By
Austin Coker
Reading time: 11 min
Is SaaS dying? No.
What is under real pressure is the per-seat pricing model and the demand engine most SaaS companies were built on, which assumed buyers would research in Google, land on your site, and convert through your funnel.
Both of those assumptions broke at the same time, which is why the market reaction looked more dramatic than the underlying business fundamentals. Below is what caused the “SaaSpocalypse”, what changed in how software gets bought, and what SaaS teams are doing to stay visible where those buying decisions now start.
The SaaSpocalypse refers to the early-2026 repricing of publicly traded software companies, driven by investors betting that AI will displace a meaningful share of seat-based software revenue. Bloomberg’s coverage tied the trigger to three things landing close together: a round of disappointing earnings, a visible jump in model capability, and a new add-on from Anthropic (Claude Cowork). Bloomberg described it as the biggest selloff driven by fear of AI displacement that markets have seen, with SaaS hit hardest.
The important distinction for operators is that this was a bet against a revenue model, not against software itself. Companies that sell outcomes, workflows or data still have pricing power. Companies that sell a per-user license for a tool an AI agent can partially perform are the ones investors marked down first. That same logic is now showing up in renewal conversations, which is where the market story becomes a pipeline story.
Forbes’ analysis pointed to the pressures behind the repricing: slowing revenue growth, companies cutting the number of software suppliers they use, AI tools reducing how many licenses they need, and AI coding tools letting teams do the same work in less time. Each one shows up differently in a go-to-market plan.
When AI absorbs part of a role, headcount growth slows and seat counts stop expanding with it. The revenue damage is quiet because it doesn’t show up as churn. It shows up as flat net revenue retention in accounts that used to grow every renewal.
Procurement teams are consolidating onto platforms that cover more of the workflow. Fewer vendors per account means more competitive displacement cycles and fewer greenfield deals. Your category page matters less than your comparison and alternatives coverage, because the buyer is choosing between incumbents rather than discovering a new category.
AI coding tools made “build it internally” a realistic answer for narrow, single-purpose software. This mostly threatens point solutions with shallow data moats. It rarely threatens products with compliance surface area, multi-team workflows or proprietary data.
The money hasn’t disappeared, but more of it is being redirected. Budget that used to fund incremental seats is increasingly competing with AI initiatives. For marketing leaders, this means your buyer’s internal business case now has to compete with an AI project owned by someone else in the company.
Not everyone reads the repricing as permanent. Wedbush’s Dan Ives argued software equities could climb more than 20% in 2026 as AI monetization accelerates, as Forbes noted. The practical read for a growth team is to plan as if the demand model changed permanently and let market sentiment recover on its own schedule.
We’ll run your top buying-intent prompts through ChatGPT, Claude, Perplexity, and Google AI Mode and show you who gets named, and what for.
The buying behavior shift is better documented than the market narrative. Forrester’s Buyers’ Journey Survey, 2025 found that 94% of business buyers now use AI in the buying process, and that twice as many buyers named generative AI or conversational search as a more meaningful information source than any other, ahead of vendor websites, product experts and sales. Forrester calls the result zero-click buying: a shortlist forms before the vendor ever sees a visit.
G2’s Answer Economy report, based on a March 2026 survey of 1,076 B2B software buyers and decision-makers, puts numbers on how far that has gone. About half of buyers now start their software research in AI search tools like ChatGPT, Claude and Perplexity. Seventy-one percent rely on these tools for software research, up from 60% just seven months earlier. Most significantly for competitive strategy, 69% chose a different vendor than they initially planned based on what AI search told them, and a third bought from a vendor they had never heard of before.
Read those last two figures together. The same mechanism that can remove you from a shortlist you would have won is the one that can put you on a shortlist you had no awareness to compete for. Category leaders have more to lose here than challengers do. We map the rest of the journey in how B2B SaaS buyers buy.
Click behavior has moved in the same direction. Pew Research Center’s analysis of real browsing data from 900 U.S. adults found that users clicked a traditional search result in 8% of visits where an AI summary appeared, compared with 15% of visits without one, and clicked a link inside the summary in about 1% of visits. It’s an observed association rather than proven causation, but the direction matches what most SaaS teams are seeing in their own analytics.
Gartner forecast in February 2024 that traditional search engine volume would drop 25% by 2026 as generative AI tools became substitute answer engines. Whatever the final number turns out to be, the pattern in a typical SaaS analytics account looks similar: informational traffic that rarely converted falls first, while high-intent pages hold up better, with the caveat that those pages now get summarized in the answer before the click. We break the click-loss pattern down in more detail in our zero-click search guide.
The mistake at this point is to treat this as a content quality problem. Rankings for informational queries are still there in many cases. The click is what went missing, which means the job changed from earning the visit to being the source the answer is built from.
The economics justify the shift in effort. Semrush’s study of AI search traffic found the average AI search visitor is worth 4.4 times an organic visitor based on conversion rate, because that visitor arrives already pre-qualified by the model. Fewer sessions at much higher intent is a trade most SaaS teams would take.
Generative engine optimization is the work of becoming the source those answers draw from. It overlaps with SEO, and it is not the same job. For the ChatGPT side in depth, see our B2B SaaS ChatGPT guide. Here is what the work looks like in practice.
Models need to be able to state plainly what your product is, who it serves and what it replaces. That means one unambiguous positioning page, consistent product naming across your site, and category descriptions that match the language buyers actually use rather than the internal language you invented. Vague or over-branded positioning is one of the most common reasons a product gets left out of an answer it should have been in.
Since vendor consolidation makes most deals competitive, comparison and alternatives queries are where the buying decision actually happens. Publish your own comparisons against the vendors you displace, cover the “alternatives to X” space honestly, and be specific about which buyer each option fits. Models reward pages that make a clear recommendation for a clear situation and tend to skip pages that hedge.
Answers pull heavily from sources that are not your website. Review platforms, category roundups, Reddit threads and industry publications feed the model’s view of you. If your review profiles are thin or your category never mentions you outside your own domain, no amount of on-site work fixes the citation gap.
See how we get SaaS brands cited in ChatGPT, Claude, Perplexity, and Google AI Overviews for the questions their buyers actually ask.
Proprietary data is the most durable citation asset available right now. Benchmark reports, survey data from your user base and anything with a number nobody else can publish give models a reason to cite you by name rather than paraphrase you anonymously.
Track how often your brand appears in answers for your buying-intent prompts, which competitors appear alongside you, and what the model says you are best for. That last measure is the one most teams skip and the one that explains most lost deals, because the model may be recommending you accurately for the wrong use case.
Since a third of G2’s respondents bought from a vendor they had never heard of before, awareness now has a direct effect on which brands models surface and how confidently they describe them. Paid, partnerships, podcasts and founder-led social all create the third-party mentions that feed the answer layer, which is why we don’t separate GEO from demand generation in practice.
Across the SaaS brands we run GEO and demand generation for at 95 Projects, the fastest movement has come from pairing comparison content with third-party review presence in the same category, rather than from on-site changes alone. Traditional search still matters for the queries where buyers check your claims and evaluate you directly, which is why we keep SEO running alongside it rather than trading one for the other.
If you want a quick read on where you stand, run your top ten buying-intent prompts through ChatGPT, Claude, Perplexity and Google’s AI Mode, and note who gets named and what they get named for. That takes about twenty minutes and usually tells you whether you have a visibility problem or a positioning one. Happy to take a look at yours if it’s useful.
Book a free strategy call. We’ll check how ChatGPT, Claude, Perplexity, and Google AI Mode describe your product and hand back a plan across GEO, SEO, and demand gen. No pitch deck required.
Austin Coker is the CEO and founder of 95 Projects. He has worked in SEO since 2020, when he built and sold an ecommerce brand that grew mainly through search. He founded 95 Projects in 2022 to help SaaS, ecommerce, and B2B service companies show up wherever their buyers search, from Google to ChatGPT and AI Overviews.
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Jefferies equity trader Jeffrey Favuzza coined it, according to Forbes, citing Bloomberg. The term describes the early-2026 selloff in publicly traded software, during which the sector lost more than $1 trillion in market value while the S&P 500 stayed roughly flat.
No. What is under pressure is per-seat pricing and search-dependent demand generation. Forbes tied the selloff to slowing revenue, vendor consolidation, fewer licenses and AI coding tools. Products with proprietary data, multi-team workflows or compliance requirements are better insulated than narrow point solutions.
Analysts disagree. Wedbush’s Dan Ives argued software equities could climb more than 20% in 2026 as AI monetization accelerates. The market bet heavily the other way early in the year, with hedge funds earning $24 billion shorting software, according to Forbes.
GEO is the work of getting your product accurately represented and cited in AI-generated answers, covering entity clarity, comparison content, third-party mentions and original data. It matters because Semrush found the average AI search visitor is worth 4.4 times an organic visitor by conversion rate.
Make your positioning unambiguous on your own site, publish comparison and alternatives pages that recommend clearly, build presence on the review platforms and communities these models pull from, and publish original data. G2 found 69% of buyers chose a different vendor than planned based on AI search guidance.
Yes, for high-intent queries where buyers verify claims and compare vendors directly. What changed is informational traffic. Pew found users clicked a traditional result in 8% of visits with an AI summary versus 15% without, so more of the value now sits in being the cited source.
