Ten AI Cross-Industry Trends Shaping Our Future
We are witnessing an unprecedented pace of advancements in artificial intelligence.
Just this week, the release of GPT-4o was announced by OpenAI, a powerful new multimodal language model — text, vision, and audio. Google also announced Astra, the next-generation AI assistant, combining multimodal capabilities — a human-like assistant that can SEE, HEAR, and TALK. Meta announced it is working on AI ‘Camerabuds’, going beyond smart glasses.
The business world is being reshaped as we speak, by the potential that is emerging. Here are Ten key AI trends to keep in mind as you navigate this transformation no matter what industry you are in.
1. The War for AI Talent Intensifies
The competition for top AI talent has reached the stratosphere. Companies are offering eye-popping salaries and benefits to lure the best and brightest AI engineers, data scientists, and researchers. Google’s Sergey Brin personally intervened to retain a top employee who was being recruited by OpenAI and Elon Musk says ‘Craziest talent war I’ve ever seen’. [1] With estimates of only a few hundred people worldwide having high-level generative AI qualifications, this talent war shows no signs of slowing down.
2. GPUs Become the New Asset Class
Graphics processing units (GPUs) are the assets powering the AI revolution. The demand for high-end AI chips is skyrocketing, with Nvidia’s data center GPU sales at the end of 2023 growing at a stunning 409% rate yoy. Companies like Meta are placing massive orders for hundreds of thousands of GPUs, underscoring the critical role of this hardware in enabling advanced AI capabilities. Banks are financing the purchase or renting of GPUs — Buying, Renting, or Financing GPUs: Navigating the AI Compute Wars. Sam Altman alluded in a recent discussion at the All-in Podcast, to the idea of a Universal Basic Income (UBI) that is a slice of the computing power of GPT-7 (Check out my linkedin post on this news).
3. Digital Laggards at a Disadvantage & On-prem rising from the dead
The rapid pace of AI adoption is putting immense pressure on digital laggards to catch up. These companies are being forced to accelerate their digital transformation efforts, re-architecting their data infrastructures to prepare for leveraging AI.
The digitally mature organizations that are integrating new AI capabilities and frequently running them on-premise, signaling a shift away from the public cloud for certain AI use cases.
4. Efficiency Gains Completely Dominate Early AI Adoption
While the potential of AI to drive new revenue opportunities is immense, the vast majority of current enterprise AI adoption is focused on achieving efficiency gains.
With the exception of the health sector, in most other sectors and certainly, in financial services, all use cases are about doing existing tasks faster, cheaper, and better, rather than enabling entirely new business models or revenue streams. This suggests we are still in the very early stages of the AI transformation journey.
5. AI Risk Management Frameworks are Becoming Normal
As AI systems become more powerful and pervasive, the need for robust risk management frameworks is becoming increasingly apparent. Enterprises are starting to adopt AI risk management practices to identify, assess, and mitigate potential risks such as bias, privacy breaches, and unintended consequences. Expect to see the emergence of industry standards and best practices in this area.
There are several existing AI risk management frameworks from the pre-ChatGPT era that can be used. An example is the NIST AI Risk Management Framework which I discuss with Helen Yu on Navigating AI’s Waters | Risk Management at Enterprise Scale
6. Ethical AI Considerations Take Center Stage
The ethical implications of AI are coming into sharp focus as the technology advances. Enterprises across industries are grappling with thorny questions around bias, fairness, transparency, and accountability. There is growing recognition of the need to develop AI systems that are aligned with human values and societal norms. Ethical AI frameworks and guidelines are starting to shyly become part of enterprise AI best practices to help navigate this complex landscape. Ethical AI frameworks have been developed over the past 5 years, as the AI inflection of 2023 didn’t happen out of the blue but was the result of decades of research and advancements. The EU for example, presented Ethics Guidelines for Trustworthy Artificial Intelligence already in 2019 and in its recent AI act, there is an emphasis on Human agency and oversight, and social impact.
7. The Disruption of Intellectual Property Laws is Real
AI is forcing a rethinking of traditional notions of intellectual property (IP). With generative AI models being trained on vast amounts of web data, including copyrighted material, the lines between fair use and infringement are blurring. High-profile lawsuits, such as Getty Images suing OpenAI, are seeking to redefine IP in the AI era. While several AI models are shifting away from web scraping towards training on proprietary datasets, legal concepts around IP ownership and protection are being challenged. Watch my discussion with Tech lawyer and entrepreneur Caroline Hughes on Unpacking the AI Legal Minefield — from OpenAI to Enterprise AI users
8. Cybersecurity Faces New Frontiers
The rise of AI is introducing new cybersecurity challenges that require innovative solutions. The cybersecurity industry, already stretched thin, must make strides to leverage new AI hardware and software to gain efficiencies in existing cyber use cases, while also developing novel approaches to tackle emerging threats posed by generative AI itself. AI-powered cybersecurity is becoming a key trend, with AI-based solutions increasingly used to detect and defend against cyber attacks.
9. AI App Fatigue Sets In
While there is no shortage of new AI apps hitting the market, user retention is proving to be a challenge. Many AI apps are seeing extremely high churn rates as the novelty factor wears off and users fail to find lasting value. To succeed, AI apps will need to focus on delivering meaningful, sticky experiences that solve real user needs.
The 2024 AI Index Report published by Stanford University’s Human-Centered Artificial Intelligence Institute (HAI) highlights the challenge facing AI apps as the novelty factor wears off fast.
10. Separating AI Hype from Reality
The hype around AI is high with “AI washing” becoming rampant in the startup world. Many ventures are slapping the AI label on their offerings.
AI middleware is being developed to help connect enterprise data and applications with large language models.
AI startups that are truly investable will need to build deep moats and develop proprietary tech stacks that are not easily replicated. Cutting through the noise to identify real investable AI innovation is not easy.
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The AI revolution is still in its early stages, but its transformative impact is already being felt across industries. The Business leaders that want to lead and shape our future, will be the ones creating the new business opportunities and those creating new business.
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[1] ‘Craziest talent war I’ve ever seen’: Elon Musk says AI is hitting a new frenzy — and he helped set it off 9 years ago with Larry Page and Sam Altman
https://fortune.com/2024/04/04/ai-talent-war-elon-musk-sam-altman-openai-google-engineers/
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