Something Big Is Happening —— We May Be Underestimating the Speed of AI’s LeapRecently, AI entrepreneur Matt Shumer wrote an article with a rather dramatic title:Something Big Is Happening The title looks alarmist, but after reading it, I actually feel it’s worth taking seriously.What this article is really discussing is not “whether AI will get stronger,” but a more important question:Has AI already crossed a capability dividing line? 1. From “assistant tool” to “task executor”Over the past two years, most people’s understanding of AI has stayed at the tool level:Help you write codeHelp you polish writingHelp you summarize documentsHelp you generate scriptsIt’s powerful, but it still requires frequent human involvement.Shumer’s view is: Recent changes in models have already shifted from “capability enhancement” to “a change in the form of capability.”The key differences are:Whether it can continuously execute multi-step tasksWhether it can detect its own errors and fix themWhether it can reach a goal without human interventionIf a system can complete a closed loop of task execution, then its role is no longer just a tool.2. Why he says this is a “qualitative change”The article mentions several signals:(1) Models begin to participate in model R&DOrganizations like OpenAI and Anthropic are already using models to assist with code generation, testing, and evaluation workflows.This means:AI is not just the object being trained; it is starting to participate in improving itself. This is symbolically significant in the history of technology.Tools are beginning to become part of the means of production.(2) The marginal cost of cognitive tasks is dropping rapidlyOver the past decade, automation mainly replaced:Repetitive laborAssembly-line workStandardized operationsBut now, what AI is approaching is:AnalysisReasoningWritingProgrammingDecision supportThat is, it’s touching “cognitive-layer work.”This is what’s truly unsettling about the article.3. But we need to avoid getting emotional about technologyI don’t fully agree with the narrative of “immediate, comprehensive replacement.”Historically, every technological leap has gone through three stages:Technical breakthroughApplication validationStructural restructuringRight now we are clearly in a period transitioning from the first stage to the second.The real world still has:Cost constraintsReliability issuesDifficulty of system integrationLaw and regulationInertia in corporate processesThe emergence of technical capability ≠ an immediate change in industrial structure.4. What really matters is not “replacement,” but “structural change”What I care more about is a trend:The marginal cost of cognitive work is falling. When cognitive costs fall, what happens?The upper limit of what small teams can do risesIndividual productivity is amplified dramaticallyThe barrier to starting a business dropsDecision cycles shortenThis means the competitive model will change.It may not be “you lose your job,” but rather “the organizational structure you’re in gets rewritten.”5. What this means for individuals in the real worldIf you are a knowledge worker:ProgrammerProduct managerResearcherAnalystContent creatorThe question truly worth thinking about is not:Will AI replace me? But rather:In a world where AI exists, what abilities are still scarce? This may include:JudgmentSystems thinkingThe ability to model abstractionsThe ability to integrate across domainsThe ability to break down complex problemsThe value at the execution layer may be compressed, while the value at the structural layer may rise.6. Maybe what we’re underestimating is not the risk, but the speedThe most important thing about this article is not its tone, but its judgment:Capability leaps are often not linear. When a model crosses a certain threshold, the changes it brings will suddenly become apparent, rather than showing up gradually.If this judgment holds, then the real challenge is not the technology itself, but:Whether our cognition can keep up with the technology curve. ClosingThis article is not a technical paper. It’s more like a sense of the times.Maybe the timeline will be slower than the author expects. Maybe the changes in some industries will be more moderate.But one thing is becoming increasingly clear:AI is no longer in the “experimental toy phase.”It is entering real production environments.The real danger is not that AI is too fast, but that our perception of change is too slow.
Something Big Is Happening —— We May Be Underestimating the Speed of AI’s Leap
Something Big Is Happening —— We May Be Underestimating the Speed of AI’s Leap
Recently, AI entrepreneur Matt Shumer wrote an article with a rather dramatic title:
The title looks alarmist, but after reading it, I actually feel it’s worth taking seriously.
What this article is really discussing is not “whether AI will get stronger,” but a more important question:
1. From “assistant tool” to “task executor”
Over the past two years, most people’s understanding of AI has stayed at the tool level:
It’s powerful, but it still requires frequent human involvement.
Shumer’s view is: Recent changes in models have already shifted from “capability enhancement” to “a change in the form of capability.”
The key differences are:
If a system can complete a closed loop of task execution, then its role is no longer just a tool.
2. Why he says this is a “qualitative change”
The article mentions several signals:
(1) Models begin to participate in model R&D
Organizations like OpenAI and Anthropic are already using models to assist with code generation, testing, and evaluation workflows.
This means:
This is symbolically significant in the history of technology.
Tools are beginning to become part of the means of production.
(2) The marginal cost of cognitive tasks is dropping rapidly
Over the past decade, automation mainly replaced:
But now, what AI is approaching is:
That is, it’s touching “cognitive-layer work.”
This is what’s truly unsettling about the article.
3. But we need to avoid getting emotional about technology
I don’t fully agree with the narrative of “immediate, comprehensive replacement.”
Historically, every technological leap has gone through three stages:
Right now we are clearly in a period transitioning from the first stage to the second.
The real world still has:
The emergence of technical capability ≠ an immediate change in industrial structure.
4. What really matters is not “replacement,” but “structural change”
What I care more about is a trend:
When cognitive costs fall, what happens?
This means the competitive model will change.
It may not be “you lose your job,” but rather “the organizational structure you’re in gets rewritten.”
5. What this means for individuals in the real world
If you are a knowledge worker:
The question truly worth thinking about is not:
But rather:
This may include:
The value at the execution layer may be compressed, while the value at the structural layer may rise.
6. Maybe what we’re underestimating is not the risk, but the speed
The most important thing about this article is not its tone, but its judgment:
When a model crosses a certain threshold, the changes it brings will suddenly become apparent, rather than showing up gradually.
If this judgment holds, then the real challenge is not the technology itself, but:
Closing
This article is not a technical paper. It’s more like a sense of the times.
Maybe the timeline will be slower than the author expects. Maybe the changes in some industries will be more moderate.
But one thing is becoming increasingly clear:
AI is no longer in the “experimental toy phase.”
It is entering real production environments.
The real danger is not that AI is too fast, but that our perception of change is too slow.