Sunday, July 21, 2024

Who’s Getting Rich From the AI ​​Gold Rush?

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Francis Walshe examines the current AI landscape and analyses who the real winners are.

Between 1848 and 1854, an estimated 300,000 people came to 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 equipment. While hundreds of thousands of people dug fruitlessly in the state’s creeks and riverbeds, the companies that sold them shovels enjoyed success beyond their wildest dreams.

Now, California is providing the stage for another gold rush. Based in Silicon Valley, OpenAI, the artificial intelligence startup behind ChatGPT, has forever changed the world of technology, and following a takeover bid reported by the New York Times earlier this year, the still-private company is valued at around $80 billion. This figure makes OpenAI more valuable than a host of Big Tech giants, including Spotify and Snap.

But ChatGPT is just the beginning. The first golden nugget of the AI ​​revolution has spawned dreams of countless more; more advanced programs, specialized apps, and flashier platforms that promise to make ambitious developers and startups unimaginably rich. No doubt some of these would-be pioneers will succeed. However, the smart money says many, many more will see their dreams go up in flames.

But that’s not the end of the story. As the AI ​​bubble continues to inflate, providers of secondary products and services that facilitate its development will be able to line their pockets. While Silicon Valley’s idealistic prospectors dig up the earth for precious metals, established players in the hardware market will make a fortune selling them shovels.

Is AI living up to expectations?

“We decided to incorporate a highly-rated AI-based legal research tool into our operations, and I was among the proponents of this approach,” says Andy Gillin, an attorney and managing partner at GJEL Accident Attorneys in California. “The result was initially impressive, but our enthusiasm waned when we started to see the gaps.

“After several months, it became quite clear that while the tool had potential, its execution did not meet our company’s requirements. Our team returned to relying more on traditional legal research methods.”

Jon Morgan, CEO of consulting firm Venture Smarter, experimented with an AI-based predictive analytics tool and was also disappointed with the end result. “Trends and market conditions can change quickly in our business. Unfortunately, the AI ​​models we employed were unable to effectively capture these nuances or adjust their predictions accordingly. As a result, the insights provided by the platform did not 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 jumped on and off the AI ​​bandwagon. I spoke to managers who experimented with automated customer service chatbots, content generators, customer relationship managers, and fitness instructors. The platforms my interviewees tried seemed impressive and were helpful to a point. However, when things got tough, many simply couldn’t replicate human-level work. None of the people I spoke to were able to replace a human employee with an AI tool.

Of course, if you’re looking for financial gains as a technology innovator, you don’t always need concrete results. Apparent potential will often work out just fine. This wouldn’t be the first time that widespread hype in the tech industry has ended in tears; the dotcom boom and bust at the beginning of the 21st century provide us with useful precedents to consider.

Pets.com, an online pet supply store, raised $82.5 million in its initial public offering in 2000 before filing for bankruptcy just nine months later. Online grocery delivery company Webvan imploded even more dramatically, shutting down in 2001 after reaching a $1.2 billion valuation in 1999.

The holes in these companies’ business models may 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 in recent years, it seems inevitable that we’ll see more stories like this in the years to come.

Will the boom continue?

The big question is how much automation AI will be able to achieve. In April 2023, a Goldman Sachs report estimated that generative AI could take over 300 million human jobs; while there have been some reports of AI-related job losses over the intervening 12 months, it seems we are still a long way from obsolescence on that large scale.

So should we just be patient? Should we expect the upward trend in AI model capability to continue indefinitely? According to Dr Ruairi O’Reilly, a lecturer in the Department of Computer Science at Munster University of Technology, probably not.

“LLMs are inherently limited by the data they’ve been trained on and this hasn’t really been recognized,” says O’Reilly. So even programs that are clearly useful (like ChatGPT) may be approaching the limit of what they can achieve in the near future.

Then there’s the energy issue. “As these models get bigger and bigger, they need more computing power,” O’Reilly says. “So at some point, the efficiency of the larger models will be outweighed by the costs associated with training them.”

In fact, computing power and storage have hampered AI progress for decades. Neural networks, the machine learning processes that underlie much of today’s machine learning infrastructure, were invented in the 1990s; however, “they only became viable when computing power and storage became cheaper with the advent of cloud computing,” O’Reilly notes.

This speaks to the phenomenon of “AI winters.” The level of interest (and investment) in artificial intelligence has waxed and waned since the invention of the computer; bursts of rapid growth in the space have repeatedly been followed by long periods of low participation.

O’Reilly believes that productivity improvement is an area where AI innovators are poised to make great strides in the near future. He points to Fin, a chatbot program launched by Intercom, which has been very successful in handling customer queries without human intervention. “Programs like these will enable businesses 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 achieved.”

But there’s a problem. If the market falls due to another loss of confidence, potential success stories like this could fall flat, simply because of some 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 worry that if one big company failed, it would cause contagion; they could collapse like a house of cards,” says O’Reilly. “This could spur innovation by companies that are actually making a profit and providing value to their customers.”

Who’s making money from AI now?

While LLMs have made us more efficient, they have not entirely changed the world of work. If historical trends are anything to go by, it could take decades, rather than years, for the world of work to become automated.

However, there’s no denying the massive amounts of money changing hands to keep the current AI giant running. As noted, OpenAI has been the biggest winner on the software side, but even bigger strides have been made in the hardware space.

Nvidia, which makes the graphics processing units (GPUs) needed to train and run programs like ChatGPT, has seen stock price gains of more than 241 percent in 2023, making it the best-performing stock in the S&P 500 for the year. Its $2 trillion valuation makes it the third-largest company in the world as of April 2024.

And the company is likely to maintain this success “as long as GPUs remain the dominant force behind model training,” O’Reilly says. Nvidia controls about 95% of the GPU market today, and demand for these chips will continue to grow as developers continue to train ever more advanced machine learning applications.

Nvidia isn’t the only semiconductor company that has been performing well lately. AMD (which counts Meta and Microsoft as customers) has also made surprising gains, tripling its market cap between January 2023 and February 2024. Intel has also posted gains.

These industry giants have erected significant barriers to entry into the computing hardware space. The initial investment for a newcomer would be huge and much of the key intellectual property is protected by patents.

The hardware demands of artificial intelligence don’t begin and end with chips. Memory storage facilities, data centers, cooling systems, and networking equipment have also become more important lately.

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 2023 marked a 265 percent increase over the same period in 2022. These companies aren’t just growing on pure speculation; they’re being fueled by big wads of cash.

So while a cooling AI market will hurt hardware giants, they have already made plenty of money in the still-bright sun. Semiconductors and data centers were around long before the current AI bull run, and they will still be here long after it ends.

The flashes on the river bed

So what does the future hold? Valuable products and services will always attract demand, and it’s clear that AI has a lot of value. While there were many costly losers during the dotcom crash, 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 already down this path, and others will surely follow.

The outlook is treacherous and uncertain, however. When the next AI winter arrives, it will freeze out many nascent players in the space. Hardware vendors that have already made billions selling metaphorical shovels are far less likely to sit on the sidelines.

By Francis Walshe

Francis Walshe is a freelance writer who focuses primarily on legal, business and technology stories. He is originally from Waterford, Ireland but currently lives in Vancouver, Canada.

Discover how emerging technology trends are reshaping tomorrow with our new podcast, Future Human: The Series. Listen now on Spotifyin Apple or wherever you get your podcasts.

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