AI and the rise of Total Experience
April 2024
Want to know the future of AI? Follow the money!
The last few years has seen a wide range of incredible technical advances in AI and related areas. From computers now capable of performing a billion instructions every billionth of a second to self-taught algorithms capable of learning to play Go from scratch and beat the world’s best players. When thinking about the future of AI, it’s hard not to look at these examples and speculate about what new capabilities will come next and how they will be used.
Yet, I think for many of us concerned with where AI is heading, this may well be the wrong starting point. Taking this technical perspective may be exciting, but it can also be misleading. By adopting this engineering viewpoint, most digital leaders and industry executives lacking the technical fundamentals will find that the potential of AI remains shrouded in incomprehensible jargon and obscured by futuristic promises. Instead, a deeper understanding may be gleaned by adopting the simple adage: follow the money. It is the financial landscape of AI – how companies are investing, generating revenue, and ultimately, transforming entire business models with AI – that may offer them the greatest insight into the future of the AI revolution.
Over the past few years, AI has transitioned from being the focus of researchers and scientists to be very firmly in the cross-hairs of business. The new wave of excitement in AI across the public and private sectors has a clear driver: AI promises to automate tasks, improve efficiency, and generate data-driven insights, all of which translate to potential cost savings and profit gains. Private companies see AI as a way to create new products and services faster, cheaper, and better suited to client needs for maximum revenue. Public organizations highlight AI as a way to optimize operations, reduce service backlogs, and contain growing costs.
While of significant direct benefit, there are also important indirect benefits from this results-based view of AI adoption. This financial motive fuels research, attracts investment, and drives the rapid development of the field, making AI’s potential as much about economic advantage as technological advances.
When seen through this economic lens, there are many avenues to explore. In my experience, there are 4 key perspectives to adopt when gaining an appreciation AI’s current and future directions: Selling the AI Stack; Research and Government Spending on AI; How VCs are Investing in AI; and New Business Models Enabled by AI.
The rise of AI has spurred the development of a robust “AI stack” – a collection of hardware, software, and services needed to build and deploy AI solutions. From specialized graphic processing units (GPUs) to robust cloud computing platforms and sophisticated algorithms, companies are actively investing in building and acquiring these components. The BigTech companies like Google, Amazon, and Microsoft are all vying for dominance in this market, offering comprehensive AI toolkits like Google Cloud TPUs, Amazon SageMaker, and Microsoft Azure Cognitive Services. This intense competition drives down costs and fuels innovation, making AI solutions increasingly accessible to organizations of all sizes.
It is worth remembering that fuelling these technologies requires an incredible amount of power. In January 2024, Mark Zuckerberg described the computer power needed to train their Llama 3 AI model. He revealed that by the end of 2024 Meta will have AI hardware with the performance equivalent to 600,000 Nvidia H100 GPUs (the fastest GPU processors currently available). The other major AI tools and solutions vendors are acquiring similar amounts with the result that in 2023 the backlog for these chips resulted in a waiting time of up to year for Nvidia’s AI GPUs, and prices up to $70,000 per GPU in China where sales are restricted.
As a result, this resurgence of interest in AI has caused a major surge in stock prices for companies involved with AI technology. Investors are eager to capitalize on the potential of AI, with significant discussion on which businesses will be the short and longer-term winners.
To understand this better, The Economist examined the different layers of the AI stack, which includes hardware, software, and cloud platforms. They explored just how rich these businesses are getting in the current “AI Gold Rush” and how it is creating opportunities for other companies hoping to capitalize on AI’s disruptive influences. To do this, they considered four distinct layers of the AI stack particularly relevant to the current wave Generative AI solutions: hardware, cloud computing platforms, AI models, and applications.
According to the Economist’s review, the biggest winners so far have been hardware makers, particularly Nvidia, which controls most of the market for AI chips. They observe that the value of hardware companies has grown by $3.5 trillion since October 2022 (when OpenAI announced ChatGPT). Eyeing this opportunity, this area has become a prime target for the major cloud computing companies such as Amazon, Microsoft, and Google now starting to develop hardware for their own growing AI needs and to sell to others. There are major barriers to entering the hardware manufacturing market for high performance GPUs. Yet, these companies may well have the data, talent, and resources to develop these capabilities and control the more of the AI stack.
Another big winner identified in the Economist article is the independent model-makers, such as OpenAI, the creator of ChatGPT. They note that the value of these companies has grown from $29 billion to $138 billion in just 16 months. However, they also comment that competition in this space is fierce, and it is unclear which companies will ultimately prevail.
With all this AI power on tap, the application layer is also growing rapidly, with new AI-powered applications emerging all the time. The Economist observes that the value of application companies has grown by $1.1 trillion since October 2022. However, it also notes that we are in the early days of AI application adoption and many of these applications may struggle to be profitable due to the low barriers to entry in this space.
Finally, with AI capabilities and applications relying on reliable global access and reach, cloud computing platforms such as Amazon Web Services, Microsoft Azure, and Google Cloud Platform have seen their market value grow by $2.5 trillion since the AI boom began. Perhaps not all of this growth is due to AI; however, it has undoubtedly it has been a major contributor. The Economist estimates that this growth is less than the hardware layer in absolute terms, but it is a much larger multiple of their expected near-term AI revenue. This suggests that investors believe that cloud giants will be the biggest winners for AI adoption in the long run, as they are best positioned to control the entire AI stack and capture the most value form its widespread use.
The private sector is not alone in propelling the AI revolution. Governments and research institutions around the world recognize the transformative potential of this technology and are investing heavily in research and development (R&D). The United States, China, and the European Union are leading the charge, with initiatives like the US National Artificial Intelligence Research and Development Strategic Plan, the EU Digital Europe Programme, and China’s Next Generation Artificial Intelligence Development Plan. This substantial public funding fosters innovation at universities, research labs, and startups, accelerating advances in core AI disciplines like machine learning and natural language processing.
The level of government funding being directed into AI is staggering. For example, the EU Digital Europe Programme commits over 7.5 Billion Euros to fund projects in five related areas: supercomputing, AI, cybersecurity, advanced digital skills, and ensuring the wide use of digital technologies across the economy and society. Meanwhile, in the US, in President Biden’s FY 2024 budget requested billions in new funding or AI-related research, hardware, software and services at the departments of Defense, Energy, Homeland Security, Health and Human Services, Transportation, and other agencies throughout the federal government. Similarly, in the UK the government has made a series of funding announcements including over £1.5 billion of investment in compute infrastructure to significantly enhance the amount of state-of-the-art compute available to our researchers to enhance research and enable the UK to apply AI to improve public services.
Furthermore, government agencies are refocusing their spending plans toward AI and increasingly deploying AI solutions themselves. Applications range from facial recognition systems for security purposes to AI-powered chatbots for citizen service. This not only will this boot productivity and streamline government operations, but it also serves as a powerful validation of AI’s efficacy, encouraging wider adoption within the private sector.
Venture capitalists (VCs) are known for their keen sense of where the next big opportunity lies. Unsurprisingly, they are pouring significant resources into AI startups. Goldman Sachs predicts that upto $200 billion will be invested globally in AI by 2025. This influx of capital fuels the growth of innovative AI companies, allowing them to refine their technologies, expand their reach, and develop groundbreaking new applications.
The types of AI companies attracting VC attention are diverse. This includes healthcare startups developing AI-powered diagnostics and drug discovery tools, retailers investing in AI for personalized product recommendations and enhanced customer experiences, transportation organizations designing autonomous vehicles powered by AI, and much more. These diverse applications highlight the broad impact of AI and the vast potential for financial returns, attracting even more VC investment and accelerating the pace of innovation.
Overall, some reports highlight that VCs are now beginning to target new styles of AI investing. This involves a shift in AI investment with a focus on practicality and away from hype. Several trends are already emerging:
Perhaps the most compelling aspect of AI for many organizations lies in its ability to disrupt traditional business models and create entirely new ones. Companies are leveraging AI to learn more about operational activities to allow them to automate tasks, streamline processes, and gain deeper insights into customer behaviour. This translates into significant cost reductions, increased operational efficiency, and the ability to deliver a new generation of highly personalized products and services. Businesses seeing these opportunities are either evolving, pivoting entirely, or being disrupted by new AI-powered competitors.
Of course, adopting AI capabilities presents a particular opportunity for Small-Medium Enterprises (SMEs). Smaller, more flexible companies can use their agility to move faster than established businesses. They can create AI-powered versions of existing businesses, offering similar services at a lower cost. This strategy can disrupt entire industries, similar to how Uber’s model transformed the transportation sector.
Industry leaders like Eric Schmidt, former Google CEO, recognize this opportunity. He urges companies to embrace AI, highlighting the risk of being left behind. He highlights sectors with high demand and limited resources, like healthcare and education, as presenting significant opportunities for AI-powered solutions. Entrepreneurs can address these challenges and reduce inequality through rapid AI adoption.
However, the business models of Large Established Organizations (LEOs) can also be disrupted through AI. Often this occurs by replacing existing operational practices with more intelligent, automated solutions. For example, AI-powered chatbots can provide 24/7 customer support, reducing reliance on expensive call centres. Predictive algorithms can anticipate customer needs, allowing businesses to pre-emptively deliver relevant products and services. In addition, many HR and project management tasks can be transformed by AI, with smart approaches to skills development and platforms utilizing AI to match workers with tasks more efficiently.
These are just a few examples of how AI is fostering entirely new business models. As AI capabilities continue to evolve, we can expect even more disruptive and innovative approaches to emerge across all aspects of organizations, large and small.
While the technical advances in AI are undeniably impressive, a deeper understanding of the financial landscape surrounding AI offers a more grounded perspective on its future trajectory. AI has become a magnet for investment, with significant funding flowing from private companies, venture capitalists, and governments. This financial fuel is propelling research and development, driving innovation, and fostering the creation of entirely new business models.
This financial perspective offers a valuable lens through which to examine the future of AI. By following the money, we can see how different actors are investing in the “AI Stack” – the hardware, software, and services needed to build and deploy AI solutions. We can also observe the surge in venture capital funding for AI startups across diverse industries, from healthcare to transportation. Finally, we witness the transformative potential of AI on traditional business models, with established companies either adapting or being disrupted by new AI-powered competitors.
Understanding the financial forces driving AI is crucial for anticipating its future impact. Leaders can gain a deeper understanding of AI’s potential and future directions by focusing on the financial implications of AI. By investing in AI solutions and exploring the potential for new business models, organizations can secure a competitive edge and thrive in the rapidly evolving digital landscape.
Originally posted here
April 2024
April 2024
April 2024