecomes a skill in its own right.
Lesson 1 : Start with the result, industrialize then. What matters first is to demonstrate that the product can generate use, not that architecture is flawless.
Identify real technical locks
A well -led project is based on an ability to distinguish what is complex from what is blocking. Certain technical problems, although delicate, can be bypassed or masked in a MVP. Others-in particular those linked to the fidelity of outputs, spatio-temporal coherence or personalization-require deep treatment.
The good cto will know isolate the three to five critical locks To be resolved to achieve acceptable quality of production. This approach supposes a Clear priority of build prioritiesbut also an ability to cut efforts when they do not produce tangible advances.
In some cases, an identified lock on labial synchronization or the style of expression in a generated video mobilized the entire IA team on cycles of several weeks, with a fine measure of progress and output thresholds defined in advance.
Lesson 2 : Fight against dispersion. The technical time is precious. It must be reserved for locks which condition the quality perception or the viability of the product.
Integrate the product team into the search loop
In a project mixing AI and product, The link between R&D engineers and the product team cannot be transactional. It must be continuous.
Each progress on the model – reduction of latency, gain in consistency, reduction in inference costs – must be Immediately integrated into the product cycle. Likewise, each user return, each identified friction, each limit perceived on the forehead must be transferred as technical input.
This integration logic requires a team culture where engineers include uses and where PMs master the constraints of the IA engine. Without this double movement, the risk is great to build an efficient but unusable model, or an attractive interface backed by an unstable brick.
Lesson 3 : The interface is not a layer higher than the model. It is a mirror. And both must be thought of as a single entity.
Align build speed and robustness
A frequent error in the scaling phase is to want to speed up growth without having secured technical foundations. Conversely, other CTOs voluntarily slow their roadmap business to stabilize their IA model.
The truth is in an evolutionary balancewhich varies according to the stages:
- In phase 0 → 1: we are looking for a satisfactory output, proof that the engine produces a noticeable effect.
- In phase 1 → 10: we structure the inference chain, we measure latency, stability, cost by generation.
- In phase 10 → 100: we work scalability, monitoring, the quality perceived on hundreds of cases per day.
Each phase requires a distinct tempo, but all require the same discipline: Do not accelerate the marketing to the detriment of the reliability of the technical heart.
Lesson 4 : Technological endurance takes precedence over the demo effect. Users forgive a sober interface. They do not forgive a false response or incoherent behavior.
Accept the part of random and navigate uncertainty
Building an AI product is working With a moving science, unstable standards, and benchmarks in permanent recomposition.
Much progress is not linear. It happens that a three -year -old paper contains an ignored solution. That a poorly documented fork unlocks a major issue. That a minor change in the drive distribution drastically modifies the quality of an output.
The CTO in this context is not a rigid conductor but acts rather as an agile strategistcapable of repositioning efforts in real time, questioning a track, or accelerating suddenly on a technical opportunity.
Lesson 5 : The determining factor is not to master everything, but to learn faster than the others. Organizational learning becomes a competitive advantage.
The IA era requires CTO a continuous hybridization between science, product and strategy. It is no longer the power of the engine that makes the difference, but its ability to deliver a concrete promise, at the right time, and in a controlled use.
