Every productivity tool on the market promises speed. Faster writing. Faster research. Faster decision-making. The assumption is that speed is what’s holding you back – that if you could just produce output faster, you’d produce more value.
That assumption is wrong. Once automation makes speed cheap and common, speed stops setting anyone apart. Depth may be one of the hardest things to automate.
Speed as Commodity
Generative AI can produce a decent first draft of almost anything in seconds – a report, an email, marketing copy, a research summary. It’s already faster than any human at this. And it’ll only get faster.
Once everyone has access to that speed, speed stops being an advantage. The baseline rises. Everyone will be fast. The person who stands out will be the one who produces something that isn’t just fast, but good – where “good” means original, deeply reasoned, and built on a level of understanding the model doesn’t have.
This is basic economics: when a skill becomes available to everyone, its market value drops toward zero. Speed of output is already headed that way. What holds its value is whatever an automated tool can’t do – and that’s exactly what requires depth [1].
What Depth Looks Like
Depth is the ability to hold a complex chain of reasoning for 45 minutes without needing outside input. It’s the ability to look at a problem from multiple angles, pull together conflicting information, and reach a judgment that accounts for nuance.
In our view, this isn’t a skill AI is likely to replicate anytime soon. AI can produce reasoning that sounds plausible, but it may lack lived experience, hard-won domain knowledge about tradeoffs, and the ability to weigh competing values the way human depth does. Autor’s 2015 review found that tasks requiring flexibility, judgment, and common sense had resisted automation up to that point [2], although it predates today’s AI models.
Consider a hypothetical: a well-researched AI analysis of a business problem next to the analysis of a partner who’s spent 20 years in that industry. The AI’s analysis might be thorough, well-organized, and full of relevant data. The partner’s analysis might be shorter, less polished, and more nuanced – because it draws on experience that training data may not capture. The partner may know which data points actually matter, which risks are real and which are only theoretical, and which stakeholders will push back and why. That’s what we mean by depth.
Depth is rare because it’s hard to develop and easy to avoid. The knowledge-work environment actively discourages it – favoring fast responses, constant availability, and quick turnaround over sustained thinking. The person who builds depth despite that environment is building an asset that only becomes more valuable as everything else gets automated.
The Counterfeit of Depth
As real depth becomes more valuable, shallow output will increasingly try to imitate it. In our view, AI-generated content can often pass for deep analysis on a casual read. What gives it away isn’t grammar or structure. It’s the absence of any genuine discussion of tradeoffs, a lack of specific real-world knowledge, and a failure to admit what isn’t known.
The danger isn’t that you’ll be fooled by bad analysis. It’s that you won’t be able to tell the difference, because your own depth has weakened. Someone who’s actually done the work of thinking deeply – who’s held complex reasoning chains, made hard tradeoff calls, and pulled together conflicting information – can often spot shallow reasoning quickly. It tends to feel thin. It lacks the texture of someone genuinely wrestling with a hard problem. Someone who’s outsourced depth for years may lose some of that ability to sense it. Shallow output may start to feel good enough to them, because they’ve forgotten what real depth feels like.
The person who can tell real depth apart from a convincing imitation has an advantage that keeps growing. They can evaluate AI output critically, taking what’s useful and dropping what’s superficial. They can spot the gaps in an analysis and fill them with their own expertise. The person who can’t tell the difference will keep leaning more and more on output that sounds good but is thin underneath.
The Practical Counterargument
Is depth always the right move? No. There are situations where speed genuinely matters – crisis response, time-sensitive decisions, high-volume production work. The case for depth isn’t that every single task needs it. It’s that if you never train yourself to go deep, you lose the ability to do it when it actually matters. Someone who can go deep on demand but chooses to stay shallow when that’s appropriate has real options. Someone who can only go shallow has no choice at all. Training depth isn’t about rejecting speed. It’s about keeping that option open.
The Trainable Skill
Depth is a skill you can train. It takes deliberate practice at uninterrupted reasoning – not flashes of insight, not creativity, but the plain, unglamorous skill of holding a thought for longer than feels comfortable.
The premium now isn’t in generating an answer. Answers are cheap. The premium is in holding onto the question – staying with a problem long enough to actually understand it, instead of jumping to the first solution that seems plausible.
As a suggestion, with no established dose or timeline: spend 30 minutes a day on a single problem, with no interruptions, no looking up answers, no asking AI. Just hold the problem. Turn it over. Look at it from different angles. Resist the urge to wrap it up quickly. The discomfort of not knowing, held over time, may be what trains depth.
That skill can be trained. It’s rare. And in an automated world, it’s the one advantage that keeps compounding.
References
↑︎ Newport C. Deep Work. Grand Central Publishing; 2016
↑︎ Autor DH. "Why Are There Still So Many Jobs? The History and Future of Workplace Automation." Journal of Economic Perspectives. 2015;29(3):3-30. DOI: https://doi.org/10.1257/jep.29.3.3
Kurt Greiner
Kurt is a digital strategist and IT professional blending emerging technology with practical application to help businesses and individuals streamline their digital presence. His current work focuses on the intersection of intentional living and technological resilience, exploring how individuals can leverage modern tools to navigate the second half of life with purpose.
Leave a Reply