This is a market thesis, not a technical thesis.
In light of the "Pace the Frontier" movement, I think this is a particularly good time to write my opinion about the "AI bubble" and how I believe many people misunderstand it.
This past weekend, Dario Amodei (founder/CEO of Anthropic) called for regulation of AI and a more calculated/safe approach to the development of future models as they race to achieve super-intelligence, more commonly referred to as AGI (where models become smarter than people).
In the past few quarters, model development has undergone a series of breakthroughs that have caused a distinct ideological change from chasing benchmarks towards creating a super-intelligent artificial entity. Theoretically, they're building towards a model that, with enough resources (compute), would have the ability to solve humanity's most pressing issues: abundant energy, space travel, cures to complex diseases, etc.
What I find particularly fascinating about this phenomenon is how it alters the AI landscape and the (largely uninformed) public's perspective on this emerging technology.
AI Safety
I plan to write another essay just on this subject but for the sake of this read, I'll keep this somewhat brief. Without sounding too AGI-pilled, AI has the potential to become one of the most pivotal creations in human history: democratizing access to information, curing terminal diseases, and allowing for a wave of entrepreneurship because it permits a single person to accomplish what used to take hundreds of people to do. But like any great technology, used incorrectly, it can cause significant damage (no different than the internet, social media, or nuclear energy).
Do I think AI is going to kill us by 2030? No. But that said, I think it's important that we start having these conversations now and we consider what the future might look like with these advanced models. In particular, we need to ensure that this technology benefits humanity as a whole rather than just satisfying the monetary benefits of the few. Now to the fun stuff!
Is this Really a Bubble?
Many see the high valuations from the frontier labs that are creating relatively minimal economic impact (so far) and automatically assume that we're in a bubble. I once thought the same. I think it comes from the misconception that AI = Automation or AI = Magic (many people's first response to using ChatGPT). The reality is that this divergence between technology and ROI comes from a discrepancy between adoption (what people use AI for) and what the labs are building. As model capabilities improve, the vast majority of consumers continue to use these models the same way they used models from last year: advanced Google searches, brainstorming tools, cooking recipes, etc.
Therefore, we can draw many similarities between the "AI bubble" and the "Dot Com bubble." The issue isn't the underlying technology itself but a mismatch of that technology in relation to its supposed ICP (ideal customer profile).
I believe the way to view this mismatch is simply a difference in motives. Frontier labs are training with the goal of creating more intelligent models, whereas the vast majority of businesses and people aren't looking for intelligence; they are looking for effective task completion. So when they pay for increased intelligence and receive marginally beneficial results, they're disappointed and claim the technology has plateaued.
Conversely, there is a small subset of people who are receiving tremendous value from these new Fable-level frontier models, specifically experts working at the top of their fields. This is an important distinction. AI has historically been a floor raiser, allowing the average person to execute at an intermediate level in a domain they have less experience in. Experts, by that same logic, didn't benefit because AI outputs were lower quality than what an expert could produce on their own. With Fable-level models, they can operate as ceiling raisers when used by someone with strong domain expertise. This doesn't mean that everyone can now perform at this expert level (AI continues to behave as a floor raiser for the average person), but rather that experts are the primary beneficiaries of this increased intelligence.
In sum, my argument isn't that the frontier labs will crash and burn, but rather that AI models will be sorted into at least two different buckets.
- Super-intelligent models that, with brute force, can solve problems that humans can't solve alone (Frontier labs).
- Highly capable AI models that exist to automate repetitive tasks for the majority of consumers and businesses (similar to TypeSafe's philosophy).
Capability vs Intelligence
While these terms seem synonymous, they are in fact fundamentally different. Take Albert Einstein for example. He is a genius. Ask Einstein to operate an iPhone and he would be less capable than an average 12-year-old. It's not an intelligence issue; Einstein simply hasn't used an iPhone before. Now give Einstein 6 months of training and he would perform similarly to the 12-year-old. This means intelligence can translate to capability, but if you needed tech support, you still wouldn't call for Einstein. That's the problem with the commercialization of frontier models today. Most consumers and businesses don't need that much horsepower to complete daily tasks; they just need those tasks done correctly.
So Where Does This Leave Us?
Superior intelligence is something that we can strive for, but it's unlikely that these advanced models will be used to complete the majority of agentic task work. TypeSafe has the right idea, but their current model is too static. In my opinion, the future of mass-distributed AI will be self-learning and customized on a per-organization basis, specifically optimizing for task efficiency.
(Note: Companies will not be able to do this alone, just as they didn't successfully build their own SaaS).
Therefore, the commercially successful labs of the future will release base models into organizations that learn from their surroundings and interactions, form neural pathways (adjusting weights), and grow into the efficient workhorses that companies are looking for, no different from how a new employee would.
This is the model we're building at Midas.