Recently, the discussion about AI and work has been dominated by one question: as AI models continue to advance, will white-collar jobs be largely replaced? From code generation and automated customer service to content production, AI is increasingly taking over knowledge work that used to require human input. Benchmark tests are reinforcing this anxiety, as models rapidly improve in graduate-level reasoning and senior engineer-level tasks, seemingly approaching a threshold where human work is being devoured by automation.
However, Every CEO Dan Shipper puts forward an opposite observation: the more automation there is, the more work there is for humans to do. Every is a heavy user of AI agents, having integrated tools such as Codex, Claude Code, and various agents into their internal processes. The result is not the wholesale replacement of employees; instead, the nature of work has been restructured. Engineers are now reviewing and designing systems, editors are deciding what is worth writing, and customer service representatives are maintaining systems that handle basic tickets.
What is most noteworthy is not whether AI can perform a task, but how it has redefined the role of humans. AI excels at making previously honed skills cheap, such as generating code, copy, or research reports. When these skills become universally accessible, what emerges is not high-quality output but a large quantity of “default output” that lacks judgment and context. AI commoditizes “yesterday’s human capabilities,” while what is truly scarce is the judgment required to address specific, current issues.
Automation has not eradicated experts but created more scenarios that require expert intervention. When operations can submit code using AI, engineers must determine which code is worth merging; when marketers generate thumbnails, designers must ensure they align with brand goals. AI expands the radius of production but amplifies the need for quality control, system construction, and differentiated expression.
This paradox is further explained by benchmarking. Model scores measure performance within a specific problem framework, but the framework itself is set by humans. When a model conquers a framework, humans push the problem into a more complex one. As models become stronger, they catch up to boundaries drawn by humans rather than replacing the humans who drew those boundaries. The future of knowledge work is not about humans disappearing, but shifting from executors to framework designers, system maintainers, and meaning definers.
Ultimately, after automation, the value of human work has not disappeared but has become more challenging and reliant on judgment. AI makes “knowing how to do” cheap, but makes “knowing what is worth doing, why do it, and to what extent it is done well” more scarce.
[BlockBeats]
The AI Paradox in Crypto: Why Human Oversight Will Outperform Pure Automation
The recent discussion around AI’s impact on knowledge work reveals a fascinating paradox: as AI becomes more capable, humans don’t become less busy but rather shift to higher-value roles. This dynamic has profound implications for the crypto market, where AI integration is accelerating across trading, DeFi, and blockchain infrastructure.
The Automation Paradox and Crypto’s Future
In blockchain projects, we’re witnessing the same pattern described in the article. Automated trading bots, AI-powered yield optimization, and algorithmic market-making are proliferating. However, rather than eliminating the need for human involvement, these tools are creating demand for a new class of “AI curators” – specialists who can evaluate, refine, and oversee automated systems.
Projects like Ocean Protocol and Fetch.ai are building infrastructure for AI on blockchain, but the real value may lie not in the automation itself, but in the human oversight layer. As AI becomes commoditized in crypto, the scarcity shifts to those who can apply judgment to AI-generated outputs – whether that’s evaluating a DeFi strategy, validating a smart contract audit, or curating tokenomics parameters.
Token Implications: From Execution to Governance
We’re likely to see a bifurcation in the AI-crypto token market:
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Pure Automation Tokens: Tokens that simply enable AI execution (many current AI-crypto hybrids) may face diminishing returns as their underlying capabilities become commoditized.
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Human-AI Collaboration Tokens: Tokens that facilitate human oversight and judgment in AI systems will likely appreciate. These include:
- Governance tokens for DAOs that set parameters for AI-driven protocols
- Tokens for platforms that allow human validation of AI-generated trading strategies
- Infrastructure tokens that enable “explainable AI” features for DeFi systems
The most promising projects will be those that recognize this dynamic and build systems where humans focus on the “what” and “why” while AI handles the “how” – mirroring the shift from execution to framework design described in the article.
Market Opportunities and Risks
Opportunities:
– Projects enabling human-AI collaboration in DeFi governance
– Tools for evaluating and improving AI-generated trading strategies
– Infrastructure for “explainable AI” in smart contract auditing
– Tokens that facilitate human oversight of automated market makers
Risks:
– Over-automation without proper human oversight could lead to systemic failures
– The “default output” problem could create homogenized strategies across the market
– Projects focusing solely on AI capabilities without human integration layers may struggle to maintain competitive advantages
Investment Thesis
The crypto market is moving through a similar transition to broader knowledge work: AI is making execution capabilities cheap, while increasing the value of judgment and context. The most promising investments will be in projects that recognize this shift and build infrastructure for human-guided AI systems rather than purely autonomous solutions.
As the article notes, “the value of human work has not disappeared but has become more challenging and reliant on judgment.” In crypto, this translates to projects that empower humans to make meaningful decisions about AI-driven systems – not just tokens that enable automation for its own sake.
The future belongs to crypto projects that understand this paradox and build systems where humans and AI augment each other’s strengths, rather than replacing human judgment with pure automation.