I started working with SEO around 2010, and one of the most powerful ideas in that world was simple: people were already looking for something, so your job was to make the right page visible when their intent appeared. A business could build useful content, improve technical quality, earn authority and gradually receive a large stream of unpaid visitors who were already interested in the problem it solved.

That model created a fairly understandable growth path for software and online businesses. Existing demand brought the first visitors, some of them became users, and those users created reviews, recommendations, links, rankings and word of mouth. Once the product had some traction, advertising, retargeting, email and audience building could accelerate a process that had already begun.

The problem today is that many new products never reach that first stage. They can be useful, operate in a category with proven demand and still struggle to get enough people through the door for the rest of the growth machinery to begin.

Software supply expanded faster than discovery

AI-assisted development has created a much larger population of builders while also allowing experienced developers to produce more. A category that once had a small number of serious products can now contain dozens or hundreds of credible alternatives, many with polished landing pages, decent onboarding, attractive screenshots and a clear explanation of what they claim to do.

The available discovery space has grown much more slowly. Google still has a small group of results that receive most of the attention. App stores have limited prominent positions. A YouTube reviewer can cover only a fraction of the available tools, and an AI assistant becomes less useful if it responds to a recommendation request with a list of fifty products.

This creates a basic imbalance. We can produce software at a rate that discovery channels cannot surface, which means a good product can remain invisible simply because too many other products are competing for the same few moments of attention.

The old growth wheel often fails before it starts

The traditional internet growth model depended on getting enough early users into the product for secondary effects to appear. Search traffic produced trials, trials produced customers, customers produced reviews and recommendations, and those outcomes improved trust for the next person. Paid advertising could then retarget people who had already shown interest or increase volume around a proposition that had begun to work.

When the first stream of people is too small, none of those later advantages develops properly. There are too few users to create meaningful reviews, too little branded search, too few conversations, too little referral activity and too little behavioral data to understand what should be improved. The product remains stuck at the beginning of the process.

This is one reason the current advice that distribution is the main defensible advantage feels both correct and incomplete. Distribution is difficult, although the problem includes discovery, trust, reputation, product understanding, recommendation quality and the sheer amount of competing software trying to enter the same channels.

Product quality and product-market fit still decide many outcomes

There are two obvious reasons many new products will fail before any marketing discussion becomes useful. Some products are unreliable, confusing or incomplete. Others solve a problem that people simply do not care enough about to change their behavior or spend money.

Those failures should remain separate from the discoverability problem. For this argument, assume the product is usable and the category already contains successful competitors, which gives us reasonable evidence that demand exists. Even under those favorable conditions, a new product can still struggle to become visible and trusted.

Poor experiences also make the next user more cautious. Someone who has tried several disappointing small apps may rely more heavily on reviews, reputation and recommendations before giving another one a serious chance. New products need social proof at exactly the stage when they have the least ability to produce it.

AI search changes what being found means

Traditional SEO focused heavily on ranking and earning the click. AI search adds another layer because the search product can read several sources, construct an answer and satisfy the user without requiring a visit to every contributing website.

A company can therefore remain visible in the information layer while receiving fewer direct visits. The important questions expand from where the page ranks to how the company is represented, whether the assistant understands what it sells, whether it is cited, whether it appears in comparisons and whether it is selected when the user asks for a recommendation.

This is a major shift for businesses that historically received a large share of their customers from unpaid search traffic. The old exchange between ranking and website visit becomes less dependable, while the new rules around AI visibility, recommendation and selection are still developing.

Social became the obvious alternative and became crowded too

As search traffic becomes less predictable, companies and builders move toward social platforms because social can create awareness before a person searches. X, LinkedIn, Instagram, TikTok, YouTube, Reddit, newsletters and communities all become part of the same broader job of helping people discover and trust a product.

The difficulty is that publishing has also become much easier. Almost every founder, developer, consultant, marketer and creator can run a channel, while AI can help produce text, images and video at a pace that would have required much more time a few years ago. Automated accounts and generated replies add even more material to feeds that were already crowded.

A small product therefore enters social media with the same problem it faces in search. It needs attention and trust from people who already follow more accounts, channels and communities than they can realistically keep up with.

Findability now spans many surfaces

I think the job that used to sit comfortably inside SEO is becoming much larger. A product needs to be findable across Google, AI assistants, social networks, YouTube, communities, marketplaces, app stores, product feeds, direct audiences and recommendation environments.

Each surface has its own rules, although they increasingly influence one another. A strong discussion in a community can affect what people search for. Reviews can influence whether an AI recommendation feels credible. Clear website content can help both a search engine and an assistant understand the product. A well-known founder can reduce the trust barrier for a new application.

The practical question therefore becomes broader than ranking. Where does the product appear, how is it described, who talks about it, which sources confirm that it works, and can an AI assistant understand enough about it to include it in a useful recommendation?

The product also needs to be selectable by machines

AI assistants introduce another audience into product discovery. A person may ask for the best tool for a particular job and allow the assistant to reduce a large market into a few options. That gives the assistant enormous influence over which products receive consideration.

A machine cannot experience every product the way a long-term user can. It often relies on documentation, website content, structured information, reviews, references and other material available around the product. When many applications have polished websites and similar claims, the assistant also faces difficulty distinguishing real quality from good packaging.

This increases the value of clear descriptions, detailed product information, credible third-party references and machine-readable pages. It also connects findability with the agent-facing web, because a product that can be understood, compared and operated by an assistant has a stronger chance of becoming useful inside that environment.

Then the product meets an already full digital life

Discovery is still only the first barrier. Even after someone notices the product, trusts it and gives it a serious trial, the product has to fit into a life already full of applications, subscriptions, emails, feeds, notifications and communities.

That is where the Replacement Economy becomes a separate discussion. People are increasingly trying to remove digital obligations at the same time that new products are asking to be installed, followed, subscribed to and remembered. A new app may therefore need to displace an existing habit or tool before it can earn repeated use.

The findability problem and the replacement problem reinforce each other. A builder first has to become one of the few products the user discovers, then one of the few they trust, and finally one of the even smaller group that earns a permanent place in daily life.

Marketing after software abundance

The phrase that marketing is the moat captures the frustration builders feel after software creation becomes easier. Creating the product can be enjoyable and fast, while distribution requires repeated testing, spending, measurement, rejection and long periods where almost nothing appears to happen.

Marketing alone still feels too small as a description of the challenge. The work includes positioning, reputation, social proof, integration into existing behavior, clear product information, AI visibility, community relationships, direct audience building and enough product quality that early users want to stay.

The builder also has to decide which channels deserve attention because trying to be everywhere creates another version of the same overload. A small company cannot dominate every search surface, social platform, community and recommendation channel at once.

The new job is findability

The SEO question I learned in 2010 was essentially: how do I make this page findable when someone searches for the problem it solves? The 2026 version is larger because discovery can happen through people, algorithms and AI assistants across many different surfaces.

A company now needs to think about whether it can be discovered, understood, trusted, compared and selected. If assistants become more capable of operating products and completing transactions, findability will extend further into whether the chosen product can actually be used from inside the assistant environment.

Software abundance makes creation easier and selection harder. The products that succeed will still need to be useful, yet usefulness only becomes relevant after someone finds the product, believes it deserves attention and gives it enough time to prove itself. That is the bottleneck many builders are now encountering before the older growth machinery ever has a chance to begin.