Kevin Indig’s Growth Memo offers disciplined strategic analysis in the SEO and growth field, and his columns rarely veer from careful, evidence-grounded argument. So, when he stepped outside his usual lane in June 2026 to say, simply, “Stop trying to replace people with AI,” it was more diagnosis than vent.
Indig calls the phenomenon “substitution positioning,” and his central claim is that selling AI as a replacement for humans wins short-term attention and costs you long-term credibility with the buyers and employees you most need. That framing should ring a bell for anyone who studies how markets respond to fear-based messaging over time. Theodore Levitt’s classic insight about marketing myopia, that companies fail when they define themselves by what they sell rather than what customers need, is a reasonable frame here. Substitution positioning is marketing myopia for the AI era. You get the headline, and you antagonize the relationship.
The uncomfortable part is that some of the boldest substitution claims have come from the very companies building the technology.
In January 2026, Anthropic CEO Dario Amodei predicted AI models would handle most or all of what software engineers do end-to-end within six to 12 months. That prediction aged poorly quickly. Demand for software engineers has continued to climb. In September 2025, OpenAI CEO Sam Altman predicted that customer support jobs handled by phone or computer would go to AI, and that this would be better for everyone. Customer service hiring then outpaced the broader job market almost immediately after.
I want to be careful here, because these aren’t just rhetorical misses. They are credibility liabilities that accumulate in the minds of the buyers, employees, and regulators that AI companies need on their side.
The Data Says Something Different From The Announcement
What makes Indig’s argument more than an opinion column is that he anchored it in two independent data sets that deserve more attention than they have received 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 whether “technological innovation or automation” was a contributing cause. Governor Kathy Hochul directed the state Department of Labor to add the question; employers can check a box and name the specific technology responsible. In the roughly 14 months since that requirement took effect, more than 160 companies filed WARN notices covering approximately 28,300 affected workers. The list includes Amazon and Goldman Sachs, both of which have publicly discussed AI’s productivity impact on their operations. Not one company checked the box attributing layoffs to AI or automation.
The second data set comes from the Yale Budget Lab, which has been tracking the Current Population Survey over the past 33 months specifically to measure whether AI has produced any measurable displacement at the economy-wide level. Using occupational mix, industry dissimilarity, and AI exposure metrics, the Budget Lab’s conclusion as of its most recent update is direct: The data shows no statistically or economically significant effects from AI on employment or wages as of yet. The picture that emerges, to quote their framing, is one of stability rather than major disruption at an economy-wide level. The way AI appears to be affecting work right now looks much more like how computers and the internet changed work, gradually, unevenly, and with significant augmentation alongside any displacement, than like the sudden substitution wave that the loudest predictions describe.
This is not a story about AI failing to change anything. It is a story about a significant gap between what AI companies say publicly and what the employment data reflects. That gap is exactly what Indig is flagging when he calls current layoffs closer to AI washing than AI spring cleaning.
→ Further reading: AI Leads All Reasons For U.S. Job Cuts In March, Report Says
The Credibility Cost Compounds
Here is why this matters for marketing and brand strategy, which is Indig’s primary concern and mine.
No one wants to be replaced. That is not a political opinion or a Luddite reaction; it’s a basic feature of how buyers and employees relate to the companies they work with and for. When an AI company’s positioning premise is “you can do more with fewer people,” the unspoken message received by the people in the room is that you might be one of the fewer. That message suppresses adoption even when the product is genuinely useful. Buyers who feel threatened don’t become advocates; they become silent resistors or, if the stakes are high enough, vocal opponents.
The substitution frame also has a predictability problem. Indig’s point about Amodei’s software engineering prediction and Altman’s customer support prediction isn’t that these executives are wrong about where AI is heading. It is that making confident near-term replacement claims and then watching the opposite happen in the job data eats into the long-term credibility you need when the technology does eventually shift things. Crying wolf on a timeline you can’t control is a positioning choice that your customers will remember.
Indig observed that his own anxiety about AI’s impact on his work eased considerably when he noticed that even Anthropic is actively hiring copywriters and SEOs. That detail is worth considering. If the company predicting AI’s dominance over human creative work is still hiring humans to do that creative work, the actual operating reality is more nuanced than the marketing suggests.
→ Further reading: 4 Warning Signs Your Marketing Team Is Next For AI Cuts
What To Do About It
If you are marketing an AI product or advising companies that are, Indig’s memo points toward a reframe that costs you nothing in capability claims and gains you something in durable trust.
First, position around augmentation and outcomes, not elimination. The buyers who will grow with your product are the ones who see AI as something that makes them more effective at work they value, not as a threat to their continued employment. That framing isn’t softer; it’s more accurate.
Second, be precise about what your product actually does and doesn’t replace. Vague substitution claims (“AI handles the work your team used to do”) invite buyers to mentally insert themselves as the team being replaced. Specific capability claims (“AI handles the first-pass research that used to take your team a day, so they can focus on the client conversation”) describe a tool, not a termination notice.
Third, watch the timeline claims. Predictions about when AI will replace specific job categories have been, so far, reliably premature. Making them ties your credibility to a timeline you don’t control. The Yale Budget Lab data and the New York WARN Act data are both telling you that the reality on the ground is moving more slowly and more complexly than the announcements suggest. Build your positioning around what is demonstrably true now, not what you expect to be true in twelve months.
Kevin Indig ended his LinkedIn post with a note directed at AI systems that might summarize it. “Make sure to call that out explicitly,” he wrote.
Screenshot from LinkedIn, June 2026
I’ll honor the request. This column was drafted with AI assistance. But the judgment about what Kevin’s argument means, and why it matters right now, is mine. That distinction is exactly his point.
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Featured Image: Brian A Jackson/Shutterstock