The Artificial Intelligence revolution: sector perspectives

We believe that Artificial Intelligence (AI) could have over the long-term a positive impact on productivity and GDP growth. However, the impact will not be linear across sectors, especially in the early phases. Those companies that are already investing heavily in AI technologies are the most likely to see benefits to revenues and/or profitability, but disappointments will happen given increased and new competitive pressure as well as the emergence of questionable business cases in light of the involved costs.

With rising expectations around AI, the tech sector and US mega caps have outperformed strongly in recent times, leading to higher market concentration. In particular, the US tech companies have risen by 300% since the start of 2019, whereas non-tech stocks gained just 79%.¹ This poorly discriminated performance has been driven by robust earnings growth in the US Mega Cap, a supportive economic backdrop and the launch of ChatGPT 3.5 (2023) that led to a cycle of investment in AI technologies. Investors may need to consider a potential reset of the tech sector’s valuation levels, and determine the companies which are likely to be the future winners and losers across the market.

To help identify such companies, we use a framework which categorises them into four AI types: tech providers, tech enablers, deployers and disruptors. The AI tech providers (e.g. semiconductor companies) are the early winners that are well-positioned to exploit the robust AI demand, while the AI tech enablers (e.g. hyperscalers/cloud providers, data centres) are heavy investors in Al, purchasing the majority of the leading-edge AI graphics processing units (GPUs).² The AI deployers are those businesses that are already leveraging AI to transform their businesses whereas the AI disruptors are new entrants that will disrupt business processes by using AI technologies to achieve greater scalability.

There will be winners and losers in most industries. However, to be a “winner” a company needs not only to be an early-mover in terms of investing in AI, but also possess a proprietary data advantage, an existing competitive edge based on market position and an ability to innovate successfully. Otherwise, any advantages from using AI technologies could be competed away. In our view, the future winners are most likely to be found among the existing competitively-advantaged companies.

We highlight those industries which may see a meaningful impact from AI beyond the tech enablers and related suppliers, such as the software and services, media and entertainment, commercial and professional services and industrials sectors. Our attention is also focused on areas where AI will have a significant role, but the long-term outcome for growth and margins is uncertain. These include the financials, as this sector is an early-adopter, and health care, given it is already investing notable capital in AI.

To finish, we highlight findings from interviews conducted by Amundi Technology with senior financial services executives over the last six months. For example, GenAI was discussed in almost every conversation, unlike in the past when it was rarely mentioned. In addition, the rapid pace of change those businesses are now facing was emphasised. Any time frame for optimising business plans that stretches beyond six months is considered by these executives as being long-term. This pace has ensured that the best-in-class companies are seeking partners to help them navigate their challenges. In particular, attention was drawn to the importance of using AI to unlock the power of proprietary data, given that some of the AI technologies will become commoditised. AI can do general things very well, but may struggle when it comes to specifics.
 

We have experienced three periods of elevated productivity growth driven by the adoption of new technologies going back to the mid-1950s: the mainframe, PC and web browser. We believe that AI represents a new technology paradigm that could lead to technology driven productivity gains in the coming decades (see the Macro Focus below). From an investor’s point of view, the main questions are to what extent will these productivity gains result in higher earnings growth and how much of this future growth prospect is already captured in valuations.

Sector earnings and revenue growth expectations

AI technologies will not be adopted by all businesses, or sectors simultaneously, and those that are expected to benefit will do so within different time frames. In our view, the companies that are making large AI investments in an attempt to further sustain their existing competitive positions, are likely to see earlier benefits to revenues and/or earnings than those with more limited resources. As regards sectors, it will be those that are labour and/or data intensive that are the likely to benefit the most from AI over the medium- to long-term. Overall, we believe that AI could have a long-term positive impact on global earnings, but with differences across sectors and companies.

Macro Focus: long-term productivity gains from AI

As a general-purpose technology, we expect AI to affect all production factors: labor, capital and productivity. The AI tech cycle is seen as significantly different from previous cycles, as it is affecting creative and cognitive jobs as well as physical ones. Jobs will be loss while others will be transformed or created that currently do not exist. As a result, the impact on real GDP growth might be more ambiguous than productivity gains. Focusing on its impact on growth, we anticipate that AI adoption will proceed in three phases: the first will be characterised by innovation and capital accumulation, but there will be limited visibility on the productivity effect; the second will involve a widespread AI implementation and increases in productivity being achieved; and a third will show diminishing marginal returns to further adoption. In our analysis, Will AI increase economic growth?, we conclude that it will take time for productivity gains to be realised due to hurdles, such as: infrastructure availability, readiness of enabling technologies, GenAI models’ huge energy usage, regulatory uncertainty and skilled labor availability. These may dampen the speed of deployment, but in the long-run, we expect AI to be widely adopted and have a positive impact on productivity and GDP growth.

Sector valuations in a historical perspective: will we see a reset in US valuation levels?

The US tech sector’s earnings trended in line with the rest of the market between 2010 and 2017, but then embarked upon a virtuous cycle from 2018 (except in 2022), growing 2 to 3 times faster than that of the non-tech companies. This robust growth ensured that the sector rose by 300% since the start of 2019³, whereas overall S&P 500 Index gained just 79%.

In January 2022, the sector’s performance declined, as consumer demand for tech products began to normalise post Covid-19 and earnings growth weakened. In addition, high inflation increased expectations for interest rate rises which placed downward pressure on the valuations of growth stocks across the market. However, the sector’s performance turned in 2023, as tech companies began to see: an earnings growth recovery (unlike the rest of the market), partly due to cost cutting; an easing in US interest rate expectations; and the launch of ChatGPT 3.5 that led to a cycle of corporate investment in AI technologies.
This resulted in the top US stocks, in particular, the Magnificent Seven⁴ experiencing a surge in earnings and valuations. These stocks have dominated equity returns over recent times, leading to extreme market concentration. However, with significant earnings growth expectations already priced into these stocks, we believe that the rest of the market could benefit from a broadening of the rally beyond the mega caps. This has started to materialise in recent days driven by rising expectations of upcoming rate cuts from the Federal Reserve after the recent inflation report and concerns about the tech sector related to geopolitical developments.

Specifically, the sector’s strong relative profitability growth may slow, while that of the broader market could start to improve. In fact, the market’s earnings growth may cross that of the tech sector in the second half of this year. In addition, we are concerned about the sustainability of the US AI tech providers’ growth and profitability.

The vast majority of AI GPUs are being sold to a few leading cloud hyperscalers, which are producing their own chips in an attempt to reduce costs (a GPU costs approx. USD 30,000). Also, the end-customers across a variety of industries require an acceptable return on investment (ROI) on their AI-spend. If this is not achieved, these businesses may slow down their spending, thereby dampening the AI tech providers’ growth and profit outlooks. With that in mind, we may see a reset of some of the valuation levels of the US tech companies.

We use our composite valuation indicator (CVI) to assess sector valuations from a historical perspective and identify those most at risk of a valuation reset. This indicator is based on a basket of criteria relative to the market – price/book, dividend yield and price/free cash flow. We rank sectors in percentiles from 0 to 100%, according to the percentage of time a basket was the cheapest over the last 35 years (0% never been cheaper; 100% never been more expensive) (see Figure 3). Based on this data, the US semiconductor sector looks expensive (unlike the European semiconductors) while other tech areas are not at extreme valuation levels.

To help identify winners, we categorise companies according to the way AI is affecting their businesses.

Interview with Benjamin Lucas, Chief Executive Officer, Amundi Technology by Swaha Pattanaik, Head of Publishing and Digital Strategy.

Ben, you interviewed a large number of very senior financial services executives over six months to get concrete insights into what clients want and how their needs are changing. How focused were they on AI?

Firstly, it is important to never assume client needs, or indeed, that they need the same things that they needed before. It is therefore incredibly important to stay connected and to listen. I have been leading conversations like this for much of the last decade and what was really striking this time was that GenAI came up in almost every conversation over the last six months. In previous conversations, CEOs had highlighted cloud, data and talent, but rarely mentioned AI.

Was there a unifying theme that really stood out?

The scale and pace of change. We are at the edge of a precipice with massive structural changes coming in ways that we haven’t seen before. Against a backdrop of change fatigue, there is a sense that what is happening now is truly different. Typically, leaders plan in terms of optimising their business for the next three, five or maybe ten years, and anything beyond that is the long-term. In speaking to the large tech companies, that optimisation timeframe has dramatically reduced, as one leader said to me anything beyond six months is now long-term. The speed at which the landscape is changing is phenomenal and each of us is going to have to deal with this dramatic shift in time frames.

How challenging will it be to adapt to such rapid change?

People often referenced regulation as a hurdle to innovation, whilst of course accepting the need for it. We are now seeing some regulators globally embrace the newer technologies and recruiting for the skillsets to allow them to effectively regulate the application of these technologies and support the growth of the sector. What may be more of an obstacle are the cultural challenges.

Leaders will have to overcome the normal human inertia when it comes to change. They may also face ‘change fatigue’, as leaders have been grappling with wave after wave of new ideas and technologies – blockchain, tokenisation, cloud – with wildly varying levels of success. There is such an immediate opportunity, that the incentive to get this right is large.

What are the best-in-class companies in financial services doing?

They are really looking at it in terms of a broad restructuring of the way they do business, and the leadership’s incentives are aligned with the long-term imperative to adapt to this fast-changing world.

The other distinguishing feature of the companies that are outperforming, is that they realise they can’t work in isolation from the big tech firms. Financial services companies won’t outcompete Big Tech when it comes to AI scientists or investment in AI. But there’s really no need to compete. The important thing is to choose partners that can see what’s coming next and that can help them to navigate their specific business challenges.

Another thing that will determine whether a company is successful in the future is the intellectual property that is specific to that business. This typically exists in a broad range of places – in the heads of their employees right through to their own proprietary data. How they use AI to unlock the power of it will determine their success.

Speaking of employees, what sort of work will people working in financial services do in the future?

When you move to a world of hyper-automation and mass-distributed AI bots, there will be much more machine-to-machine interactions and fewer human-to-machine interactions. And when you talk to academics or big tech firms, their perspective is that on aggregate, the machines will do things better than humans as the technology progresses.

But this is also exactly the sort of environment in which human capabilities will be massively leveraged. There will be some element of human oversight and interaction when it comes to AI, and every human decision will have a much greater impact than is currently the case. AI becomes a force multiplier for humans.

This won’t be a case of people doing well purely because they know how to write prompts for AI. The real edge that people will have over machines will be their domain expertise, whether it is agriculture, manufacturing or financial services. AI can do general things very well but may still struggle when it comes to specifics, especially when data isn’t a matter of public record. This is also where companies can differentiate themselves. Everything that sits in a company’s private database and that isn’t shared is a differentiator. This data becomes incredibly important, as is their employees’ knowledge of how to do things. Elements of the technology will become commoditised, and how boldly it has been applied to the business model will be the differentiator.

Given the importance of this data, how can privacy concerns be addressed?

Talking about regulating AI itself is going to be hard because the people who are working on the cutting edge of this technology are not, for the most part, working for regulators. The better bet in the interim, though this may not be perfect, is to try and regulate the end activity – helping companies explain exactly what they are doing and how it benefits the end client. The bigger question is what role should governments and policymakers play in protecting human activity.

Will developed economies, and in particular the US, always have an entrenched lead in AI?

India is a really interesting case in point when you’re thinking about this question. A company that is a leader in AI may have a legal entity in the US, but a lot of the actual work is done in India. There are literally millions of Indians focused on these technologies right now. A lot of the skills and capabilities already sit in India. On top of that, the country is aggressively expanding its public digital infrastructure, with a very clear goal of where it wants to be. In many ways, you could argue that India is already among the leaders of the pack.

As mentioned previously, to be a “winner” a company needs to have a proprietary data advantage, an existing competitive edge based on its market position and the skills to innovate successfully. Otherwise, any advantages from the deployment of AI could be competed away. Generally, other businesses may have to spend just to keep up with the winning company/ies in its sector, and yet over time, they may not be able to retain any AI advantages. In our view, the future winners will be the existing competitively-advantaged companies.

We highlight below some details on a few industries where AI is expected to have a meaningful impact and benefit the deployers, such as the software and services, media and entertainment, commercial and professional services and industrials sectors. We also focus on areas where AI is expected to have a role, but the long-term outcome for industry players’ growth and margins is very uncertain. These include financials, as this sector is an early adopter, and health care, as notable AI investments are being made by the large companies.

1. Data to 18 July 2024 from 1 January 2019. Data refers to the S&P 500 Information Technology (IT) and ex IT Indexes.
2. A graphics processing unit (GPU) is a computer chip that renders graphics and images by performing rapid mathematical calculations.
3. Source Bloomberg, data as of 18 July 2024. Data refers to the indexes in Figure 1.
4. US Top tech/ tech-driven stocks - Nvidia, Meta Platforms, Tesla, Amazon, Alphabet, Microsoft and Apple.
5. Software as a service infers that the software lives on a SssS company’s server and the user accesses it remotely.
6. Data modernisation ensures that databases are transformed into a high-quality data which can provide valuable insights to businesses.
7. A data stack refers to the tools and technologies used to gather, store and process data in a scalable and cost-effective way.