November 12, 2024
November 12, 2024
This month, Dario Amodei, CEO of Anthropic, spelled out AI’s comprehensive future, focusing most on its rapid progression and the idea that a century's worth of advancements may be compressed into just one decade. Dario does not view AI strictly as an efficiency tool, but instead as a transformative force that could solve humanity’s most complex challenges, from climate change to economic inequality.
Research we commissioned at Mission North earlier this year actually struck a similar balance, between optimism and caution, highlighting the incredible opportunities AI presents but not shying away from its potential risks (e.g., resource constraints and social challenges). What these many conversations have revealed is that harnessing AI’s full potential truly comes down to both careful management and strategic, long-term thinking.
To discuss the wide world of AI innovation, I recently sat down with Stefan Weitz, an 18-year Microsoft veteran who built technologies used by 1 billion people and then successfully sold Radial, the second largest independent e-commerce company in North America. He’s a best-selling author on technology, a multi-patent holder, and angel investor.
Stefan’s currently the CEO and co-founder of HumanX, the world's preeminent AI gathering on organizational AI journeys. Before that, he was an Operating Partner at Sterling Partners (helping the incubator launch four companies in four years). In March, he’ll convene more than 300 speakers and thousands of leaders at HumanX, to explore the many nuances of AI’s global impact.
What follows is an edited version of our discussion:
You've been deeply involved in the AI world from various perspectives. How do you see AI balancing its transformative potential with the risks and responsibilities it brings, especially from an investment standpoint?
There’s definitely tension between these areas. [Former Google CEO] Eric Schmidt recently said we’re going to fail at achieving our climate goals, so we should focus more on building all the energy sources we can because AI – and its massive, and ironic power consumption – might be our best shot at fixing it. AI can compress a century of progress into a decade, potentially solving massive challenges like climate change. However, there are real concerns about scalability, tipping points, and recovery. That said, if you believe superintelligence is on the horizon, then even our most complex challenges could be tackled by AI.
<split-lines>"AI can compress a century of progress into a decade, potentially solving massive challenges like climate change."<split-lines>
There’s been a lot of talk about "AI washing" — companies claiming they have AI when they don’t. How do you think tech companies should approach innovation without falling into that trap?
It’s tricky because analysts are asking companies about AI on every earnings call, forcing them to respond whether they have something tangible or not. A fascinating example was Disney being threatened with a lawsuit by a shareholder for not publicly disclosing their AI strategy, causing their stock price to drop. We’ve also seen companies say they have an AI strategy, but in reality, very few know how to develop one properly. Executives often think they understand AI, but the vast majority don’t. That disconnect can lead to a lot of hype with little substance.
You’ve been working with HumanX on this upcoming AI conference. What excites you most about it?
Probably just ensuring that attendees leave with confidence in their AI journey. Conferences can often be chaotic and feel like a waste of time. We’re working hard to make sure HumanX is different, by offering real-time transcription, summaries, and analysis so attendees don’t miss anything. We’re also using AI to measure audience engagement to improve the experience in real time. Ultimately, I want people to walk away feeling like they’ve gained valuable insights that move both their business and humanity forward.
<split-lines>"HumanX is different, by offering real-time transcription, summaries, and analysis so attendees don’t miss anything. We’re also using AI to measure audience engagement to improve the experience in real time."<split-lines>
You’ve mentioned before that AI can help with economic development, but there are concerns about how developing regions can sustain AI advancements. How do you see AI playing a role in these regions?
It reminds me of mobile payments in Africa with M-Pesa. People innovated out of necessity because they didn’t have the same resources we did in the West. AI today is power-hungry and expensive, so it’s unlikely to scale in developing regions in its current form. However, I believe we’ll see innovation in those areas that leapfrog current limitations, much like mobile payments did. They’ll develop AI solutions that fit their specific constraints, which might not happen in more developed markets because the need isn’t as urgent.
What are some untapped opportunities in AI that VCs should be paying more attention to?
One area I’m excited about is how AI can revolutionize physical processes by transforming them into data that AI can manipulate. For example, in the supply chain industry, we’re seeing AI being used to track everything from sourcing raw materials to predicting sales in stores. These processes are often too complex for humans to manage because they involve so many data points, but AI can stitch it all together. This isn’t about replacing jobs — it’s about enabling humans to do what we currently can’t, due to the sheer volume and complexity of data.
<split-lines>"This isn’t about replacing jobs — it’s about enabling humans to do what we currently can’t, due to the sheer volume and complexity of data."<split-lines>
Lastly, how do you see companies preparing their workforces for AI? Not in terms of job replacement, but in leveraging AI for better outcomes.
The ‘big bang approach’ — giving everyone access to AI tools like ChatGPT — often fails. The expense goes up, and usage drops because people don’t know how to use it effectively. Successful companies are rolling out AI gradually, starting with small tiger teams that are highly engaged and testing AI tools before broader deployment. This helps avoid over-complicating workflows and ensures AI solutions are genuinely useful before scaling. The goal is to improve top-line revenue and customer service, not just deploy AI for the sake of it.
