The AI fluency tax

Your biggest competitor isn’t necessarily building a better product. They’re building with AI from the ground up. While you’re using ChatGPT to write emails, they’re eliminating entire job functions. The gap is widening daily, and it’s costing you more than you think.

How companies actually use AI today

I’ve been watching and learning how startups adopt AI, and there’s a clear pattern that emerges. Most fall into predictable buckets based on how deeply they’ve integrated AI into their operations. The differences between these approaches aren’t just philosophical. They translate directly into competitive advantages and incredibly efficient operational costs.

• The skeptics still think AI is overhyped and too risky to trust with real work. They’re manually analyzing everything because “AI makes mistakes” and they feel they can’t afford any “errors”.
• The dabblers use LLMs for writing and maybe some basic automation, but they haven’t fundamentally changed how they operate. They feel productive at an individual level, but the core processes of the teams and the broader organization remain untouched.
• The integrators have built AI into their workflows but still need humans to check everything and make final decisions. They’re getting real value, but and there are step change improvements in pockets of the organization.
• The rebuilders started from scratch and designed their entire operation around what AI can do today. Great operators regularly redesign their org chart, and now they’re starting that thought exercise with AI at the core. They’re not just using AI tools in pockets of the org, they’re building AI-native companies.

From the hundreds of chats I’ve had recently, it seems that most startups get comfortable at the dabbler stage and never progress (or don’t know how to). This comfort zone becomes a trap because modest improvements feel meaningful until you realize others have made exponential leaps.

The hidden costs of staying comfortable

Let me give you some real numbers that should terrify you about the true cost of manual processes. The obvious disadvantage is efficiency loss. But, it’s also about fundamental differences in business models and unit economics that compound over time.

• People costs: A leader I spoke to has multiple FTEs analyzing hours of customer calls, costing over $300K annually just to have a process in place to understand what customers are saying. Others process 100x more conversations with zero FTEs.
• Speed costs: You spend two weeks manually analyzing customer feedback to decide what to build next. AI-first competitors ship features based on patterns detected in real-time from every customer conversation. Time from customer comment to action is within minutes, not weeks.
• Quality costs: Your human analysts miss things because they’re overwhelmed by volume. They introduce cognitive bias. They can only process a fraction of your data, meaning decisions are often based on incomplete information.
• Scale costs: Every hire you make for manual work that could be automated widens their competitive moat and increases your operational burden permanently.

The math is brutal because these costs compound. While you’re hiring ops analysts to manually review rows in a spreadsheet, your AI-first competitor processes ten times more customer data with zero analysts. Their customer acquisition cost is permanently lower because they can identify and solve problems faster, retain customers better, and optimize based on comprehensive data rather than samples. The quality difference is just as damaging as the cost difference. AI catches every signal consistently, without sick days, vacation time, or bad moods affecting output.

Why comfortable kills you

The problem with the dabbler approach isn’t that it’s useless. It’s that it feels productive enough to prevent real change. You’re getting help with emails and maybe generating content faster, which creates a false sense of progress. But you’re optimizing the edges while competitors rebuild the core infrastructure of how work gets done.

I see this pattern constantly across different industries. Endless “AI experiments” that never graduate to real operational change because they don’t fundamentally challenge how the business operates. Companies run pilots, get modest improvements, and declare success while missing the bigger opportunity. Meanwhile, companies that went all-in from day one are shipping their entire business model at a speed and efficiency that traditional approaches can’t match.

The worst part is how this competitive displacement happens. It’s slow at first, then sudden and devastating. You wake up one day and realize your competitor is operating with half your headcount and twice your speed, serving customers better while spending less. By then, catching up requires rebuilding everything while they continue pulling further ahead. The window for gradual transformation closes as the gap becomes too wide to bridge incrementally.

How to actually transform

Here’s what I’ve seen work in practice, based on companies that successfully made the leap from incremental AI use to fundamental operational transformation. The key is approaching this as a rebuild rather than an optimization project.

1. Pick one expensive, manual process that currently requires multiple people doing repetitive work. Don’t try to AI-ify everything at once because that leads to half-measures and abandoned initiatives. Focus creates the forcing function needed to rebuild properly.
2. Ask the rebuild question instead of the optimization question. Rather than “How can AI help this process?” ask “If we were starting fresh today, how would we design this with AI from the beginning?” The answers are usually shocking because they reveal how much of your current process exists only because of historical limitations.
3. Measure business model changes, not just task efficiency. Track if AI changes your fundamental economics: can you avoid hiring people, serve more customers with the same team, or make decisions faster than competitors?

Real examples I’ve seen work include companies that went from quarterly research reports to daily intelligence updates by automatically analyzing every sales call. Instead of manual competitive analysis, another built a system that monitors competitors continuously, knowing about pricing changes and new features before their competitors’ own sales teams do. The pattern across successful transformations is these companies didn’t use AI to do the same work faster. They used it to do completely different work that was previously impossible at scale, eliminating entire job categories while improving output quality and speed simultaneously. In most cases the headcount is reallocated to far more strategic initiatives rather than layoffs. It can be a win-win for everyone who embraces the redefinition.

Why this matters right now

This isn’t theoretical. I’m watching AI-first companies today operate with 50-60% fewer employees than traditional startups in the same space while delivering superior results. They move from customer insight to product decision in days, not weeks, because their intelligence gathering and analysis happens automatically rather than through manual processes.

Every month you stay comfortable while competitors go all-in, the operational gap widens exponentially rather than linearly. Your fundraising gets harder because your unit economics can’t compete with companies operating more efficiently. Your hiring gets more expensive because you need more people to do the same work that others automate. Your competitive position weakens because you’re always reacting to market changes instead of acting on real-time intelligence. This is happening across every industry as AI capabilities become more accessible and competitors more sophisticated.

The bottom line

You’re already paying the AI tax through higher costs, slower decisions, and weaker competitive positioning. It shows up in your burn rate, your decision speed, and your ability to compete for customers and talent. Adding AI features to your product or marketing AI capabilities to customers is not enough. The real strategic challenge is rebuilding how your company actually works at the core operational level.

The companies that survive the next few years won’t be the ones with the best AI marketing or the coolest demos. They’ll be the ones that fundamentally changed how they operate, using AI to do work that was previously impossible or prohibitively expensive. The choice is yours, but the window for gradual transformation is closing fast.