Editor’s Note: Yes, I wrote this with Grok. But, lulz, Shumer probably used AI to write his essay, so no harm no foul. I just didn’t feel like doing all the work to give his viral essay a proper response.
Matt Shumer’s recent essay, “Something Big Is Happening,” presents a sobering assessment of current AI progress. Drawing parallels to the early stages of the COVID-19 pandemic in February 2020, Shumer argues that frontier models—such as GPT-5.3 Codex and Claude Opus 4.6—have reached a point where they autonomously handle complex, multi-hour cognitive tasks with sufficient judgment and reliability. He cites personal experience as an AI company CEO, noting that these models now perform the technical aspects of his role effectively, rendering his direct involvement unnecessary in that domain. Supported by benchmarks from METR showing task horizons roughly doubling every seven months (with potential acceleration), Shumer forecasts widespread job displacement across cognitive fields, echoing Anthropic CEO Dario Amodei’s prediction of up to 50% of entry-level white-collar positions vanishing within one to five years. He frames this as the onset of an intelligence explosion, with AI potentially surpassing most human capabilities by 2027 and posing significant societal and security risks.
While the essay’s urgency is understandable and grounded in observable advances, it assumes a trajectory of uninterrupted exponential acceleration toward artificial superintelligence (ASI). An alternative perspective warrants consideration: we may be approaching a fork in the road, where progress plateaus at the level of sophisticated AI agents rather than propelling toward ASI.
A key point is that true artificial general intelligence (AGI)—defined as human-level performance across diverse cognitive domains—would, by its nature, enable rapid self-improvement, leading almost immediately to ASI through recursive optimization. The absence of such a swift transition suggests that current systems may not yet possess the generalization required for that leap. Recent analyses highlight constraints that could enforce a plateau: diminishing returns in transformer scaling, data scarcity (as high-quality training corpora near exhaustion), escalating energy demands for data centers, and persistent issues with reliability in high-stakes applications. Reports from sources including McKinsey, Epoch AI, and independent researchers indicate that while scaling remains feasible through the end of the decade in some projections, practical barriers—such as power availability and chip manufacturing—may limit further explosive gains without fundamental architectural shifts.
In this scenario, the near-term future aligns more closely with the gradual maturation and deployment of AI agents: specialized, chained systems that automate routine and semi-complex tasks in domains like software development, legal research, financial modeling, and analysis. These agents would enhance productivity without fully supplanting human oversight, particularly in areas requiring ethical judgment, regulatory compliance, or nuanced accountability. This path resembles the internet’s trajectory: substantial hype in the late 1990s gave way to a bubble correction, followed by a slower, two-decade integration that ultimately transformed society without immediate catastrophe.
Shumer’s recommendations—daily experimentation with premium tools, financial preparation, and a shift in educational focus toward adaptability—are pragmatic and merit attention regardless of the trajectory. However, the emphasis on accelerating personal AI adoption (often via paid subscriptions) invites scrutiny when advanced capabilities remain unevenly accessible and when broader societal responses—such as policy measures for workforce transition or safety regulations—may prove equally or more essential.
The evidence does not yet conclusively favor one path over the other. Acceleration continues in targeted areas, yet signs of bottlenecks persist. Vigilance and measured adaptation remain advisable, with ongoing observation of empirical progress providing the clearest guidance.
