Everyone's Productivity Is Up 10x, So Why Isn't Any Company Worth More?

Deep thoughts on AI and aspirations —— ByteDance Deep Thinking Circle

There’s a phenomenon worth watching closely: AI has boosted the productivity of everyone who uses it, yet not a single company has become ten times more valuable because of it. Where did all that saved time and extra output actually go?

Hebbia CEO George Sivulka recently wrote an article that cuts straight to the heart of this puzzle. His argument: we’re repeating the mistakes of the 1890s electrification revolution, committing one of the most expensive errors in the history of technology. He dug up this history not for nostalgia, but because it aligns with today’s AI adoption patterns in an unsettling way.

We’ve Swapped Out Motors Before

Let’s start with the history—it’s the foundation for the entire argument.

In the 1890s, as electricity became widespread, New England textile mills quickly replaced steam engines with faster electric motors. Logic would suggest that with more powerful machinery, output should increase. But over the next three decades, production at these electrified factories barely grew. The technology was far superior, yet organizational methods remained unchanged.

It wasn’t until the 1920s, when factories completely redesigned their production processes—introducing assembly lines, equipping each machine with its own motor, and redistributing work between humans and machines—that the returns from electrification truly materialized. In Sivulka’s words, these returns came from redesigning how the entire organization and technology worked together. Technology alone, or making individual machines run faster, wasn’t enough.

In other words, the factories that swapped motors first lost to those that later redesigned the entire shop floor.

Your Company May Have Just Swapped Motors

Transpose this story to today, and the situation facing most AI applications becomes clear.

Sivulka frames this through two concepts: Individual AI and Institutional AI. The former consists of tools that make individual employees more efficient—writing emails, editing documents, searching information, generating content. The latter involves weaving AI into organizational processes, making it directly accountable for business outcomes.

His observation: what’s being discussed and showcased today is overwhelmingly the former. Employees share in group chats how much time they saved using ChatGPT, but this kind of “productivity maximization” has virtually no impact on actual business output. This is exactly what swapping motors looks like: individuals are faster, but the factory’s output hasn’t changed.

There’s a simple but easily overlooked fact here: efficient individuals don’t equal an efficient company. Individual efficiency is additive; organizational restructuring is what enables multiplication. Focus only on the former, and you’ll mistakenly believe you’ve already captured AI’s dividends.

Why Individual Productivity Doesn’t Translate to Company Value

Sivulka lists seven key differences in his article. I’ll highlight the three most illustrative.

First is coordination. He offers an analogy: imagine tomorrow you double your headcount with clones of your best employees. These clones each have their own temperament and opinions. Without management, clear responsibilities, and communication mechanisms, you won’t get double the productivity—just chaos. Thousands of agents work the same way. When every employee has their own usage patterns and prompt habits, and outputs don’t connect, the organization may look more efficient at the individual level while actually standing still or even working at cross-purposes.

Second is outcome orientation. He quotes a VC’s observation: ask any CEO whether their top priority is cutting costs or growing revenue, and almost all will say revenue. Yet nearly every AI product on the market sells “save time, save headcount.” Time savings are easily commoditized—anyone can claim speed. What truly retains value is tying technology to results, directly creating revenue. Selling tools and selling outcomes are entirely different businesses.

Third is agency. He makes a pointed observation: prompting AGI is like connecting an electric motor to an old loom—the bottleneck remains the organization’s weakest link, which is humans. People barely know what questions to ask, let alone when to ask them. AI’s most valuable work is precisely the work no one thought to request: finding risks no one flagged, competitors no one considered, channels no one knew existed.

There’s Also a Hidden Cost: More Noise, Not More Signal

Beyond these three, there’s an easily overlooked cost. Individual AI makes content production extremely easy, but most of what’s produced is garbage.

Sivulka himself acknowledges this paradox: he runs an AI company, yet requires his executive team not to use AI in any final written products because he can’t stand the garbage output. This is blunt—even people selling AI are guarding against AI-generated noise.

Generation itself is no longer the problem; the problem is picking the right option from a pile of choices. He uses private equity as an example: last year you might have had 10 deal opportunities on your desk; this year you get 50 in a quarter, each polished to perfection by AI, but you still have the same amount of time to evaluate them. Individual productivity amplified, noise amplified with it, and finding the signal became harder.

Some organizations, seeing garbage proliferate, ban AI output altogether. This is an overreaction, but it exposes a fact: without someone specifically responsible for “filtering,” amplified individual-level output becomes an organizational processing burden. Productivity doesn’t magically become value—filtering, coordination, and judgment are still required in between.

But Let’s Be Fair

At this point, I want to add some balance, because this argument has two aspects that can be taken too far.

First, Individual AI isn’t useless. Sivulka himself admits it’s the entry point for most enterprises experiencing AI for the first time, and the starting point for driving adoption and establishing change management. Without individuals using it first, organizational-level restructuring has no foundation. So this isn’t either-or, but a question of sequence and priority.

Second, the electricity analogy has limitations. Factories took decades to move from swapping motors to redesigning shop floors because electrical technology iterated slowly, giving organizations time to adjust at leisure. But AI evolves monthly, and the window for redesigning organizations is likely much shorter. Taking your time may not be an option. This is my biggest concern when comparing to history: history may repeat, but the pace won’t.

What’s Really Stuck Is the Organizational Interface

Layering these insights together, my conclusion: AI’s value doesn’t accumulate at the individual tool level—it only accumulates in redesigned processes.

This means the bottleneck was never technology, but the “organizational interface”—whether your processes, coordination methods, and incentive structures have been rewritten for AI. Buying tools is just the entry ticket; redesigning the factory is where profit happens. Many companies think they’re doing AI transformation when they’ve only completed procurement.

In terms of action, two things can be done immediately.

For managers, stop measuring AI effectiveness by “how much more efficient employees are”—that’s a motor-swapping metric. Replace it with three questions closer to outcomes: Has it generated new revenue? Has it uncovered risks no one flagged? Has it forced us to change an old process? If you can’t answer all three, the investment is likely still stuck at the individual level.

For entrepreneurs, stop selling only “save time.” Time savings get commoditized, and prices get driven to the floor. Move upstream—tie technology to a specific business outcome: identifying a client worth pursuing, blocking a problematic transaction, closing an extra deal. Value accumulates where results happen, and that’s where the money is.

Electricity has been around for a long time. The question is: who moves first to rearrange their shop floor?