Are we headed for another AI winter?
I remember the 1999 tech boom like it was yesterday. I was at Merrill Lynch and the average first-day premium for a Nasdaq IPO was 90 per cent! What stood out was investors’ predilection for anything with a ‘.com’ attached to it. Plenty of companies, even junior mining explorers, whacked a .com on the tail of their changed name and watched their valuations surge. Today, I’m seeing the same trend with artificial intelligence (AI).
Amid the rising tide of enthusiasm for it, companies are adorning their technology with an ‘AI’ label, hoping it will see their shares skyrocket in sympathy with genuine AI companies.
Take the example of the BBC’s interview with former Beatle, Paul McCartney, who shared how AI was aiding the remaining Beatles to create a new song featuring the voice of John Lennon, who was assassinated in 1980.
Before getting too excited, let’s clarify the reference to AI. John Lennon’s voice is not being resurrected. John Lennon sang the words of the “fresh” song, titled “Now and Then,” in front of a microphone in 1978 for a demo, the quality of which is considered low by today’s audio standards.
Sound technology has advanced exponentially since 1980, and today’s tools can easily extract a singer’s voice from a scratchy and aged recording. Importantly, this tech is not AI. The technology is similar to the audiovisual pattern recognition applications that have existed for decades. Remember the holographic images and voice of Elvis performing a duet on American Idol with Celine Dion in 2008? Or Whitney Houston’s posthumous tour of the UK and Ireland in 2020?
By calling it AI, promoters may be overstating reality and implying a level of intelligence or autonomy that is undeserved. Today’s AI reality is not Hollywood’s AI fiction.
And while Google’s search engine, iPhone’s face-recognition unlocking feature, and Facebook’s newsfeed algorithm has been operating under “AI-driven” technology for years, what we are witnessing today is the categorisation of AI so wide that it embodies almost any large-scale, big-data, statistics-based computer program.
It goes without saying the launch of the current wave of AI hype was last year’s debut of ChatGPT, which highlighted the impressive dialoguing capabilities of modern large language models (LLMs).
And while ChatGPT is a captivating program, it has natural limits, and as I have argued previously, it’s not a given that AI will do anything more than produce incremental revenue for established technology companies or help a company merely maintain its current competitive position.
Tagging everything with AI, may of course help share prices for some companies rise in the short-term, but my prediction is it will end painfully for many investors.
Perhaps it’s also worth knowing that AI has passed through several false dawns and subsequent “winters”. The “artificial intelligence” phrase was coined in the 1950s to describe the ambition of mirroring human cognitive abilities through code and circuitry, a goal experts then thought might be achieved by the 1970s!
Scientists’ initial attempts turned out to be tedious and unfruitful, funding dried up, progress stagnated and AI went through its first winter. Then, in the 1990s and 2000s, it was neural networks that offered promise. Instead of meticulously organizing information for the computer to process – as was proposed in the 1950s – this method had increasingly powerful super-computers ingest massive volumes of unstructured data to discern patterns. Another winter.
As news outlet, Axios, recently noted, “The ubiquity of the “artificial intelligence” category in tech today might be the most phenomenally successful act of rebranding in corporate history.”
However, beneath the surface, many of the new AI products being unveiled by Silicon Valley are primarily just efficient upgrades of technologies we’ve been interacting with for a long time.
As articulated by Meredith Broussard, an academic at NYU and a known critic of AI, in a recent interview with The Markup, “Once you use it, AI feels mundane. It just feels like using any other technology.”
Broussard is an accomplished data journalist and associate professor at New York University, and she has spent years exploring the potential pitfalls of AI in the academic sphere, consistently criticising “technochauvinism” – a term she coined in her 2018 book, “Artificial Unintelligence,” to describe the seemingly blind faith in technology as a panacea for all of the world’s problems.
In Broussard’s latest book, “More than a Glitch,” she examines various real-world examples that reveal the profound and sometimes troublesome ways AI is already – perhaps adversely – impacting various aspects of our daily lives, from soap dispensers failing to recognise darker skin tones to the way AI technology provided her with a breast cancer diagnosis.
Thus, the latest hype or fad in AI may arguably be best summarized by Broussard herself: “It would be really great if we could diagnose more people earlier. It would be great if we could use technology to save more lives from cancer. We are absolutely all united in that goal. But the idea that AI is going to be our salvation for diagnosing all cancers in the next few years is a little bit overblown.”
Investors have been warned; another winter may be just around the corner.
Roger Montgomery is the Founder and Chairman of Montgomery Investment Management. Roger has over three decades of experience in funds management and related activities, including equities analysis, equity and derivatives strategy, trading and stockbroking. Prior to establishing Montgomery, Roger held positions at Ord Minnett Jardine Fleming, BT (Australia) Limited and Merrill Lynch.
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