A Silicon Valley Startup Claims to Have Solved Hallucinating AI

Some assertions start out as whispers—quiet but persistent—and gradually gain attention due to their mere interest rather than their spectacle. That’s how the announcement from Logical Intelligence came to be. Calmly, a startup that few people had heard of claimed to have solved the problem of hallucinating AI. not downplayed it. resolved it.

I was immediately drawn to that. For anyone who has worked with generative AI, hallucination is a natural part of language models and not a side effect. They have remarkable fluency in their word predictions, although their accuracy is frequently questionable. However, Kona, Logical’s tool, doesn’t work that way.

In contrast to a language model, Kona is an energy-based model, or EBM, and does not attempt to connect words using patterns. It assesses potential results in a rational manner. Consider presenting a puzzle to an AI that considers the entire board before selecting a piece, rather than speculating on the next move. That’s Kona—intentionally cautious, incredibly successful.

Overconfidence has been a recurring problem in the years I’ve used a lot of AI products. It creates a citation when you ask for one. It creates a case when you ask for legal precedent. Refusing to guess when uncertain is what Kona does differently. It feels remarkably different because of that reluctance, that readiness to stop and reconsider.

Logical Intelligence presents a radically novel method through the use of EBMs. Kona is not required to use the internet. It makes no effort to imitate human speech at all. Despite having fewer than 200 million characteristics, it is incredibly effective at targeted tasks. The startup solves specific, mission-critical problems in industries where accuracy is essential rather than pursuing general knowledge.

That change—from domain-specific to all-knowing—is both calculated and unexpectedly economical. Large models are quite expensive to compute. Faster and far lower energy usage deployments are made possible by Kona’s lightweight design. That is especially helpful to clients in the semiconductor, robotics, and energy industries.

One of the most renowned experts in machine learning and a well-known opponent of the exaggerated hype surrounding language models, Yann LeCun, provides the philosophical underpinnings of Kona. He sits on Logical’s board and does more than just offer advice from the sidelines. That alone indicates that this is a real endeavor supported by in-depth research and cautious ambition rather than a speculative moonshot.

One statement made by CEO Eve Bodnia that has stayed with me is, “We’re not chasing scale.” Our goal is structure. The way Logical develops is influenced by such clarity. Public APIs are not released by it. GitHub isn’t overloaded. Rather of depending on generic internet inputs, the team collaborates with a few clients and trains models on their data while honoring context and limits.

Kona addressed electrical grid optimization in one use case, which is often a time-consuming simulation procedure. However, Kona significantly increased the efficiency of identifying optimal configurations rather than executing thousands of cycles. It was changing the way energy could flow, not attempting to discuss energy.

In contrast to many AI businesses, Logical makes no claims about its all-encompassing capabilities. It thinks AI should be layered instead. Conversation is handled by one model. Reasoning is handled by another. A third engages in physical interactions. This type of ecological thinking is especially creative and closely relates to how people use various brain regions to solve problems.

I took note of Bodnia’s tone during our call—focused without going overboard. There were no assertions of AGI superiority or racial invective. Just a subtly strong case for grounded thinking and a shift away from conjecture. “Hallucinations are not bugs,” she stated. They are signs of the way LLMs think. It was very evident.

Here, there’s a confidence that doesn’t require show. And that was invigorating to me. According to logic, intellect shouldn’t be broad and ambiguous. It feels like it ought to be explicable, particularly when infrastructure or lives are at stake.

Paragraphs of text are not spewed out by Kona. It doesn’t imitate Shakespeare or write poetry. However, if you give it a task with a lot of constraints, like as figuring out how to allocate resources, solving a puzzle with constraints, or creating a chip system that can withstand faults, it doesn’t guess. It gives a rationale.

Flash doesn’t appeal to the startup because of this. It is results-oriented. It is avoiding the bloat and delusion pitfalls that still plague the LLM giants by collaborating with a small number of companies.

This isn’t scalable, according to others. And if you’re searching for an AI to produce news summaries or marketing content, that might be the case. However, Kona’s design appears to be quite dependable if you need an AI to make accurate decisions, particularly in situations when mistakes are costly.

Logical’s digital philosophy has an almost analog quality. It approaches AI as engineering rather than magic. Each layer has a function. Each function needs to be able to be verified. Although it might not make news, that fosters trust.

AI must first demonstrate that it is capable of responsible failure and clear reasoning before it can be used in power plants, hospitals, or aerospace systems. According to Kona’s early pilots, the goal is not only attainable but is now being achieved covertly.

We have been racing toward scale for years. However, Logical is a strong argument for taking your time and exercising caution. Performance is giving way to accuracy, and lofty narratives are giving way to measurable outcomes.