Initially, Gorham used his brain-computer interface for single clicks, Oxley says. Then he moved on to multi-clicks and eventually sliding control, which is akin to turning up a volume knob. Now he can move a computer cursor, an example of 2D control—horizontal and vertical movements within a two-dimensional plane.

Over the years, Gorham has gotten to try out different devices using his implant. Zafar Faraz, a field clinical engineer for Synchron, says Gorham directly contributed to the development of Switch Control, a new accessibility feature Apple announced last year that allows brain-computer interface users the ability to control iPhones, iPads, and the Vision Pro with their thoughts.

In a video demonstration shown at an Nvidia conference last year in San Jose, California, Gorham demonstrates using his implant to play music from a smart speaker, turn on a fan, adjust his lights, activate an automatic pet feeder, and run a robotic vacuum in his home in Melbourne, Australia.

“Rodney has been pushing the boundaries of what is possible,” Faraz says.

As a field clinical engineer, Faraz visits Gorham in his home twice a week to lead sessions on his brain-computer interface. It’s Faraz’s job to monitor the performance of the device, troubleshoot problems, and also learn the range of things that Gorham can and can’t do with it. Synchron relies on this data to improve the reliability and user-friendliness of its system.

In the years he’s been working with Gorham, the two have done a lot of experimenting to see what’s possible with the implant. Once, Faraz says, he had Gorham using two iPads side by side, switching between playing a game on one and listening to music on the other. Another time, Gorham played a computer game in which he had to grab blocks on a shelf. The game was tied to an actual robotic arm at the University of Melbourne, about six miles from Gorham’s home, that remotely moved real blocks in a lab.

Gorham, who was an IBM software salesman before he was diagnosed with ALS in 2016, has relished being such a key part of the development of the technology, his wife Caroline says.

“It fits Rodney’s set of life skills,” she says. “He spent 30 years in IT, talking to customers, finding out what they needed from their software, and then going back to the techos to actually develop what the customer needed. Now it’s sort of flipped around the other way.” After a session with Faraz, Gorham will often be smiling ear to ear.

Through field visits, the Synchron team realized it needed to change the setup of its system. Currently, a wire cable with a paddle on one end needs to sit on top of the user’s chest. The paddle collects the brain signals that are beamed through the chest and transmits them via the wire to an external unit that translates those signals into commands. In its second generation system, Synchron is removing that wire.

“If you have a wearable component where there’s a delicate communication layer, we learned that that’s a problem,” Oxley says. “With a paralyzed population, you have to depend on someone to come and modify the wearable components and make sure the link is working. That was a huge learning piece for us.”

Expect datacentre spending to increase tenfold. That was among the claims Nvidia CEO Jensen Huang made during the company’s latest quarterly earnings call. He forecast that capital expenditure (CapEx) on datacentres would increase from the $300-400bn mark today to $3-4tn by 2030.

Huang’s remarks at the end of February came a few days after Microsoft’s AI Tour London event, when CEO Satya Nadella effectively called for enterprise software developers to use the capabilities now built into Microsoft 365 to create agentic AI workflows for streamlining business processes.

Nadella discussed the need to have an efficient token factory, where phrases or tokens can be streamed into AI engines that interpret natural language for querying large language models (LLMs). The Microsoft vision of enterprise AI is built on the M365 foundation, which acts as a knowledge store on which a new category of knowledge-based software can be built.

During his keynote presentation, Nadella spoke about the intelligence that exists in the various IT systems used across the business. He said that businesses should be able to harness the intelligence that already exists enterprise-wide, starting with what he described as the “data underneath Microsoft 365”, which, according to Nadella, represents the people in the business, their relationship to coworkers, and work artefacts such as projects, calendars and communications data. “This is massive information,” he said, which can be used to bootstrap agentic AI projects.

“Our goal is to have all of the innovation and the systems available in the token factory,” said Nadella. “That way you can build software which has the ability to use all of the capability [we provide] to train models and deliver models for inference.”

In effect, he sees the Windows software developer ecosystem evolving to where it is now a Microsoft 365 ecosystem, where enterprise data is stored in AI-enabled office productivity tools such as Word, Excel, PowerPoint, Teams and Outlook, and these can be used as the foundation for a new generation of applications that can draw on these AI knowledge sources.

It is this idea that all software will need to be knowledge-aware, which Huang spoke about during the company’s earnings call. “Token generation is at the centre of almost everything that relates to software in the future and relates to computing,” he said. “If you look at the way we use computing in the past, however, the amount of computation demand for software in the past is a tiny fraction of what is necessary in the future.”

According to Huang, the amount of computation necessary to run AI is 1,000 times higher than the computing power needed to run non-AI software. “The computing demand is just a lot higher,” he said. “And so, if we continue to believe there’s value in it, then the world will invest to produce that token.”

When asked whether Nvidia is confident that its customers will continue to have the ability to spend more on AI infrastructure, which could impact Nvidia’s ability to grow, Huang spoke about the opportunity in enterprises to make use of agentic AI and its widespread usefulness across organisations.

“We have now seen the inflection of agentic AI, and the usefulness of agents across the world and enterprises everywhere,” he said. “You’re seeing incredible compute demand because of it. In this new world of AI, compute is revenues. Without compute, there’s no way to generate tokens. Without tokens, there’s no way to grow revenues.”

At least, that is how he positioned AI for the investment bank analysts on the earnings call. The company posted fourth quarter revenue of $68bn, up 73% year-over-year. Datacentre revenue increased by 75% to $62bn, which Nvidia said was being driven by demand for its Blackwell architecture and AI inference deployments. It also reported networking revenue of $1bn, up 3.5x year-over-year, fuelled by adoption of NVLink, Spectrum X and other Nvidia ethernet technologies.

Last year, during his keynote presentation at the GTC conference in the US, Huang claimed that the lowest cost per token was being achieved using the most expensive GPU – which at the time was the Grace Blackwell NVLink 72.

Nvidia describes the GB200 Grace Blackwell as a “superchip”, which connects two high-performance Nvidia Blackwell Tensor Core GPUs and the Nvidia Grace CPU with the NVLink-Chip-to-Chip (C2C) interface, capable of delivering 900 GBytes/s of bidirectional bandwidth.

Significantly, the architecture means that applications have coherent access to a unified memory space. According to Nvidia, this simplifies programming and supports the larger memory needs of trillion-parameter LLMs, transformer models for multimodal tasks, models for large-scale simulations, and generative models for 3D data.