The author critiques current AI models like Fable and Astra, arguing that despite impressive domain-specific outputs they fundamentally fail to synthesize across varying levels of abstraction and chronology, and suggests that industry AGI narratives are likely overhyped given these persistent architectural limitations.
I haven't used Astra yet, but have been using Fable extensively for many months, focused mostly on systems biology in complex disease. While I remain impressed by the stunning output quality and insights, they only further highlight the fundamental limitations. It's moving in a direction other than AGI: to achieve exceptional detail it quickly narrows and loses any ability to conceptualize across abstract models with varying levels of fidelity and chronology, or it thinks broadly without any ability to incorporate detail, but never both at once. It becomes dimensionally reductive quickly, while elegantly speaking the language of complexity, and doesn't know when or how to mix different levels of abstraction. I'm unsure if this is because the translation layer of systems biology was never written down, the operationalization of these models requires that certain efficiencies be found, or the text-only architecture is inherently self-limiting. When I ask it to connect molecular kinetics with tissue dynamics and clinical chronology, I'm asking for something the model has never seen, but not a long reach either. This is where a generative intelligence should earn its keep. Instead, it overthinks locally and underthinks globally, rearranging training data in passably intelligent ways without a constructive synthesis. While excellent at complex but decomposable math, it can't hold and grow the context in a way that one might consider "thought." At least one of these things is true: The leaders of these companies are pushing an AGI narrative as pre-IPO hype, and they hope to scale their way out of it after an IPO (and perhaps they will, through some mix of tooling and memory, but they seem stalled on this particular limitation). The leaders of these companies are very intelligent, consider themselves a rare exception, and assume this passes as intelligence to the rest of us. The leaders of these companies are less intelligent than I think, and this looks like a superintelligence from their perspective. A more charitable option is that the benchmarks have also been decomposed into what's measurable, and the gap persists because we're not measuring it, not because it can't be closed. If they want an AGI, I hope they're placing other bets. I struggle to see how this road leads there.
The article argues that current AI systems are far from AGI, using examples like creating manga or game characters to illustrate that AI cannot yet match human-level creativity and integration, criticizing figures like Elon Musk and Sam Altman for suggesting otherwise.
The article critiques the marketing hype around AGI, contrasting grand visions of transformative AI with current applications, and mentions warnings from industry figures like Demis Hassabis.
The article criticizes current AI agents as overhyped and fundamentally limited, arguing that they are essentially LLMs with scaffolding and that their reliance on next-token prediction makes them unreliable for long-horizon planning and accountability.
An inquiry into the real-world effectiveness of AI agents, highlighting challenges in production such as inefficiency and error rates, and inviting community experiences.
The article warns against hyping GPT-6 Astra's ARC AGI-3 results, noting Nvidia's 100% achievement with AVO and OpenAI's use of a non-standard harness.