This Week in AI: Nvidia & Google, Business Use Cases, AI Business Models, and Future-Proof One's Career
Issue #4
👋 I'm Joyce Li. Welcome back to "AI Simplified for Leaders," your weekly digest aimed at making sense of artificial intelligence for business leaders, board members, and investors.
In this edition, we begin with observations on Nvidia and other noteworthy developments in the AI sphere. We then progress to real-world AI business use cases that are being adopted across various sectors. Additionally, we'll delve into the unique challenges AI companies face when crafting their business models. It might be worth pointing out that these are not investment recommendations. Lastly, I share some strategies to AI-proof one’s career.
I hope to provide news and insights of lasting significance, and hence invite you to explore the past issues here.
Noteworthy AI News
Nvidia’s Surge
Nvidia, while not officially a monopoly, effectively commands monopolistic profits through its stronghold in the lucrative AI chip market. It recently reached $2 trillion in market cap and achieved a historic market cap surge of $277 billion in a single day.
Nvidia's dominance extends beyond AI chips; its comprehensive ecosystem includes a bespoke operating system, advanced AI platforms, software, services, and the increasingly influential circle of AI companies Nvidia invest in.
Of course, nothing lasts forever. Wall Street Journal reports that both competitors and customers are eager to create alternative sources of AI chips. In an interview by The Information, Ali Ghodsi, CEO of Databricks, predicts a ‘major drop of AI chip prices’, drawing a parallel from the temporary internet bandwidth shortage in the 2000s. It will be welcomed news for consumers of these AI chips and further accelerate AI adoption, but may also trigger a burst of some bubbles formed around the business models of GPU resellers and GPU clouds who recently raised hundreds of millions from investors, essentially arbitraging on the high prices of Nvidia GPUs.
Google's Gemini Image Creation Controversy
Google has found itself at the center of controversy with its AI tool, Gemini, which faced backlash over its handling of race-related imagery. CEO Sundar Pichai deemed the AI app's problematic responses as "completely unacceptable," highlighting the challenges of managing AI's societal implications. The controversy underscores the difficulties in balancing innovation with responsible AI development, as well as the public relations hurdles tech giants face in navigating these complex issues.
This issue extends beyond Google, prompting a need for leaders and directors to critically evaluate the limitations of generative AI technologies. It raises important questions about the application and testing scope within organizations, considering that LLMs lack awareness of individual users' cultural contexts. Although not directly applicable to this case, a related debate over establishing a "ground truth" in culturally diverse applications underscores the complexity of developing AI that is both innovative and sensitive to societal norms.
AI Business Use Cases
AI is changing the game for businesses everywhere, making processes smarter and decisions faster. Here are some real-life AI business use cases.
“A lot of our thinking has been how to apply AI in collaboration with industries, and think about it as a transformation play. ”
Hemant Taneja, Managing Director at General Catalyst, a VC firm investing in AI
Customer Service:
This seems to be low-hanging fruit for many companies to start their AI journey. I find the announcement from Klarna, a Buy-Now-Pay-Later fintech company in the e-commerce space, particularly interesting, as the company shared the quantifiable impact of its AI assistant a month after launch:
The AI assistant has had 2.3 million conversations, two-thirds of Klarna’s customer service chats
It is doing the equivalent work of 700 full-time agents
It is more accurate in errand resolution, leading to a 25% drop in repeat inquiries
Customers now resolve their errands in less than 2 mins compared to 11 mins previously
Content Creation:
Intuit uses generative AI to unify the voice and brand messaging across its various products, such as TurboTax, Credit Karma, QuickBooks, and MailChimp. By utilizing AI Knowledge Graph, Intuit is able to maintain brand consistency and compliance across its content.
Healthcare:
Independent Health, a healthcare insurer, streamlined healthcare compliance by using AI to simplify the management of complex healthcare policy documents.
A recent paper published in Nature Medicine provided evidence of adapted LLMs outperforming medical experts in clinical text summarization across multiple tasks. The researchers suggest integrating LLMs into clinical workflows could alleviate the documentation burden, allowing clinicians to focus more on patient care.
Investing:
Investment firms are leveraging AI to process data from different sources and automate mundane work previously done by analysts, to proactively and efficiently create insights to assist in the investment process. For instance, the $21bn hedge fund Balyasny has been building its own version of ChatGPT to better utilize internal and third-party datasets.
Recruiting:
AI is now used in screening, communications, and due diligence in the recruiting process. For instance, Cisco integrates AI into candidate communication in the recruiting process to improve candidate experience.
Legal:
AI is widely used in legal research, document drafting and review, end-to-end management of documents, and other legal services. Colin S. Levy, author of The Legal Tech Ecosystem, listed some prominent legal tech companies for each use case category in his LinkedIn post.
Important Considerations:
Effective AI-assisted decision-making requires businesses to focus on the critical decisions at hand, identifying the necessary data for these decisions rather than being led by the data available. It's crucial to evaluate current processes and workflows through the lens of future business objectives, asking whether they remain relevant in the evolving AI landscape. This strategic approach ensures AI integration is both purposeful and aligned with long-term goals.
AI Business Models
This week I’d like to highlight some interesting pieces on the opportunities and challenges of AI business models. Unlike the business case for the use of AI, the prospect of return on investment for AI businesses from models to applications is still under heavy debate. You can click on the article titles to read more.
Startups vs Incumbents in the AI Era
By Pete Flint, NfX
The method for navigating the AI revolution involves distinguishing between disruptive and sustaining innovations. Startups should assess whether AI in their industry serves as a disruptive force, capable of creating new opportunities, or as a sustaining one, aimed at improving existing processes.
For fields where AI acts as a disrupting technology, key strategies include identifying markets where speed and agility offer competitive advantages, delivering solutions that are significantly better than existing options, exploiting incumbents' business model conflicts, and leveraging unstructured data. For fields where AI acts as a sustaining technology, startups can find success by identifying niches that incumbents overlook or are too slow to address, essentially predicting and capitalizing on how industries will adapt to and integrate AI technologies.
Untangling the web of strategic tech investment in generative AI
By Melissa Incera, S&P Global Intelligence
The strategic investment landscape in generative AI is becoming increasingly complex, with key players like hyperscalers, software, and media providers, and generative AI companies themselves vying for a stake in this burgeoning sector.
Readers of this newsletter know I'm skeptical about the returns on investment in AI foundation models. The cycle of 'capital raise → model training → more capital raises' seems endless, especially as training costs skyrocket, limiting potential financial investors to a few sovereign wealth funds and VCs. Meanwhile, AI chips and cloud infrastructure providers, riding the AI boom, have the cash flow to keep investing as strategic investors. What implications does this intricate web of investments and dependencies have for the future of AI development?
AI-Proof One’s Career
Upon good reader responses to last week’s discussion on the critical role of leadership in overseeing AI-induced transformations in the workforce, I've gathered some advice on how to AI-proof one’s career:
Sharpen Human-Only Skills: Boost abilities like creativity and empathy that AI can't mimic.
Keep Learning: Stay on top of AI trends affecting your field.
Understand AI Basics: Know how AI tools can streamline your tasks.
Pursue AI-Related Fields: Look into roles where AI and human skills intersect.
Look Around You: Ask what's now possible and what's obsolete, then adapt by owning these changes.
Take the first step: use AI tools every day.
In a recent post, Professor Ethan Mollick shared some OpenAI custom GPTs that his Wharton students with no prior coding knowledge were able to create with only prompts:
“Dragosh Castravet created a tool that allows search funds to more easily reach out to potential partners. Shanicee McKoy prototyped a GPT that helps real estate investors analyze rent rolls and deal memorandums. Hari Joy crafted a GPT that takes potential acquirers through a due diligence process. Stephen Serrao built a GPT that helps government officials understand economic development reports. Yuval Luxenburg automated the process of creating customer journeys and finding potential pain points with products.”
I encourage you to pass this knowledge on to the less experienced in your professional circles, as your mentorship is crucial in guiding them through the evolving landscape of AI.
Reflections and Gratitude
Each issue of this newsletter is a step in my journey through the AI landscape, primarily a tool for clarifying my thoughts and enhancing my understanding. The fact that it resonates with you—leaders in finance, strategy, and board governance—surprises and encourages me.
Your shares, subscriptions, and unexpected notes of encouragement are powerful motivators. They remind me of the community we're building, eager to delve into AI's complexities and opportunities and lead in a modern world. They also remind me of the reason why I want to make my voice heard in the first place.
I'm committed to ensuring that this newsletter continues to serve the core audience, providing insights and analyses that resonate with your professional interests and challenges as a leader. The direction of the content, while guided by my personal curiosity, is greatly influenced by your feedback and the dynamic nature of our fields.
Thank you.
Joyce