Deploying a Language AI model is only the beginning. The work is done only when people trust it.

Today, there are nearly 300 Language AI model families, and 1+ million fine-tuned models available in the growing Hugging Face repository. It is a maze. Only a handful are successful, judging by criteria like revenue, active users, adoption, performance and safety.

I believe that in 2026, success depends on something else. In a maze of hundreds of commercial LLMs, trust is the new product.

Trust does not get the credit it deserves in making AI successful.

Tech firms, small and large, talk about data, latency (speed), and ease of implementation as differentiators. Indeed, the success of Language AI and agentic AI implementation depends on external factors, and thus, providers can only control performance and fine-tuning. Performance, speed and implementation therefore become common differentiators that tech firms focus on. However, a commonly talked about differentiator is not a differentiator.

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Take, for example, the volume of parameters of pre-trained models. Knowing that this has now reached hundreds of billions of parameters is tantalizing to one’s imagination because it leans on something we can imagine is an indicator of performance: volume.

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That is true to a limited extent in AI models. Pre-trained models need trillions of parameters to learn language context and nuances. Once the pre-training is finished, using such a model saves you energy and resources. However, after pre-training, models need specific context and fine-tuning be fit for a job. Models also need ongoing monitoring to safeguard against, for example, sycophancy and misaligned behavior.

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To this effect, I loved Anthropic’s release of Petri, an open-source tool that helps researchers (rather than end-users) test models and understand their behavioral alignment to a desired standard. Petri shifts testing from adhering to static benchmarks (like answering a multiple-choice test) to dynamic agentic loops, where an AI auditor actively tries to trick the model.

Here is how trust can be built in AI products.

Privacy

Privacy becomes important when people understand the consequences of giving models sensitive data. Privacy is not to be toyed with. Someone can leak or misuse data. This is not a forewarning; it is a fact of having a digital footprint. For consumers, this topic is sensitive. It is also difficult to understand the implications to privacy of new AI products.

I recently read about toys that use AI to chat with and recognize what children say, then generate an answer. Is this smart or sloppy? The 2025 "Trouble in Toyland" report by U.S. PIRG highlighted significant risks in AI-enabled toys. After raising concerns over safety, the Kumma teddy bear by FoloToy was withdrawn because of inappropriate responses.

Plus, what children say two toys can be a tricky privacy topic for parents and for themselves. How is privacy built into these devices? The rhetoric of this question is that it should be built-in, without commenting on the extent to which this is already happening.

In B2B, privacy is a standard. People receive training and expected to know what data is safe to share with Language AI models and agents.

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