We have been following Artificial Intelligence (AI) at Manning & Napier for decades, and despite the recent buzz, AI is not a new concept.
Scientist Alan Turing wrote the first AI manifesto, “Intelligent Machinery” in 1948. Eight years later, Dartmouth College held a workshop where the term “artificial intelligence” was coined to describe machines that could simulate human behavior. AI has undergone continuous cycles of development, disillusionment, and progress in the decades that followed. Each iteration has built on the work of the previous cycle, getting closer to a state in which machines will achieve full parity with, and ultimately exceed, the capabilities of the human mind.
Years of experience lead us to believe that we still have a way to go before we reach that final state. Research firm, Gartner, developed a five-stage visual framework for technology hype cycles. A new technology emerges, generates tremendous enthusiasm, and then falls short of inflated expectations. Eventually, practical applications emerge, and mass adoption takes hold.
Gartner Hype Cycle |
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| Stage 1 | Innovation Trigger: A potential technology breakthrough kicks things off. Early proof-of-concept stories and media interest trigger significant publicity. Often no usable products exist and commercial viability is unproven. |
| Stage 2 | Peak of Inflated Expectations: Early publicity produces a number of success stories - often accompanied by scores of failures. Some companies take action, many do not. |
| Stage 3 | Trough of Disillusionment: Interest wanes as experiments and implementations fail to deliver. Producers of the technology shake out or fail. Investments continue only if the surviving providers improve their products to the satisfaction of early adopters. |
| Stage 4 | Slope of Enlightenment: More instances of how the technology can benefit the enterprise start to crystallize and become more widely understood. Second- and third-generation products appear from technology providers. More enterprises fund pilots; conservative companies remain cautious. |
| Stage 5 | Plateau of Productivity: Mainstream adoption starts to take off. Criteria for assessing provider viability are more clearly defined. The technology’s broad market applicability and relevance are clearly paying off. |
If AI is one large cycle, then the generative AI exuberance we are currently witnessing is a ‘mini hype cycle’ on a zoomed-out, upward- sloping trend. And, there have been many mini-cycles along the way, whether it was natural language processing (NLP), big data, or machine learning. Google search has included AI technology for years. Image recognition and language translation programs leverage AI technology. Generative AI pulls in many features of previous AI cycles. It promises benefits that will streamline business operations and make our lives easier – but we are already starting to see cracks. Generative AI is notorious for hallucinations. Programs generate essays, legal briefs, and other bodies of work full of fallacies and fabrications.
It’s unclear how many additional cycles will occur before we reach a state of general AI – which is, effectively, general-purpose AI with the ability of a single AI to do “everything” a human can, rather than employing many individual, application-specific AIs. We suspect it is more likely to take place over 50 years than 5 years.
We are confident, however, that the companies we look to invest in are best positioned to benefit from these advancements over time. Microsoft, Nvidia, Alphabet, Amazon, and Meta have the scale, talent, data, and computing power needed to drive rapid innovation. Data centers cost $500Million-$1Billion, and Microsoft, Alphabet, Meta, and Amazon own tens of them. Top academics and scientists are attracted to institutions with funding and resources. For example, Turing Award-winning scientist and NYU professor, Yann LeCun, works as Meta’s Chief AI Scientist. OpenAI emerged as a high-value game changer, and Microsoft invested billions to gain a large ownership stake.
As is the case in any hype cycle, we believe some stocks may be getting ahead of themselves. Stocks like AMD, Dell, Tesla, C3.ai, Palantir, UiPath, and IBM have some degree of AI hype in them. The reality is that each separate end market is nuanced. Individual companies vary in terms of competitive advantage and growth potential, and every stock has a fair value.
Looking forward, we are optimistic about the transformative potential of AI. We do not think that current valuations are so egregious that we are in a general mania or bubble territory, but we clearly see the need to be selective. Our time-tested investment strategies and experience with previous hype cycles serve as valuable tools for navigating risks and opportunities.
Just as Warren Buffet famously warned investors to be opportunistic during times of fear, we are prepared to take advantage of periods of disillusionment. The promised land for AI may be years away, but we believe that progress will continue as the hype ebbs and flows.
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This material contains the opinions of Manning & Napier Advisors, LLC, which are subject to change based on evolving market and economic conditions. This material has been distributed for informational purposes only and should not be considered as investment advice or a recommendation of any particular security, strategy, or investment product.
