For two decades the growth playbook for a young company was almost boringly consistent: write for Google, climb the rankings, and wait for the clicks to arrive. That machine is now being quietly dismantled by the chatbots and answer engines that increasingly sit between a question and its answer, and a new piece in Startup Daily argues that founders who understand the shift have far more room to move than they think.
The headline thesis is blunt. Startups will never out-muscle Google on Google’s own terms, and they should stop trying. What they can do, according to the Startup Daily, is win the much smaller and much stranger contest that is now unfolding inside tools like ChatGPT, Perplexity, Gemini and Google’s own AI Overviews. In that contest the prize is not a blue link ranked third on a crowded page. It is being the source a model reaches for when it composes its answer.
From ranking pages to being quoted
The distinction matters because the two games reward completely different behaviour. Classic search engine optimisation is a volume business built on keywords, backlinks and domain authority that compounds over years, which is precisely why incumbents with deep pockets and long histories tend to win it. The newer discipline, variously called generative engine optimisation or answer engine optimisation, is about becoming the trusted, well-structured, frequently-cited reference that a language model treats as authoritative on a narrow topic.
In practice that means a few things the Startup Daily piece puts at the centre of the strategy. Publish content that directly and clearly answers real questions rather than content stuffed with search terms. Make the underlying information machine-readable through clean structure and schema so a model can parse it without guessing. Build a consistent footprint across the wider web, because the engines assemble their sense of who is credible from mentions, reviews, directories and references scattered well beyond a company’s own site. The goal is not to trick the system but to be the obvious thing for it to cite.
There is a logic to why this favours the small. A well-funded brand can dominate a thousand keywords, but a startup that becomes the single clearest authority on one specific, well-defined problem can find itself quoted by name in an AI answer that a potential customer never even realised was generated by a model. When the chatbot does the choosing, breadth matters less and precision matters more, and precision is something a focused young company can actually afford.
The counter-argument
It would be easy to read all this as a clean escape hatch from Google’s gravity, and that is where a healthy dose of scepticism belongs. The uncomfortable reality is that the biggest answer engine in the Australian market is still Google, and its AI Overviews are stitched directly into the same results page that has always been its stronghold. Winning AI search, in other words, does not actually mean leaving Google behind. It often means competing inside a Google product that increasingly answers the question itself and sends fewer people onward to anyone’s website at all.
That raises a hard commercial question that the optimistic framing tends to skate over. If a model answers a user completely and the user never clicks, being cited may win attention without winning the traffic, the sign-up or the sale that a startup actually needs to survive. Measuring success becomes genuinely difficult when the win is a sentence inside someone else’s chatbot rather than a visit you can see in your analytics. Several of these engines also disclose their sources inconsistently, which makes it hard for a founder to even know whether the strategy is working, let alone to prove the return on the effort.
There is also a moving-target problem. The engines change their behaviour constantly, the citation patterns of one quarter can evaporate in the next, and anyone building a business on being favoured by a system they do not control is exposed to the same platform risk that has burned a generation of companies dependent on social media reach. Betting the growth plan on the current quirks of a model is not obviously safer than betting it on Google’s algorithm was.
What it means for Australia
For Australian startups the argument lands at a sensitive moment. Local founders operate in a small domestic market with modest marketing budgets, and the traditional cost of acquiring customers through paid search has climbed to the point where many simply cannot compete with global players for the same keywords. A discipline that rewards clarity, authority and a tight focus rather than raw spend is, at least in theory, the kind of levelling that an under-capitalised ecosystem should welcome.
The behaviour of Australian buyers is shifting in the same direction. A growing share of consumers and business purchasers now open an AI assistant before they open a search tab, and that is true for the business software, professional services and niche products where many local startups actually compete. If the person deciding which vendor to shortlist is asking a chatbot, then the company the chatbot names has already won the first round before any website is visited. This is the same terrain FluentSea has covered in its reporting on how Australian brands are chasing visibility inside AI Overviews and ChatGPT search, and the stakes for smaller players are if anything higher.
Regulation and trust add a distinctly Australian wrinkle. Local businesses are navigating privacy reform, consumer law and a national conversation about how much to rely on offshore AI platforms, and a growth strategy that depends entirely on foreign-owned engines will inevitably draw questions about sovereignty and data that a founder cannot wave away. The practical answer for most will be a hedge: treat AI search visibility as one channel among several rather than a silver bullet, and keep owning the direct relationship with the customer wherever possible.
What’s next
The near-term reality is that founders will have to run both playbooks at once, because traditional search has not disappeared and the AI layer is not yet mature enough to trust on its own. Expect a scramble for better measurement, as marketing teams try to work out how to track citations and referrals from engines that were not built to report them, and expect a wave of local agencies and tools repositioning themselves around the new discipline. The companies that come out ahead will be the ones that treat being quoted by a machine not as a trick to be gamed but as a reputation to be earned, which, stripped of the jargon, is a fairly old idea wearing new clothes.
Sources: Startup Daily.
