Companies embroiled in expensive AI arms race – BBC News

There’s no doubt we’re in an AI arms race, says Jon Collins.

He has worked in IT for 35 years in various roles, including as a software programmer, systems manager and Chief Technology Officer.

He is now an industry analyst for research firm Gigaom.

The current arms race was sparked by the launch of ChatGPT in late 2022, Mr Collins says.

Since then, many such generative AI systems have emerged, and millions of people use them every day to create illustrations, text, or video.

For business leaders, the stakes are high. Generative AI systems are very powerful tools that can process more data in minutes than a human could do in several lifetimes.

Suddenly, business leaders are aware of what AI can help them, and their competition, achieve, Mr. Collins explains.

“Fear and greed are the driving force,” he says. “And that creates an avalanche of momentum.”

With the right training, a custom AI system could allow a company to leap ahead of its rivals with a research breakthrough, or save costs by automating the work currently done by humans.

In the pharmaceutical sector, companies are adapting AI to help them discover new compounds to treat diseases. But it is an expensive process.

“You need data scientists, and you need model engineers,” Mr. Collins explains.

Those scientists and engineers need to understand, at least to some extent, the pharmaceutical area in which the AI ​​will work.

And that’s not all. “You need the infrastructure engineers who can build your AI platforms,” he continues.

Such highly qualified workers are not easy to find.

There just aren’t enough people who “understand how to create these systems, how to actually make them perform and how to solve some of the challenges ahead,” says Andrew Rogoyski, director of innovation at the Surrey Institute for People-Centred AI. at the University of Surrey.

The salaries of those who can meet these challenges have reached “ridiculous” levels, he adds, because they are so important.

“If we had the capacity, we could produce hundreds of AI PhDs because people would give them jobs.”

In addition to the skills shortage, gaining access to the physical infrastructure needed for large-scale AI can be a challenge.

The kind of computer systems needed to run an AI for cancer drug research typically require between two and three thousand of the latest computer chips.

The cost of such computer hardware alone could easily exceed $60 million, before the cost of other essentials such as data storage and networking.

Part of the problem for business is that this kind of AI has appeared quite abruptly. Earlier technology, such as the rise of the Internet, was built up more slowly.

A large bank, pharmaceutical company or manufacturer may have the resources to purchase the technology it needs to take advantage of the latest AI, but what about a smaller company?

The Italian start-up Restworld is a recruitment website for catering staff, with a database of 100,000 employees.

Chief Technology Officer Edoardo Conte was keen to see if AI could benefit the company.

The company was considering building an AI-powered chatbot to communicate with users of the service.

But Mr. Conte said that, for thousands of users, “costs are going through the roof.”

Instead, it looked at a narrower problem: the problem that candidates don’t always present their experience in the best way.

For example, a candidate may not list serving as a skill. But the algorithms Mr Conte has developed make it easier to find out additional information, including whether they have applied for and been granted a watch role in the past.

“The AI ​​can infer that they are a waiter, or that they may be interested in other waiter vacancies,” he says.

One hurdle in hospitality recruitment is getting candidates to the interview stage.

So Mr Conte’s next challenge is to use AI to automate and customize the interview process for his candidates.

The AI ​​can even have a ‘conversation’ with candidates and create summaries that can be passed on to recruiters.

It could speed up the whole process, which currently can take days, during which time a waiter or chef might have found another job.

In the meantime, larger companies will continue to pour money into AI projects, even if it’s not always clear what they are likely to achieve.

As Mr Rogoyski says, AI adoption is in a “Darwinian, experimental phase”, and it is difficult to see what the consequences will be.

“That’s where it gets interesting. But I actually think we should continue with it,” he says, before adding: “I’m not sure we have a choice.”