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Generative AI has burst onto the scene following the launch of ChatGPT in 2022. As a concept, artificial intelligence (AI) is not new, and Generative AI represents the latest inflection point in the evolution of AI. However, what is distinctive about Generative AI is the tremendous potential it holds to transform work across industries and boost overall productivity. Taking a more holistic view, Generative AI might not only bring the power of AI itself to the masses but in fact accelerate the wider democratization of innovation. We believe it is a game changer. Although it feels like Generative AI has come out of nowhere, the rise of AI starting in the 1950s and through its significant growth over the past decade. This growth has brought with it a host of potential opportunities and challenges. The first wave of potential opportunities for Generative AI is centered on the technology value stack. Historically, the Silicon layer has been the de facto foundation of almost all technological shifts in the technology value stack, and Generative AI is expected to drive significant growth for compute (i.e., processing power), networking, and memory chips. However, we see opportunities in each layer. In the Infrastructure & Platforms layer, we see the hyperscalers/cloud providers racing today to build the underlying infrastructure that enables Generative AI applications and services, but over time we expect to see higher or more differentiation. When it comes to Models and Machine Learning Operations (MLOps), the open-source community is likely to be a key driver of innovation. Moving further up the stack, we believe nearly all software companies will be impacted in some form by Generative AI, and company-specific execution will be critical. Lastly, we believe Generative AI represents a step forward from ongoing AI/automation initiatives at the Services layer. Opportunities are not just limited to the technology value stack —they are also spilling into sectors outside of technology. To capture the full picture, we extended our analysis to look at the impact of Generative AI across six supersectors. We do this through a two-stage framework to assess risk/reward and apply that broadly across companies and sectors. Our analysis finds the Financials & FinTech supersector to be the most likely to be impacted overall, followed by the Consumer sector. At the other end of the spectrum, Natural Resources & ClimateTech at this stage look the least likely ones to be impacted. Key challenges include those centered around bias, inequality, authenticity, infringements as well as the more debated one underpinned by existential concerns. The emergence of Generative AI has unsurprisingly seen AI as a broader topic become a firm focus for policy makers around the world. However, the regulatory path taken so far has varied. Given the stakes involved, we believe policy and governance evolution will play a defining role. What does the future look like for Generative AI? We investigate the global trends and growth is from the perspective of investment in technological innovation. We do this by analyzing the number of AI-related patent applications over time and across countries. Research papers are also telling, with the total cumulative AI research output increasing 1,300% between 2003 and 2021. Given the importance of AI as a foundational technology, the race is on between countries for scientific and technological dominance.

Generative AI has burst onto the scene following the launch of ChatGPT in 2022. As a concept, artificial intelligence (AI) is not new, and Generative AI represents the latest inflection point in the evolution of AI. However, what is distinctive about Generative AI is the tremendous potential it holds to transform work across industries and boost overall productivity. Taking a more holistic view, Generative AI might not only bring the power of AI itself to the masses but in fact accelerate the wider democratization of innovation. We believe it is a game changer.
Although it feels like Generative AI has come out of nowhere, the rise of AI starting in the 1950s and through its significant growth over the past decade. This growth has brought with it a host of potential opportunities and challenges. The first wave of potential opportunities for Generative AI is centered on the technology value stack. Historically, the Silicon layer has been the de facto foundation of almost all technological shifts in the technology value stack, and Generative AI is expected to drive significant growth for compute (i.e., processing power), networking, and memory chips.
However, we see opportunities in each layer. In the Infrastructure &amp; Platforms layer, we see the hyperscalers/cloud providers racing today to build the underlying infrastructure that enables Generative AI applications and services, but over time we expect to see higher or more differentiation. When it comes to Models and Machine Learning Operations (MLOps), the open-source community is likely to be a key driver of innovation. Moving further up the stack, we believe nearly all software companies will be impacted in some form by Generative AI, and company-specific execution will be critical. Lastly, we believe Generative AI represents a step forward from ongoing AI/automation initiatives at the Services layer.
<img src="/rcs/v1/media/citigpa/akpublic/storage/retrieveMediaFile/64f7dc62b7581721266e09ae.png" alt="Tech" />
Opportunities are not just limited to the technology value stack &mdash;they are also spilling into sectors outside of technology. To capture the full picture, we extended our analysis to look at the impact of Generative AI across six supersectors. We do this through a two-stage framework to assess risk/reward and apply that broadly across companies and sectors. Our analysis finds the Financials &amp; FinTech supersector to be the most likely to be impacted overall, followed by the Consumer sector. At the other end of the spectrum, Natural Resources &amp; ClimateTech at this stage look the least likely ones to be impacted.
<img src="/rcs/v1/media/citigpa/akpublic/storage/retrieveMediaFile/64f7dc83c1acef4c8ae226e1.png" alt="Supersectors.JPG/" />
Key challenges include those centered around bias, inequality, authenticity, infringements as well as the more debated one underpinned by existential concerns. The emergence of Generative AI has unsurprisingly seen AI as a broader topic become a firm focus for policy makers around the world. However, the regulatory path taken so far has varied. Given the stakes involved, we believe policy and governance evolution will play a defining role.
What does the future look like for Generative AI? We investigate the global trends and growth is from the perspective of investment in technological innovation. We do this by analyzing the number of AI-related patent applications over time and across countries. Research papers are also telling, with the total cumulative AI research output increasing 1,300% between 2003 and 2021. Given the importance of AI as a foundational technology, the race is on between countries for scientific and technological dominance.

What does the future look like for Generative AI? One way to investigate the global trends and growth is from the perspective of investment in technological innovation. We do this by analyzing the number of AI-related patent applications over time and across countries. Research papers are also telling, with the total cumulative AI research output increasing 1,300% between 2003 and 2021. Given the importance of AI as a foundational technology, the race is on between countries for scientific and technological dominance.

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Generative AI represents the latest inflection point in the evolution of artificial intelligence (AI). Although AI is not new, what is distinctive about Generative AI is the tremendous potential it holds to transform work across industries and boost overall productivity. We look at the opportunities for AI not only in the technology sector but across six supersectors. Given the race to dominate in the AI space, we also analyzing research papers and patents by country.

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The total cumulative AI research output—that means the number of papers, reports, studies that touch on AI—increased 1,300% between 2003 and 2021. Given the importance of AI as a foundational technology, the race is on between countries for scientific and technological dominance. 