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  • AI Bubble Bursting into AI Winter – yes or no? (History of LLMs #5)

AI Bubble Bursting into AI Winter – yes or no? (History of LLMs #5)

Navigating the AI Hype: Deep Analysis of the Situation with Strategies For Investors

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Suddenly, the machine learning (ML) turf became the new wild west, with Generative Artificial Intelligence (GenAI) capturing minds and igniting a rush. Evoking the feverish pursuit of gold in 19th-century California, GenAI's potential has led to an investment stampede. But is this new frontier a path to prosperity or a bubble about to burst? Are we experiencing the overbearing excitement and heightened expectations that could lead to a new AI winter, or are we on the threshold of a new economy and a never-ending AI summer? Let's see.

Such Different Views

Within eight weeks of OpenAI's CEO requesting the government to regulate AI, Stability AI's CEO warned that AI could be ‘the biggest bubble of all time’. Strategists at JPMorgan Chase and Morgan Stanley have raised similar red flags. While a Wells Fargo Securities expert saidIt’s not a bubble,’ pointing to more realistic valuations and earnings expectations for AI than there were for early internet companies in the late 1990s, and that companies now have immediate commercial uses for AI tech.

Meanwhile, companies and VCs are channeling massive funds into high-profile generative AI. The investment picture was looking good for ML companies the last three to four years, but the real gold rush began last year — in December after ChatGPT was launched and a while later Microsoft poured $10 billion into the chatbot's parent company, OpenAI.

These days, AI is enjoying a lot of attention, but the technology has not penetrated enough yet for investors to get a ballpark figure for its return on investment (ROI). “As there are immense possibilities with AI, navigating through it and making sure there is a reasonable ROI is one of the major concerns businesses have. This is because there is a significant investment in infrastructure, software, and personnel,” said Balakrishna D. R., Executive Vice President – Global Head, AI, and Automation and ECS, at Infosys.

As of May 2023, 598 AI & ML companies have received 114 investments worth $66,220,051,114 as per an open data-source. According to our own data, another $7+ billion has been invested since June. It has raised concerns about being in an AI bubble that is about to burst and the following AI winter.

Oh, but We Saw the Bubbles Before!

  • dot.com. Some compare the current craze with the infamous dot-com bubble in the late 1990s. The bubble, similar to most historical bubbles, eventually met its demise with a crash as investors realized that their expectations would not be fulfilled and exited their positions. Once revenue at the telecoms dipped dramatically, it rippled through the end markets and eventually pulled the economy down into the 2001 recession.

  • ICO (initial coin offering) craze. Fast forward to the late 2010s. When the Long Island Iced Tea Corp. changed its name to Long Blockchain Corp, it caused a hike in share price to 380%. ‘Blockchain’ was a magic word back then. It resulted in orgy of fraud and scammy behavior with over 80% of all ICOs during the boom being outright scams. The crypto industry, now known as web3, still can't fully overcome that reputation.

Signs of the AI Bubble

The question of whether we find ourselves immersed in an AI bubble specifically is a topic that both ignites our imagination and stirs a hint of trepidation. Here are the signs of an AI bubble that we think might be helpful:

  • Towering stakes placed upon AI and the astounding levels of funding pouring into startups that sometimes wield AI as a mystical talisman as if uttering the sacred word guarantees an abundance of investor favor. Yet, amidst this seemingly feverish pursuit, the profitability of these companies remains a looming question mark.

  • Meanwhile, a frenzied interest from the public and media amplifies the sensation of AI's omnipresence, perpetuating an atmosphere of grandiose claims and exaggerated expectations.

  • In this swirl of commotion, one can hardly escape the swift-talking wave of research, with social media feeds awash with tweets proclaiming, "I have birthed my very own chatbot—behold my GitHub!" Amidst this cacophony, it becomes increasingly difficult to discern the genuine contributions of dedicated researchers and to truly grasp the genuine transformations transpiring within the realm of AI.


But what many people constantly forget about AI is that it’s not a new phenomenon. Artificial Intelligence (specifically, machine learning) has been under heavy research and in an active implementation stage for more than half a century. It was in the 1980s when expert systems transformed AI from an academic field to practical applications. During its already long life, the AI industry has already experienced at least five winters and come out of it stronger than ever.

Also, the present companies that work with AI are well-established, well-organized, and financially secure. So comparing it to the dot.com bubble and ICO craze is both relevant and incomplete. There is already a huge amount of practical use of AI and ML; it's just that the hype around GenAI has raised a wave of interest from the general public and over-investing tendencies from VCs.

Maybe AI Bubble is Around GenAI?

GenAI is certainly the latest promising technology that is publicly available and has many immediate use cases. Which raises a wave of interest, and inevitably, hype.

Some experts don’t help with lowering that hype: painting a clear picture of the rapid rise of generative AI, McKinsey’s latest research estimates that the technology could add the equivalent of $2.6 trillion to $4.4 trillion annually across the 63 use cases the team analyzed (by comparison, the United Kingdom’s entire GDP in 2021 was $3.1 trillion.)

That's a lot of money, a VC thinks. Not only a small VC thinks so; big players are ready to play all in. Cognizant and Accenture have been bitten hard by the GenAI bug since the investments are flowing by the millions every week. The hype is palpable as companies plan to continue investing in content-generating technology. Even Softbank is back in the AI game again, after investing $140 billion with not much result. 

Just like all AI booms that have been followed by desperate AI winters, the media tends to exaggerate the significance of tech developments. In this grand AI soiree, distinguishing the maestros from the mimics is becoming an art in itself. But are we at the threshold of another AI winter? Are current ambitious claims and companies’ high evaluations a path to disappointment?

Winter has yet to come

Here is an interesting thought from Paul Graham’s post “What Bubble Got Right”: ‘Recognizing an important trend turns out to be easier than figuring out how to profit from it. The mistake investors always seem to make is to take the trend too literally. Since the Internet was the big new thing, investors supposed that the more Internettish the company, the better. Hence such parodies as Pets.com. In fact, most of the money to be made from big trends is made indirectly. It was not the railroads themselves that made the most money during the railroad boom, but the companies on either side, like Carnegie's steelworks, which made the rails, and Standard Oil, which used railroads to get oil to the East Coast, where it could be shipped to Europe.’

What if GenAI is the new railroad? It might be, at least for the creator economy. As it significantly accelerates the work of a creator. By creator, we don’t only mean influencer. The work with language and communications is ubiquitous. From government bodies to military departments, from journalism to programming – it’s all based on language.

The GenAI craze instigated not only investments but also prompted government agencies to dive into and create new positions and task forces. Department of Defense (DoD) announced the establishment of a generative AI task force. The Senate Homeland Committee has moved forward with a bill that'll make sure every federal agency has a Chief AI Officer, and that they're all working together with a unified plan for AI. Both the US Coast Guard and the Department of the Air Force already got their own Chief Data and AI Officers, while the Air Force also got a Chief Responsible AI Ethics Officer. What a name. But the work they are doing is impressive, and it shows the dedication to deal with GenAI and AI in general properly.

The Defense Advanced Research Projects Agency (DARPA), famous for supporting the first AI steps last century, is not currently that financially lavish (the last news about their AI investment was in 2018), but it’s participating as a partner with companies such as Google DeepMind and Anthropic, Microsoft, and OpenAI for the AI Cyber Challenge, a two-year competition aimed at driving innovation at the nexus of AI and cybersecurity.

The Bottom Line

So, is an AI bubble possible? Well, it's a nuanced situation — interplaying between expectation and reality. To answer definitively requires a bit of finesse. One could argue that a bubble does indeed exist, looking at how fast startups are jumping on the bandwagon and securing substantial funding rounds. Another facet of this bubble can be not paying enough attention to the technology's potential and repetitively working only on ‘generative AI’. The GenAI industry is just getting started, and investments have already skyrocketed.

Unlike any previous bubbles, the generative AI bubble is unlikely to burst in the same spectacular fashion. Because AI is delivering tangible results, it's helping companies do everything from improving efficiency to diagnosing diseases through AlphaFold. The content-producing tech being ‘productized’ has hundreds of applications, disrupting the way humans learn, create, and work.

However, there's a caveat. If companies continue to misuse the 'AI' label for every digital product, we risk inflating the bubble. The focus should be on creating useful AI tools, not just on riding the AI hype wave.

As the industry matures, investors should brace for volatility, particularly with high-valued tech names. The best advice is to understand the practical implications of the technology and invest across a diverse range of companies, to mitigate risks tied to short-term success stories.

With this episode about AI Bubble, we conclude the series about the fascinating story of LLMs and their predecessors, which we’ve covered:

Stay tuned for the next fascinating series! Coming soon.

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