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Francis Walshe examines the current AI landscape and discusses who the real winners are.
Between 1848 and 1854, an estimated 300,000 people descended on the state of California after the discovery of gold at a water-powered sawmill in Coloma. Only a small fraction found enough precious metal to make the trip worthwhile.
Of course, the California gold rush created a thriving secondary market for prospecting gear. As hundreds of thousands of people dug fruitlessly in the state’s streams and riverbeds, the businesses selling them shovels enjoyed success beyond their wildest dreams.
Now, California is providing the setting for another gold rush. Headquartered in Silicon Valley, OpenAI, the AI start-up behind ChatGPT, has forever changed the world of technology and, following a tender offer reported on by the New York Times earlier this year, the still-private company is valued at around $80bn. This figure makes OpenAI more valuable than a host of Big Tech giants, including the likes of Spotify and Snap.
But ChatGPT is only the beginning. The AI revolution’s first golden nugget has spawned dreams of countless more; more advanced programs, specialised applications and flashier platforms that promise to make ambitious developers and start-ups inconceivably rich. Some of these aspiring pioneers will no doubt be successful. However, the smart money says that many, many more will see their dreams go down in flames.
That’s not the end of the story, though. As long as the AI bubble continues to inflate, the providers of the secondary products and services that facilitate their development will be able to line their pockets. While Silicon Valley’s idealistic prospectors dig up the land in search of precious metal, established players in the hardware market will make a fortune selling them shovels.
Is AI living up to the hype?
“We decided to incorporate a highly rated AI-based legal research tool into our operations, and I was among the advocates for this approach,” says Andy Gillin, attorney and managing partner at GJEL Accident Attorneys in California. “The output was initially impressive, but our enthusiasm for it waned as we began to see the gaps.
“After several months, it became quite clear that while the tool had potential, its execution fell short of our firm’s requirements. Our team began to rely more heavily again on traditional legal research methods.”
Jon Morgan, CEO of consulting firm Venture Smarter, experimented with an AI-powered predictive analytics tool, and was also disappointed with the ultimate outcome. “Trends and market conditions can shift quickly in our business. Unfortunately, the AI models we employed weren’t able to effectively capture these nuances or adjust their predictions accordingly. As a result, the insights provided by the platform didn’t offer the level of accuracy and reliability we needed to make informed decisions.”
Gillin and Morgan’s stories echo those of other business leaders who have already hopped on and off the AI bandwagon. I spoke with managers who experimented with automated customer service chatbots, content generators, customer relationship managers and fitness instructors. The platforms my interviewees tried looked impressive and were useful to some degree. However, when push came to shove, many were simply unable to replicate human-level work. None of the individuals I spoke to were able to replace a human employee with an AI tool.
Of course, if you’re seeking financial gain as an innovator in tech, you don’t always need concrete results. Apparent potential will often do just fine. This wouldn’t be the first time that widespread hype in the tech industry has ended in tears; the dot-com boom and bust of the early 21st century provides us with useful precedents to consider.
Pets.com, an online pet supplies store, raised $82.5m in its initial public offering in 2000 before filing for bankruptcy just nine months later. Online grocery delivery firm Webvan imploded even more dramatically, folding in 2001 after reaching a $1.2bn valuation in 1999.
The holes in these companies’ business models might be glaringly obvious now, but – as is typical in bubble economies – many investors failed to see the warning signs until it was too late. Given the meteoric rise of AI over the last while, it seems inevitable that we’ll see more stories like this in the coming years.
Will the boom keep on booming?
The big question is how much automation AI will be able to bring about. In April 2023, a Goldman Sachs report estimated that 300m human jobs could be taken over by generative AI; while there have been some reports of AI-related job losses over the intervening 12 months, it appears we’re still a long way away from obsolescence on that grand scale.
So, should we just be patient? Should we expect the skyward trend in the capability of AI models to continue indefinitely? According to Dr Ruairi O’Reilly, lecturer in the Department of Computer Science at Munster Technological University, probably not.
“LLMs are inherently limited by the data they’ve been trained on and this hasn’t really been acknowledged,” says O’Reilly. So, even programs that are clearly useful (such as ChatGPT) may be nearing the ceiling of what they can achieve in the near future.
Then, there’s the energy problem. “As these models get larger and larger, they need more computing power,” says O’Reilly. “So, at a certain point, the efficiency of larger models will be outpaced by the costs associated with training them.”
In fact, computing capacity and storage have been standing in the way of AI’s progress for decades. Neural networks, the machine-learning processes standing behind much of the current machine learning infrastructure, were invented in the 1990s; however, it was “only when computing power and storage became cheap with the advent of cloud computing that they became feasible,” O’Reilly points out.
This speaks to the phenomenon of ‘AI winters’. The level of interest (and investment) in artificial intelligence has ebbed and flowed since the invention of the computer; bursts of rapid growth in the space have repeatedly been followed by long periods of low engagement.
O’Reilly believes that productivity enhancement is a field in which AI innovators are poised to make huge leaps in the near future. He points to Fin, a chatbot program released by Intercom, that has had a lot of success handling customer queries without human intervention. “Programs like these will allow companies to use automated workflows that keep humans in the loop. This is likely to be an area of significant growth, where real productivity gains can be realised.”
There’s a problem, though. If the market lurches downward due to another loss of confidence, potential success stories like this could falter, simply because of unfortunate timing. While we’re currently in the midst of a scorching AI summer, this isn’t necessarily a good thing for the industry in the long run.
“I would be worried that if a big company failed, it would cause contagion; they could fall like a house of cards,” says O’Reilly. “This could stymie innovation by companies that are actually making gains and providing value to their customers.”
Who’s making the money in AI now?
While LLMs have made us more efficient, they haven’t quite turned the world of work on its head. If historical trends are anything to go by, the automation of the working world could take decades, rather than years.
However, there can be no denial of the eye-watering amounts of money changing hands to keep the current AI juggernaut on the road. As noted, OpenAI has been the biggest winner on the software side of things, but even greater gains have been made in the hardware space.
Nvidia, which produces the graphics processing units (GPUs) needed to train and run programs like ChatGPT, posted stock-price gains of over 241pc in 2023, making it the best-performing security on the S&P 500 for the year. Its $2trn valuation makes it the third-biggest company in the world as of April 2024.
Moreover, the company is likely to maintain this success “as long as GPUs remain the dominant force behind the training of models,” says O’Reilly. Nvidia controls around 95pc of the GPU market at present, and the demand for these chips will keep growing as long as developers continue training increasingly advanced machine-learning applications.
Nvidia’s isn’t the only semiconductor company to perform strongly of late. AMD (which lists Meta and Microsoft as clients) has also made astounding progress, tripling its market capitalisation between January 2023 and February 2024. Intel has also posted gains.
These industry giants have erected significant barriers to entry into the computer hardware space. The initial investment for a newcomer would be massive and much of the key intellectual property is patent-protected.
The hardware demands of artificial intelligence don’t begin and end with chips. Memory storage facilities, data centres, cooling systems and networking equipment have all become more important of late as well.
Crucially, many of the market gains in the hardware space have been backed by huge revenue streams. Nvidia’s revenue report for the final quarter of fiscal year 2023 marked an increase of 265pc over the same period in 2022. These companies aren’t just growing on the back of raw speculation; they’re being fuelled by fat wads of cash.
So, while a cooling AI market will hurt hardware giants, they’ve already made plenty of hay beneath the still-shining sun. Semiconductors and data centres were around long before the current AI bull run, and they’ll still be here well after it ends.
The glints in the riverbed
So, what does the future hold? Valuable products and services will always attract demand, and there is clearly plenty of value in AI. Though there were a lot of expensive losers during the dot-com bust, there were also companies (Amazon, for example) with solid foundations that weathered the storm and became industry giants in the years that followed. OpenAI appears to be walking this path already, and others will surely follow.
However, the landscape is treacherous and uncertain. When the next AI winter settles in, it will freeze out many nascent players in the space. The hardware providers who have already made billions selling metaphorical shovels are much less likely to be left in the cold.
Francis Walshe is a freelance writer focusing mainly on legal, business and tech stories. He hails from Waterford, Ireland but currently lives in Vancouver, Canada.
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