The models keep improving. The value doesn’t follow.
Xavier Vallée, Founder, Sapionic
Written as a reply to another post on LinkedIn.
This nails something most of the AI conversation is missing and that I’ve talked about before: RL environments.
The model wars make great headlines. But in the trenches of enterprise AI transformation, the constraint is shifting from intelligence to infrastructure: the infrastructure that turns intelligence into reliable action.
The AI projects that will deliver real value won’t win because they picked the smartest model. They’ll win because the team built the environment around it: the data pipelines, the feedback loops, the reward signals that let the system learn what “good” actually looks like in a specific business context.
Companies aren’t just missing high-fidelity training environments. They’re missing the data architecture to feed them. Everyone is busy cleaning data for Supervised Fine-Tuning, curating perfect input/output pairs so AI can imitate human work. That’s important to stay in the race but not enough to win it.
The real pivot is logging trajectories for Reinforcement Learning: the state before the decision, the action taken, and critically, the downstream results: the financial or operational outcomes.
Most data lakes are completely missing that reward signal. Without it, even the best environment can’t produce trustworthy learning.
SFT teaches your AI to play the game. RL teaches it how to win. But you need the scoreboard wired up first.
If you’re planning your AI roadmap this year, don’t just invest in model capability. Start building the foundations that will make capability reliable. That’s where durable enterprise value will be created.