If every company has access to the same frontier models, where does differentiation come from? The answer: it comes from what a model knows about your business specifically. Your data. Your decision logic. Your institutional history encoded directly into its weights. In our latest article for MIT Technology Review, Barry Conklin explains how the organizations seeing the most impressive results are the ones building domain-specialized AI. Whether it's an automotive company training models on crash-test data or a Southeast Asian government building AI in regional languages, under local governance—the common thread is treating model customization as infrastructure, rather than a one-off experiment. Generic intelligence is a commodity. Contextual intelligence is not. Read more: https://lnkd.in/eW8HjGFu
AI Executive
1mo
Yes! The shift from "we use AI" to "we've embedded our institutional knowledge into AI" is the clearest competitive moat forming in the market right now. Encoding decision logic and institutional history directly into model weights is, in effect, the AI equivalent of a proprietary process. Competitors can access the same frontier models, but they can't replicate your data, your context, or your accumulated organizational knowledge. The practical implication for executives: your AI strategy shouldn't be evaluated on which models you're using. It should be evaluated on how much of your organization's irreplaceable knowledge is making its way into those systems. This gap creates a durable advantage.
DevExplore
1mo
It’s so true, the real shift happens when you stop treating AI as a generic tool and start treating model customization as foundational infrastructure. While anyone can use a frontier model, true differentiation comes from the domain-specific logic and institutional history you encode directly into it. In a world of commodity intelligence, your own data is the only context that actually matters.
Martin irwin
1mo
Contextual intelligence may not be a commodity. But the visibility around it should not remain a black box. If AI is increasingly shaped by business-specific data, decision logic, and institutional history, then the user should be able to see more clearly: what the AI is doing, how it is doing it, why it is doing it, under what policy and authority, and what the run is actually costing. That is where I think the next serious layer sits. Not just smarter models, but visible execution of that intelligence.
The framing is right — differentiation through encoded institutional knowledge is defensible in ways that prompt engineering alone is not. The implementation challenge is usually where teams stall, and it tends to surface three problems that weren't visible before. First, the data governance bottleneck. Most enterprises discover their 'institutional history' is fragmented across systems that don't export cleanly. The fine-tuning pipeline becomes a forcing function for data infrastructure work that should have happened years ago. Teams underestimate this by 3-4x in initial timelines, and it delays the differentiated capability significantly. Second, evaluation drift. A model fine-tuned on last quarter's decision logic starts producing subtly wrong outputs when business rules change. Without continuous evaluation infrastructure, the gap grows silently. The differentiated capability degrades over time and nobody notices until a client escalates. The fine-tune is not a one-time investment — it's a process, and the teams that treat it as one outperform those that don't. Third, the access pattern problem. A model that 'knows your business' also knows things you may not want surfaced in certain contexts. This is becoming a first-class security consideration. The network isolation capabilities shipping in enterprise AI tooling this week exist precisely because organizations need enforceable boundaries on what an agent can access and where it can send data. The differentiation Mistral describes is real and defensible. But the teams extracting it durably are the ones building operational infrastructure around model updates, evaluation, and access control — not just the initial fine-tune. That's the gap between a proof of concept that works in the demo and a production system that works at 2am on a Friday.
Nicolas PAYEN
1mo
Very well put. I think there is still one layer missing in the discussion. It is not just about what the model knows. Data, context and fine tuning matter, but they are only part of the story. The real differentiation comes from how that knowledge is governed and used in real interactions. Two companies can use the same model and similar data, and still create very different outcomes depending on how they control the knowledge scope, encode decision logic, define what the AI is allowed to do, and turn answers into actions. Using the best model for each task is important. It lets you benefit from continuous model improvements. But only if governance and operational trust are maintained at the system level. Models may be a commodity. What is not a commodity is how you represent, govern and execute with them.
aiqbee
4w
Generic intelligence is a commodity and every organisation will have access to the same models within months of each other. What cannot be replicated is the knowledge that only exists inside your organisation. Where most companies struggle is not access to models but the unglamorous work upstream of the model: capturing decisions, rationale, and context in a structure the model can actually use. Fine-tuning and RAG both assume the knowledge has already been made explicit. Getting it there is the part nobody has solved at scale yet.
I mostly agree — but I think there’s one layer missing. Yes, differentiation comes from *what the model knows about your business*: data, decision logic, institutional memory. But before that, it comes from something even more basic: how clearly you can *describe* your business. Most companies don’t struggle because they lack data. They struggle because their logic is implicit. * Why do we prioritize this customer? * What actually drives margin? * Where are the real constraints in our system? If you can’t articulate that clearly, you can’t encode it into a model, no matter how advanced it is. So the real stack looks something like: 1. Clarity of thinking 2. Structured representation (logic, workflows, assumptions) 3. Data 4. Model Everyone is rushing to layer 4. The winners are fixing layers 1–2 first. That’s also why “domain-specialized AI” isn’t just about fine-tuning models — it’s about turning tacit knowledge into something explicit enough to *be modeled*. Generic intelligence might be a commodity. But structured thinking about your own business? Still very rare.
Nitesh Nahta
1mo
As much as I vouch for European Tech I honestly feel you are leaving a lot on the table in this thinking of becoming a specialized AI for vertical. The logic that AI model intelligence is going to be a commodity is flawed. With running after organizations you are limiting the data, use cases and scenarios thst you would get from 100s of use cases. You should probably think in the direction of how your AI models can perform better and smart across industries and domains. That old era of creating safeguards around your industry domain data in my opinion is going to die soon. Not an expert but sharing my own experience and a coming from a good heart for for European tech as I really want you guys to thrive but this approach of going after orgs will not gain you much in this AI race Mistral AI
Vibemaster
2w
I mostly agree, but I think there’s one layer missing. Yes, differentiation comes from *what the model knows about your business*: data, decision logic, institutional memory. But before that, it comes from something even more basic: how clearly you can *describe* your business. Most companies don’t struggle because they lack data. They struggle because their logic is implicit. * Why do we prioritize this customer? * What actually drives margin? * Where are the real constraints in our system? If you can’t articulate that clearly, you can’t encode it into a model, no matter how advanced it is. So the real stack looks something like: 1. Clarity of thinking 2. Structured representation (logic, workflows, assumptions) 3. Data 4. Model Everyone is rushing to layer 4. The winners are fixing layers 1–2 first. That’s also why “domain-specialized AI” isn’t just about fine-tuning models. It’s about turning tacit knowledge into something explicit enough to *be modeled*. Generic intelligence might be a commodity. But structured thinking about your own business? Still very rare.
One friendly pushback. Fine-tuning proprietary weights answers the commoditisation question. It does not answer the dependency question. If your institutional logic is fused into weights trained on a pipeline you do not fully control, you have moved the lock-in, not removed it. The other architectural answer is to keep the logic outside the model entirely. Markdown files, folder architecture, context any capable model can read. Substitutability (if this a word in english) at the context layer, not the weights layer.