The artificial intelligence industry has become rather obsessed with a handful of companies. OpenAI, Anthropic, Google and their Chinese competitors dominate the headlines, with each new model release accompanied by impressive benchmarks and increasingly ambitious claims about what machines will soon be capable of doing.
But behind this competition, another industry is taking shape. Building more intelligent models is only part of the challenge. We still have remarkably little understanding of how neural networks arrive at their decisions, struggle to control what autonomous agents can do, and are discovering that running AI at scale requires extraordinary amounts of computing power and electricity.
Some of the most interesting companies are trying to solve these problems rather than build another general-purpose language model. I’ve picked five that I think are worth following, each working on a different part of the emerging AI ecosystem.
Dynamo AI: Keeping autonomous AI under control
Dynamo AI was founded in 2021 by MIT researchers Christian Lau and Vaikkunth Mugunthan, whose early work focused on privacy, security and the responsible deployment of machine learning. The company has since developed technology that helps organisations test, monitor and control AI systems.
The problem becomes particularly interesting with the emergence of autonomous agents. An AI that answers questions can certainly make mistakes, but an agent that can access corporate systems, execute code, transfer information or make decisions introduces a different class of risk. Traditional security controls can determine whether someone has permission to access a database, but an AI agent might have perfectly legitimate access and still decide to do something nobody intended.
Dynamo’s products, including AgentWarden, attempt to address this by assessing the tools available to agents and enforcing policies as they operate. Rather than relying entirely on instructions telling an AI to behave itself, organisations can impose controls at the points where an agent interacts with other systems.
I find this an important area of development because the more capable agents become, the harder it will be to justify giving them unrestricted access to business operations. I should also disclose a personal connection: my son works at Dynamo AI, although I have no financial interest in the company. That has naturally made me follow its work more closely.
Goodfire: Understanding what is happening inside the machine
Goodfire was established in San Francisco in 2024 by researchers with backgrounds in AI interpretability, including work at OpenAI and Google DeepMind. Its ambition is to understand how neural networks actually work, a question that sounds rather basic until you appreciate how little we currently know about the internal mechanisms of the most sophisticated models.
Modern neural networks are trained using vast amounts of data and computation. Through this process they develop complex internal representations that allow them to reason, recognise patterns and generate surprisingly sophisticated responses. Researchers understand the mathematical operations involved, but identifying exactly how a model represents a concept or arrives at a particular decision remains difficult.
Goodfire is working on mechanistic interpretability, developing techniques that reveal and manipulate these internal representations. Its research has explored whether understanding a model’s internal workings can help reduce hallucinations, detect undesirable behaviour and improve training.
The company raised $150 million in February 2026, reflecting growing interest in this field. What appeals to me is the possibility of moving AI development away from the current reliance on enormous experiments, repeated testing and considerable educated guesswork. Other engineering disciplines generally expect practitioners to understand how their creations function. AI has made extraordinary progress without meeting quite the same standard, although Goodfire is not alone in trying to change that.
d-Matrix: Tackling the economics of intelligence
d-Matrix was founded in 2019 by semiconductor engineers Sid Sheth and Sudeep Bhoja. The company designs specialised processors for AI inference, the process of running a trained model to produce answers, rather than training the model in the first place.
The distinction is becoming economically important. Training a frontier model can cost hundreds of millions of dollars, but once deployed, that model may be asked to answer billions of questions. Every interaction requires computation, and increasingly sophisticated reasoning models can consume substantial processing resources even when responding to relatively straightforward requests.
Much of this workload currently runs on expensive GPUs, particularly those supplied by Nvidia. d-Matrix has developed an alternative architecture that brings memory and processing closer together, reducing the movement of data that can slow down conventional computing systems. Its Corsair accelerator entered full production in June 2026 and is designed to work alongside existing infrastructure to improve inference performance.
The potential implications are significant. If AI is going to become a routine part of business software, its cost per useful result will become a critical commercial measure. There is little point developing an extraordinarily capable agent that costs more to operate than the work it replaces. Companies that can materially improve the economics of inference may end up capturing considerable value, even if most people never encounter their names.
Prime Intellect: Challenging the concentration of AI computing power
Prime Intellect was founded in 2024 by Vincent Weisser and Johannes Hagemann. Its objective is to make advanced AI development possible without requiring every participating organisation to own or rent a vast, centrally managed computing facility.
The largest AI models have traditionally been trained using thousands of tightly connected processors operating within enormous data centres. This is expensive and creates a substantial barrier to entry for independent researchers, universities and smaller technology companies.
Prime Intellect is developing techniques that allow computing resources in different locations to collaborate on training tasks. That is technically difficult because processors separated by thousands of miles communicate far more slowly than machines sitting next to one another. The company has nevertheless demonstrated distributed training, including its INTELLECT-2 project, which used globally distributed reinforcement learning to train a 32-billion-parameter reasoning model.
This work interests me for reasons beyond computing efficiency. Access to sufficient processing power increasingly determines who can undertake serious AI research. If the industry’s future depends on infrastructure affordable to only a handful of companies and governments, the consequences for competition and independent innovation could be considerable. Prime Intellect is exploring whether that concentration is an unavoidable feature of AI or partly a problem of engineering design.
ElevenLabs: Changing how we communicate with computers
ElevenLabs was founded in 2022 by Polish entrepreneurs Mati Staniszewski and Piotr Dąbkowski, initially inspired by the poor quality of dubbed films. They wanted to produce synthetic speech that sounded genuinely human, preserving expression, timing and emotion rather than simply converting written words into recognisable sounds.
The company has expanded into voice generation, translation, dubbing and real-time conversational technology. In September 2026, it reached a valuation of $22 billion following a secondary share transaction, an extraordinary achievement for a business only four years old.
Its broader significance lies in making spoken conversation a practical interface for AI. A genuinely useful voice assistant needs to do considerably more than understand words. It must handle interruptions, recognise when someone has finished speaking, respond with minimal delay and maintain the natural rhythm of conversation. These are difficult technical problems that become more demanding when the system is also expected to reason and take action.
For decades, humans have adapted to computers through keyboards, screens, menus and increasingly elaborate software interfaces. Natural conversation offers the possibility of reversing some of that relationship. ElevenLabs is also confronting the accompanying problems of consent, impersonation and fraud, particularly when synthetic voices become difficult to distinguish from real ones.
An industry still being invented
What I find striking about these companies is how different their challenges are. Dynamo is concerned with controlling AI behaviour, Goodfire with understanding it, d-Matrix with making it economical, Prime Intellect with broadening access to its development, and ElevenLabs with changing how we interact with it.
None has an assured future, and some of their technologies may eventually be absorbed into the platforms of the larger technology companies. There is also a reasonable possibility that improvements in the underlying models will change the problems themselves, making some approaches more valuable and others redundant.
Yet the existence of these businesses tells us something about the maturity of the AI industry. We have made remarkable progress in creating intelligent systems, but many of the disciplines required to make those systems dependable, affordable and widely accessible are still being developed.
The major model providers will continue to attract most of the attention, understandably. I suspect, though, that some of the most consequential innovations over the next few years will come from companies solving problems that rarely feature in the benchmark tables. They are building the infrastructure, tools and scientific understanding on which the next stage of AI will depend.
