By Charles Yeomans, Founder and CEO, Atombeam. Atombeam were named ‘Best SaaS Newcomer’ at the 2025 SaaS Awards and shortlisted for ‘AI Startup of the Year’ at the 2025 A.I. Awards.

Large language model vendors are rolling out memory features that promise your AI assistant will recall your preferences and the facts of your life. This is being sold as a leap forward, but I think it is an illusion. What these systems call memory is a database with a search function attached.

AI memory features store facts, and do not alter the internals of the model. Tell the system you are allergic to peanuts and it writes that down. Mention food later and it retrieves the entry. Useful, certainly, but consider what actually happened: the system looked up a record. It is the difference between a waiter who writes down that you take your coffee black and a friend who knows your taste well enough to point you to a roastery you will love. The friend has revised an internal model of you, one deep enough to draw a near-intuitive conclusion.

After 100 conversations with a memory-enabled LLM, you are talking to the same underlying model you started with. It holds 100 stored conversations, but it is no better at understanding how you think or what you need. Ask it a question for the 1,000th time, or the millionth, and it performs exactly the computation it performed the first time. Nothing was learned. It is as though you woke up every morning with no idea who you are or where the kitchen is, and had to figure it all out again, every single morning, helped only by a set of notes you jotted down next to the bed.

What learning would look like

Picture a different morning. You open your assistant and it says: “You’re heading to New York next week. The Red Sox are at Yankee Stadium while you’re in town, so I’ve pulled ticket and transportation options for you to look at.”

No file labeled “user likes baseball” produced that. The system learned your interests over time, connected a real-world event to your travel plans, and anticipated a want you had not yet expressed. Each interaction made it better at the next one.

Apply the same idea to work. A coding assistant that learns does more than recall your previous bugs. By session 50 it knows you consistently miss edge cases in recursive functions, and it catches them before you do. A research collaborator that learns understands which sources you find credible and what kind of evidence persuades you, because it has watched you decide.

The cost of stagnant knowledge

This is not a theoretical concern. High-frequency trading algorithms are masterpieces of speed built on a simple principle: if X happens, do Y. They treat every market tick as a fresh, decontextualized event. The system cannot do what a veteran trader does, which is recognize the shape of 2008 re-emerging in entirely different instruments. It knows the data from that crash; it never formed an understanding of it. Human traders have to step in and supply the judgment the machine cannot hold. The result is enormous speed with no capacity for accumulated wisdom.

Enterprise AI has the same defect. Companies are assembling an organizational brain that cannot gain knowledge from experience. Every query starts the analysis over from scratch, with a few stored data points thrown in. The system repeats the same work and requires the same corrections, and the experience it should be accumulating simply evaporates. Meanwhile it generates ever more data, which further stresses the infrastructure underneath it.

There is a computational bill attached to this amnesia. A system that cannot learn must recompute its way to every answer. The 1,000th time it encounters a familiar question, it burns the same GPU cycles it burned the first time, reloading the same context and re-deriving conclusions it has already reached hundreds of times before. The cost per query never falls, so total compute spend simply scales with usage, which is a large part of why inference has become such a heavy and growing line item in enterprise AI budgets. Compare that with any human expert, for whom the marginal cost of a familiar judgment drops toward zero with experience; they can answer a question immediately that someone without that background might take days to research and address. An architecture that cannot convert experience into structure has no choice except to pay full price for every thought, no matter how many times it has had that thought before.

The filing cabinet problem

Retrieval-augmented generation, or RAG, is being marketed as revolutionary. It is database lookup. Your interaction history sits in vector storage and gets retrieved when relevant. The internals of the model never change. When the model does improve, it happens through a costly formal training process that has nothing to do with you. It is a general update for all users, and your thousands of interactions contributed nothing to it.

The architects are breaking ranks

I am not alone in this view, and some of the people now making the argument built the current paradigm. Ilya Sutskever, the co-founder and former chief scientist of OpenAI, declared in a November 2025 interview that the “age of scaling” is ending. Today’s models, he observed, generalize dramatically worse than people despite their benchmark scores, and he argued that the next advances will come from new architectures and continual learning rather than from ever-larger training runs. In his view the AI systems of the future will not be static trained models at all; they will be continuously learning entities that grow over time. That is a description of exactly the gap this article is about.

The philosopher Toby Ord has made the quantitative version of the case. In his analysis of the famous scaling laws, he shows that the smooth curves the industry celebrates are, read plainly, a proof of extremely poor returns to scale: by the exponents in OpenAI’s own 2020 scaling paper, the compute required grows as roughly the 20th power of the desired accuracy, so halving a model’s error required about a million times more compute. Ord’s closing observation is the one I would underline. Inefficiency this severe suggests that other architectures exist which scale far better, and the human mind, which needs nothing like an internet of data to reach general intelligence, is proof that such efficiency is possible. Our brains, which exhibit general intelligence, run on 20 watts; current AI cannot achieve artificial general intelligence with megawatts.

What we need is an AI that learns from everything on the fly, rather than one that merely stores everything. An AI that develops a working understanding of the people it serves, and changes its internals when real world circumstances change.

At Atombeam we are building exactly that: a system whose geometric understanding evolves with every interaction. The difference shows up most clearly in how little experience it needs. A transformer learns by statistical pattern matching, which is why it must see a behavior in millions of traces before it can reproduce it. A system built on cognitive understanding grasps the structure of what it is seeing, and we believe it can develop a usable understanding of a new person or a new situation from a handful of interactions, on the order of five. The knowledge is shaped, and reshaped, by use, which is far closer to how people learn than anything a filing cabinet could offer. And as it gains understanding it becomes much, much cheaper to operate, because like the human expert it no longer needs to think through every step every time.

Privacy and the grid

An AI that learns intimately about you should be under your control. The knowledge it gains should be yours: deletable when you choose, shareable only with your permission. It should never be a corporate asset that knows you intimately while belonging to someone else.

There is an infrastructure argument as well. A recent Deloitte survey found that data center and power company executives alike point to technological innovation, rather than more construction, as the way to close the infrastructure gap. We cannot simply spend our way out with more data centers. The power and the water are not there, and the money may not be either. An AI that learns is also an AI that is efficient. By processing insight locally instead of shipping every query to a centralized cloud, we believe this kind of architecture can cut computational waste on the order of 70 to 80 percent, and local processing inherently supports privacy and user control. In prototype testing, the Atombeam system has demonstrated roughly 840 times greater compute efficiency than a small transformer-based language model on subjects it has learned.

The day everything changes is the day your AI arrives at a working understanding of you within a handful of interactions and then deepens it with every one that follows, at a cost that falls with experience instead of rising with usage. No database, however large, will get there. An AI that genuinely learns from the people it works with is buildable, and it is closer than most of the industry would have you think.