AI Adoption is a Trust Problem

As companies scramble to become “AI-first” and satisfy demands from investors and their boards to ship products that are AI rather than solve customer problems, a rift has emerged between legacy companies and companies that will emerge with different shapes and structures than those of the last few decades. This pattern is timeless in business. Upstarts emerge, capable of taking risks, and incumbents can’t respond effectively to challengers because their pre-existing business becomes a liability in the face of the ongoing change.

Legacy orgs have been shortsighted

During ZIRP and the Uber-era of blitzscaling, cheap capital was abundant and it was an employee-friendly market. In tech, most people could get several job offers and negotiate and pick among their favorites. When ZIRP ended roughly in early 2022, the market switched from rewarding growth to rewarding profitability. Companies started looking at teams that weren’t directly contributing to the bottom line and started cutting.

In late 2022, AI mania began with the release of ChatGPT, and legacy institutions gleefully began imagining replacing their expensive knowledge workers with AI, somehow. As AI improved, leaders of legacy companies began to act on these impulses.

Tech layoffs have become so regular, they have become mundane from a “news” perspective. Of course, they’re hardly mundane to those affected but they are a reality of the current era. Midway through 2026, there have already been more than 100,000 tech job losses due to layoffs. To top it off, many of the executives conducting layoffs have attributed the most recent round to AI replacing the function of employees. There has been extensive conversation of whether this justification is valid (it’s mostly not), but it has been the marketing for the downsizing nonetheless.

Roughly 3.5 years since the beginning of AI mania, the tech job market finds itself in an interesting place. AI has turned out to be very useful for getting work done. Some people are much better at using AI than others. AI is more expensive than most companies budgeted for.

Legacy orgs have destroyed employee trust

The mistake legacy institutions have made with AI has been to believe AI is some product they could drop in to replace their people. This doesn’t appear to be true for most roles at most tech companies. However, AI does seem to be a technology that experts can learn to use and apply to automate many parts of their jobs today. If you’re an employee of a legacy institution, it has become pretty clear you cannot trust your employer very much with your livelihood. After all, 10-50% of your coworkers in the last few years have been laid off due to “AI replacing their jobs”.

Say you work at one of these legacy institutions and you figure out how to automate parts of your job. Why would you tell anyone about it? Doing so would put your role directly at risk. Better to keep it to yourself, be a high performer, and try and stand out to keep your job.

These companies clearly think AI use is important and are trying to work against this trend of hidden applications by mandating AI usage. It doesn’t matter. This approach will result in nothing but letter-of-the-law compliance. Employees make up tasks to burn tokens to show they used AI. This doesn’t bring the organization the most promising value of AI and it won’t.

In such an environment, productive uses of AI are tightly guarded within trusted subgroups within the organization while the company more broadly won’t benefit from the efficiency or learnings. People mostly learn in isolation, encounter similar challenges they then need to solve independently, or struggle with the tools but are afraid to say so out of fear of being cut for their lack of use of AI. Employees view the game as zero sum and meaningful efficiencies can’t be realized at the org level because automation is used as a pretense to cut the roles automated. Or it is perceived that that is the case - this is what matters.

Trust is required for effective AI adoption at the org level

The most useful adoption of AI requires high interpersonal trust in an organization. When you tell someone how you’ve automated parts of your job, you’re trusting they won’t use that knowledge to try and put you out of a job. You’re also trusting the organization will allow you to evolve your role rather than lay you off now that the job is “done”.

High-trust companies are seeing more human work to do than ever before as they automate everything they possibly can in the company with AI. This behavior is incentivized because when someone uses AI to automate a part of their job, there is more work for them to go do and their role evolves. Legacy orgs usually don’t have this type of role flexibility and, as I’ve already argued, have proven they think about jobs automated by AI purely as a way to reduce costs.

There are still a lot of problems with AI today, but a few things are becoming clear.

Using AI tools is a learned skill. When you start with a tool you might not be effective at using it but you can learn and get better at it.

When an organization makes a practice of using AI in public (in Slack, sharing openly), others learn by example. If you work at a legacy org, you’re incentivized to hide your true use of AI out of fear of layoffs. Your incentives push you to check the boxes and use the tools as is mandated, then separately do your job following the path of least resistance. These two uses of AI are not the same. Meta has tried to remedy this challenge by performing sweeping surveillance on its employees’ computer use. Employees don’t like it.

High-trust AI investment and adoption seems to compound an organization’s effectiveness. If adopted by individuals in isolation, the effect is the sum of the compounded individuals: everyone learns what they learn and gets better through a lens of what they already do and new things they learn, independently. If adopted by the organization broadly, where knowledge is freely shared, it’s the product: everyone learns from each other and all get better at what everyone does.

The current cycle

This pattern of org-level capability compounding is what I believe leads to the eclipsing of today’s legacy institutions by newly founded organizations. It’s also what I look for in organizations that I think show future promise.

In practice, this capability accumulation has always been a meaningful driver of successful businesses. Today, it is more achievable than ever to increase the reusability of these capabilities within a company using AI to build tools and make organizational knowledge accessible. The ingredients you need to make it work are embracing the tools and preserving trust among your employees that they will continue to have a role in the organization, even if that role evolves rapidly as technology changes what the work looks like.

Legacy orgs could benefit from this same compounding but seem more focused on trying to replace all of their employees with AI. My thesis is that many of these companies have overplayed their hands. Legacy orgs still need people to do their jobs as much as they seem to wish they didn’t. I expect this phase of anti-employee action will be a big setback for these orgs over the next 5 to 10 years. One that some will never recover from.