Many people are starting to realize that more than $300 billion search market, led in the west by Google, is being completely upended by new large language models and generative AI. My good friend and colleague Pete Blackshaw, founder of www.brandrank.ai, calls it “The Answer Economy” — and I think he’s right.
Below is a table I put together to compare and contrast traditional Google to the new LLM answer engines.
Explanation of the Five Factors
- Interaction Model Google requires users to frame specific search terms and refine their queries repeatedly. In contrast, LLMs offer a conversational experience, allowing users to express their needs naturally, while the model refines and adapts its responses over time.
- Result Format Google’s search results are lists of links that users must evaluate individually. LLMs, however, synthesize information from multiple sources into coherent and relevant answers, reducing the need for further interpretation.
- Efficiency With Google, users often sift through several pages of search results to find actionable information. LLMs eliminate this step, delivering concise, relevant information instantly, thus enhancing productivity.
- Depth of Insight Google Search primarily aggregates and displays existing content, offering little beyond what is readily available online. LLMs analyze data, infer meaning, and explain concepts in-depth, offering superior insights for complex queries.
- Personalization Google’s results are often standardized unless influenced by user history or location. LLMs, however, adapt dynamically to the user’s tone, intent, and knowledge level, creating a personalized experience that aligns with individual needs.
This shift from question-based searches to answer-driven interactions reflects a paradigm change, enabling faster, more effective decision-making in personal and professional contexts. The “Answer Economy” positions LLMs as indispensable tools for knowledge workers, executives, and everyday users alike. So let me review three critical questions
Action 1: See how your product or service shows up in the popular models.
Pete’s research has shown that consumers are already using LLMs in their product purchase journey. For electronics, 60% of customers consult these models. An easy way to find out is to go to www.chathub.gg, a meta search engine that allows you, if you subscribe for $19/month, you can query six LLMs simultaneously. I put the following prompt in www.chathub.gg:
You are an expert in baby products and I’d like to know what is the best, eco-friendly diaper? Choose the top three, then give me your top choice and defend your answer.
Then I added, Which one is most cost effective?
The table below shows how this dialog not only put forth a consideration set, but allowed me to reorder it with just one more query.
Everyone’s product or service will be found and rated by the new answer engines. Start reviewing where you need to look now and begin implementing methods to improve your standing.
Action 2: Map key trends for your customer.
I asked all six engines what the key issues are for the AI leader in 2025, and then put them all into another LLM to create a list of the top seven, which is not bad at all
- AI Governance & Ethical Alignment Rationale: Tightening global regulations like the EU AI Act and increasing public scrutiny demand robust ethical frameworks and compliance measures.
- Computational Resource Competition Rationale: Growing “compute arms race” driven by massive resource requirements of advanced AI models, coupled with semiconductor shortages and rising cloud costs.
- Talent Acquisition & Retention Rationale: Severe shortage of AI specialists projected through 2028, with fierce competition driving unsustainable compensation packages, especially for senior roles.
- Data Privacy & Security Rationale: Exponential growth in data processing creates heightened privacy and security risks, requiring robust protection measures amid increasing cyber threats.
- AI Explainability & Transparency Rationale: Only 22% of organizations report high confidence in AI transparency, creating critical challenges for high-stakes applications in healthcare and finance.
- ROI & Value Demonstration Rationale: Organizations struggle to demonstrate sustained value from AI investments, requiring clearer governance and measurement frameworks.
- Bias & Fairness in AI Systems Rationale: Documented biases in facial recognition, hiring, and healthcare applications highlight urgent need for fairness in AI development and deployment.
This new point of view on what is important to your customer is an ongoing dialog that all customer facing leaders should avail themselves of. They are incredibly easy to use and can dialog with you as to why they think these are the critical trends. You can ask it to argue with itself — e.g. give me the top reasons these are the wrong trends, etc. When trying to anticipate customer needs and desires the models are great conversation partners.
Action 3: Imagine You Are Looking For The Best Place To Work
I asked the six answer engines the following:
Which is the best of the big 4 to work for as a 25 year old with an MBA and accounting degree. Choose one and defend your choice.
When I asked chathub.gg all six models said Deloitte, and it bothered me a bit. I loved my 8 years at PwC and even though I have no ongoing financial relationship with the firm, I’m certainly going to tell my friends there that they need to look into how to improve their standing in the answer engines. Every leader has to ask: Where does our firm rank?
In short, we are moving from the question economy to the answer economy. Every business should look today at how their product or service ranks, what the models think are the key customer trends and where do they sit in the talent market? These are just the beginning of the implications of having answer engines everywhere.