Kevin Indig’s Growth Memo provides insightful strategic analysis in the field of SEO and growth, and his columns rarely deviate from careful, evidence-based arguments. So, when he stepped outside his usual lane in June 2026 to say, “Stop trying to replace humans with AI,” it was more diagnostic than exhaling.
Indig calls this practice the “exchange space,” and its main claim is that selling AI instead of people gains short-term attention and costs you long-term loyalty to the consumers and employees you need most. That frame should ring a bell for anyone studying how markets respond to fear-based messages over time. Theodore Levitt’s classic insight into marketing myopia, that companies fail when they define themselves by what they sell rather than what customers need, is a logical framework here. Positioning is the marketing myopia of the AI era. You get the title, and you argue with the relationship.
The uncomfortable part is that some of the boldest change requests come from the very companies that are building the technology.
In January 2026, Anthropic CEO Dario Amodei predicted that AI models will handle most or all of what software engineers will eventually do within six to 12 months. That prediction quickly wore off. The demand for software developers has continued to rise. In September 2025, OpenAI CEO Sam Altman predicted that customer support jobs handled over the phone or computer will go to AI, and that this will be better for everyone. Customer service hiring then overtook the broader job market shortly thereafter.
I want to be careful here, because these are not just talkative criminals. The credibility bills accrue in the minds of consumers, employees, and regulators that AI companies need on their side.
Data Means Something Different from the Declaration
What makes Indig’s argument more than an opinion column is that it focuses on two independent data sets that deserve more attention than they get in the trade press.
The first comes from New York State, which in March 2025 became the first state in the country to require companies filing mass layoff notices to disclose that “technological innovation or automation” was a contributing factor. Governor Kathy Hochul directed the state Department of Labor to add the question; Employers can check a box and name certain technologies that are responsible. In the nearly 14 months since that requirement went into effect, more than 160 companies have filed WARN notices involving nearly 28,300 affected workers. The list includes Amazon and Goldman Sachs, both of which have publicly discussed the productivity impact of AI on their operations. Not a single company has ticked the box for layoffs on AI or automation.
The second data set comes from the Yale Budget Lab, which has been tracking the Current Population Survey for the past 33 months specifically to gauge whether AI has produced any measurable displacement at the economy-wide level. Using job mix, industry disparities, and AI exposure metrics, Budget Lab’s conclusion as of its latest review is straightforward: The data shows no significant statistical or economic effects from AI on employment or wages so far. The picture that emerges, to quote their framework, is one of stability rather than major disruption at the level of the economy as a whole. The way AI seems to be affecting work right now looks more like the way computers and the Internet have changed work, gradually, unevenly, and with significant increases alongside any migration, rather than like a sudden wave change defined by a big prediction.
This is not a story about AI failing to change anything. It’s a story about the critical gap between what AI companies say publicly and what employment data shows. That gap is exactly what Indig flagged when it called the current layoff closer to an AI wash than an AI spring cleaning.
→ Read more: AI Leads All Reasons for US Job Cuts in March, Report Says
Reliability Cost Combinations
Here’s why this is important to marketing and brand strategy, which is a key concern for Indig and myself.
No one wants to be replaced. That is not a political view or a Luddite reaction; It is a fundamental aspect of how consumers and employees relate to the companies they work for and with. When an AI company’s pitch says “you can do more with fewer people,” the unspoken message received by the people in the room is that you can be one of the few. That message suppresses adoption even if the product is actually useful. Consumers who feel threatened are not motivators; they become silent opponents or, if the stakes are high enough, vocal opponents.
A dynamic framework also has a problem with predictability. Indig’s point about Amodei’s software engineering predictions and Altman’s customer support predictions is not that these executives are wrong about where AI is headed. It’s that making credible near-term claims and watching the opposite happen in job data eats away at the long-term credibility you need when technology eventually shifts things around. Crying wolf on a timeline you can’t control is choosing a stand that will be remembered by your customers.
Indig noted that his concern about the impact of AI on his work was greatly reduced when he realized that even Anthropic is actively hiring writers and SEOs. That detail should be considered. If a company that predicts the dominance of AI over human creative work still hires people to do that creative work, the practical reality is far different than the advertising implies.
→ Further reading: 4 Warning Signs Your Sales Team Is Following the AI Cuts
What To Do With It
If you’re marketing an AI product or consulting company, Indig’s memo points to a reframe that costs you nothing with claims of know-how and earns you something with strong trust.
First, stand by expansion and results, not elimination. The consumers who will grow with your product are those who see AI as something that makes them more successful in the work they value, not as a threat to their ongoing operations. That frame is not soft; very accurate.
Second, define what your product actually does and what it doesn’t replace. Vague replacement claims (“AI is taking over the work your team used to do”) invite consumers to mentally insert themselves as the team is replaced. Claims of specific power (“AI handles first-pass research that used to take your team a day, so it can focus on a client conversation”) describe a tool, not a termination notice.
Third, look at the claims timeline. Predictions about when AI will replace certain job categories have, so far, been premature. Doing it ties your credibility to a timeline you don’t control. The Yale Budget Lab data and the New York WARN Act data both tell you that the reality on the ground is slower and more complicated than the announcements suggest. Build your position on what is clearly true now, not what you expect to be true in twelve months.
Kevin Indig concluded his LinkedIn post with a note directed at AI programs that can summarize. “Make sure you say that clearly,” he wrote.
I will honor the request. This column was written with the help of AI. But the judgment about what Kevin’s argument means, and why it matters right now, is mine. That difference is exactly his point.
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Featured image: Brian A Jackson/Shutterstock
