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Eric Eden is a B2B marketing leader specializing in AI and SaaS. He’s a CMO, advisor, and growth strategist who has built nine-figure pipelines and helped drive eight successful VC- and PE-backed exits.
AI backlash is real, from watermark concerns to data center opposition, but Eric Eden argues that B2B CMOs cannot afford to confuse speed bumps with stop signs. Capital, buyer behavior, and executive expectations are moving toward AI. The career risk is not healthy skepticism. It is refusing to adapt while the market moves on.
I recently read Drew Neisser’s newsletter about some of the potential speed bumps facing AI, including backlash around AI watermarks, data centers, and the broader public unease about where all of this is headed.
Those concerns are real. They are also not a reason for CMOs to put on the brakes.
Marketing is an investment at the end of the day, and CMOs need to follow the money and read the tea leaves. My read is simple: AI is not a fad, buyers are not going back to the old journey, and investors are not suddenly going to abandon trillions of dollars in AI-related bets so everyone can return to 2019 workflows and terrible SaaS interfaces.
Healthy skepticism is useful. Anti-AI posture is becoming a career risk.
There is a lot of anxiety around AI right now. Some of it is reasonable. Data centers use energy and water, communities are pushing back against new facilities, and people are worried about jobs, trust, creativity, and control.
That does not mean AI adoption is going to reverse. The people using ChatGPT, Gemini, Claude, Copilot, and other AI systems are not going back to doing everything manually. They are also not eager to return to clunky SaaS interfaces when they have experienced software that answers, drafts, builds, analyzes, and acts.
The scale is already enormous. Sensor Tower estimated that ChatGPT crossed 1 billion global monthly active app users in May 2026. Google announced that the Gemini app surpassed 1 billion monthly users in August 2026, after previously reporting 950 million monthly active users in its Q2 earnings remarks. Google also said AI Mode in Search had surpassed 1 billion monthly active users.
It is just not really possible to put the genie back in the bottle at this point and go back to the good old days.
At the same time, public concern is rising. Pew has reported that Americans are more concerned than excited about AI’s role in daily life. That tension matters. But CMOs should not confuse discomfort with reversal.
Buyers may be anxious about AI. They are still using it.
If you want to understand where the market is going, follow the capital. The AI investment wave is not subtle. It is wearing a reflective vest and blocking three lanes of traffic.
OECD analysis found that AI firms captured 61% of global venture capital investment in 2025, or $258.7 billion out of $427.1 billion. Carta reported that more than 60% of venture capital raised by companies on its platform in Q1 2026 went to AI companies. Other market trackers show similar concentration, especially in foundational models, infrastructure, and AI-native applications.
This does not mean every AI company will win. Many will not. Some valuations will prove absurd, some products will fail, and some agentic demos will age like warm yogurt. But the direction of capital allocation is unmistakable.
The public markets are telling a similar story. SpaceX’s June 2026 IPO was widely reported as the largest IPO in history, and investors are treating AI infrastructure as one of the defining capital themes of the decade. Anthropic’s revenue growth has also become one of the most watched stories in tech. Recent coverage reported Anthropic’s annualized revenue run rate reaching roughly $65 billion by late July 2026, with some investors expecting it could reach $100 billion by year-end.
Could some of these projections be overheated? Absolutely. But for CMOs, the practical conclusion does not require believing every moonshot forecast. The useful signal is that founders, investors, boards, and CEOs are reorganizing expectations around AI-enabled speed, productivity, and growth.
Owners and investors will not pay for 2019 work in 2026.
The biggest marketing implication is not that AI can write copy faster. The bigger implication is that buyers are changing how they discover, evaluate, and shortlist vendors.
G2’s 2026 AI Search Insight Report found that 71% of B2B software buyers rely on AI chatbots somewhere in the software research process, and 51% start their research with an AI chatbot more often than Google. That is not a tiny channel experiment. That is a buyer behavior shift.
Buyers no longer want to dig through 20 links to assemble an answer. Increasingly, they ask ChatGPT, Gemini, Claude, Perplexity, or Copilot to synthesize the answer for them. They ask for shortlists. They ask for comparisons. They ask which vendors are credible. They ask what customers complain about. They ask what category they should even be considering.
This is why YouTube, review sites, Reddit, earned media, analyst commentary, customer proof, and clear answer-ready content matter so much. AI systems need sources to synthesize. If your company is invisible, inconsistent, or poorly represented across those sources, your brand may not make the answer.
I continue to hear from sales leaders that opportunities influenced by Gemini, ChatGPT, and other AI discovery paths are climbing fast. Measurement tools are still imperfect, and I do not think platforms like Profound or Semrush yet show the full business impact clearly enough. But the buyer behavior is already moving faster than the measurement layer.
That is uncomfortable for marketers. It is also not optional.
I am seeing a dangerous pattern inside some marketing teams. People are not just skeptical of AI. They are defining themselves against it.
That may feel principled. In some companies, it may also be professionally fatal.
I recently saw an entire marketing team of about 20 people let go after the CMO set an anti-AI tone. Team members were publicly posting “I hate AI” on LinkedIn while working at a company that offered AI capabilities in its product. That is not a values statement. That is a market misread.
At another company, I was onsite with the marketing team explaining the importance of channels like YouTube for reputation in AI Overviews and Gemini. The company had not posted a video on YouTube in four months. When I asked how long it would take to create a company overview video, the team said six weeks.
The CEO was visibly frustrated. When I tried to explain that YouTube citations can be highly influential in AI-driven discovery, the Director of Demand Gen jumped up and objected that the team did not have the resources and that I was changing previously agreed priorities.
My response was, “Let’s take a break.”
But the larger point was hard to miss. Teams that respond to AI-driven buyer change by saying “we do not have resources” are missing the very point of the technology. The mandate is not to do everything the old way with fewer people. The mandate is to learn how AI can help the team do important work faster, better, and with more leverage.
Founders, investors, and CEOs are increasingly unwilling to fund marketing teams that insist on manual processes and legacy tools when credible AI-enabled alternatives exist. This is especially true in fast-moving companies where speed is part of the operating model.
A year ago, building a strong enterprise website in a few weeks using Claude, Webflow, and related tools sounded unrealistic to many teams. Today, it is happening. Tools like Lovable, Replit, and other AI development platforms have grown rapidly because they help teams create software, websites, creative assets, and agents much faster than traditional workflows allowed.
That does not mean every marketer must become an engineer. It does mean marketers need to become AI-curious, AI-literate, and AI-practical. They need to know what can now be done differently, what still requires human judgment, what needs governance, and where AI can remove tedious manual work no one wanted to do anyway.
CMOs do not need blind AI enthusiasm. They need adaptive leadership.
First, stop treating AI as a side project. If buyers are using AI to discover and evaluate vendors, then AI visibility, content credibility, and answer readiness belong inside core go-to-market strategy.
Second, audit how AI systems describe your company. Ask ChatGPT, Gemini, Claude, Perplexity, and Copilot what your company does, who it serves, who it competes with, what customers say, and when buyers should consider you. Then do the same for your competitors. The gaps will be educational, occasionally painful, and very useful.
Third, modernize the content supply chain. If your team needs six weeks to create a basic company overview video, that is not a resource problem alone. It is a workflow problem. Use AI to compress research, scripting, editing, repurposing, and distribution while keeping human judgment on message, accuracy, and quality.
Fourth, build AI fluency into the team’s operating rhythm. Train people on practical use cases, not abstract cheerleading. Create guardrails, but do not let governance become a polite word for paralysis. Reward curiosity, testing, and measurable improvements.
Finally, align AI work to business outcomes. The goal is not more AI. The goal is faster learning, better buyer visibility, stronger content, more efficient workflows, higher-quality pipeline, and a team that can adapt as the buyer journey keeps shifting.
The CMOs who win will not be the ones who ignore AI backlash. They will be the ones who understand the backlash, respect the risks, and still move decisively because the market is already moving.
No. The argument is that CMOs should distinguish between real risks and reasons for inaction. Watermark concerns, data center backlash, governance, accuracy, and trust all matter. But they should shape smarter AI adoption, not stop it altogether.
Because buyers, investors, founders, and CEOs are shifting expectations around speed, productivity, discovery, and growth. Marketers who publicly reject AI or refuse to learn AI-enabled workflows may look disconnected from the market they are supposed to understand.
Buyers increasingly use AI tools to research categories, compare vendors, summarize reviews, build shortlists, and validate claims. That means brands need to be visible and credible in the sources AI systems use to generate answers.
No. The better question is which manual, repetitive, or slow workflows can be redesigned with AI so marketers can focus more time on strategy, insight, creativity, customer understanding, and revenue impact.
Run an AI visibility audit. Ask major AI tools to describe your company, category, competitors, strengths, weaknesses, and buyer fit. Then compare those answers to your desired positioning and your competitors’ visibility.