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Nearly 80 years before Edison invented the lightbulb, electricity had already existed. What Edison did through the invention of the lightbulb was to bring about a practical and accessible way to convert electrical energy into light. This innovation paved the way for widespread adoption in homes, businesses, and cities, fundamentally altering how people lived, worked, and interacted with their surroundings.
Similarly, while AI has been around for several years, what we are witnessing today is its increasing accessibility thanks to recent advancements in GenAI. We recently had the opportunity to sit down with Cliff Justice, KPMG’s US head of Enterprise Innovation, to delve into the transformative power of AI and its implications for individuals and businesses. Our enlightening discussion shed light on this exciting frontier.
Here’s a breakdown of our key insights from our conversation….
From Edison’s lightbulb to GenAI’s brilliance, we stand on the brink of unimaginable transformation.
During our discussion, Cliff drew parallels between GenAI’s impact and the electrical revolution ignited by Edison’s invention. At that time, electricity had existed for decades, but its practical applications were limited. However, with the advent of the lightbulb, a transformative shift occurred. As Cliff put it, “Once the practical light bulb came along and the average middle-class home could illuminate their house, and factories could install them, enabling 24-hour productivity, investment in electricity generation and the transmission grid followed, enabling many subsequent innovations adjacent to the lightbulb, like electric motors, electronics, and air conditioning.”
Much like the lightbulb, the development we’re currently experiencing has been unfolding over the past 5-7 years, where AI has transitioned from specific, tightly controlled use cases to becoming a practical and cost-effective tool with broad accessibility. This shift has led to a “network effect,” attracting more data, investment, and excitement, resulting in rapid progress—the implications of this progress span across various sectors, from education to fueling entrepreneurial ventures. ” in 2022, not many people knew what GPT was, and now it’s in your kids’ vocabulary and on their phones.”
While we witness its growing productivity capabilities, AI’s transformative influence will extend far beyond productivity. Cliff contemplated in our discussion, “What’s after GPT 4? What’s after the diffuser model? What comes next? There are a lot of potential technologies that come next. The innovation dollars flowing into these technologies will accelerate that.” The horizon is filled with unimaginable possibilities. It’s “going to happen a lot faster because we already have the electrical grid – it’s called the cloud.”
Geopolitical realities and human adaptation could throw a wrench in AI’s advancements.
Amidst this promising landscape, Cliff noted several limiting challenges. However, these challenges are less about the technology itself and more rooted in human factors and the supply chains that underpin it:
- Legacy operating models: Established companies with entrenched operating models may need help adapting to AI effectively. “Organizations that are very traditional and have a deeply entrenched operating model will face challenges in making the necessary changes to compete with AI-native businesses.”
- Reskilling the workforce: Achieving AI integration demands a workforce skilled in using AI technologies effectively. Cliff highlights the importance of this by noting that companies must quickly reskill their employees to work confidently with AI tools while ensuring policies are in place to prevent potential risks. “Talent and skillsets in this area are one of the pillars that you have to pursue, and you have to pursue it aggressively…. The companies that can do that faster will have an advantage.”
- Geopolitics and materials: Geopolitical tensions surrounding the competition for rare earth materials, crucial for advanced chips and green energy technologies, pose a significant challenge. As Cliff mentions, “You’re competing with the same rare earth materials that are needed for green energy and like solar panels and electric motors, and there are geopolitical tensions right now which are impeding the importation of those materials at the scale.” To continue progressing at the current pace, new mining operations and chip manufacturing facilities need to be established rapidly – not a simple task by any means.
Overcoming these hurdles is essential for sustaining AI advancement in the coming decade. While navigating the complexities of geopolitics, intricate supply chains, and human adaptation, the pace of AI development may experience occasional disruptions.
Cliff envisions a quantum leap in AI…alongside some disillusionment.
During our discussion, Cliff hinted at three predictions on the future of AI, highlighting a forthcoming AI leap, empowered by Quantum computing, alongside the possibility of disillusionment driven by resource constraints and inflated expectations:
- Advancements in AI and Kurzweil’s Predictions: Cliff discussed the progress of AI and its convergence with human interactions. He aligns his prediction with Ray Kurzweil’s predictions that by the 2020s, we’ll have interesting conversations with AI, and by the late 2020s, we will form relationships with AI. “he’s dead on in terms of interesting; You can’t argue that our conversations now are not interesting.”
- Convergence of Quantum Computing and AI: Cliff anticipates Quantum’s advancements to potentially increase in the next five years or more, indicating it may become “usable, productive and economical.” It will be a “gradual…. gradual…. all of a sudden, quantum is here.” Cliff predicts that as quantum computing becomes more accessible and converges with AI, “that’s when you see the Ray Kurzweil type of AI, where it’s indistinguishable, maybe even smarter than human intelligence.”
- Disillusionment Crash in AI: Cliff acknowledges the potential for a disillusionment crash in AI due to inflated expectations and computing resource limitations. He warns, “I think there will be a disillusionment crash because, as amazing as this technology is, the expectations can always get inflated.” He emphasized the challenges related to data infrastructure and chip shortages that could impact AI development.
Cliff’s insights reveal both the promise of a quantum-boosted future and the looming disillusionment, underscoring the need for a balanced and realistic approach to AI’s transformative journey.
The Bottom-Line: As we enter the promising world of AI, it is imperative to maintain a tempered perspective to unlock the full potential of AI.
Just as Edison’s lightbulb changed the course of history, AI has the power to reshape our world. With tempered optimism acknowledging the challenges ahead, we can unlock the full potential to illuminate a brighter future.