3 actions to get you ready for the response economy

Many people are beginning to realize that the more than $300 billion search market, led in the west by Google, is being completely upended by the new models of big languages ​​and generative AI. My good friend and colleague Pete Blackshaw, founder of www.brandrank.ai, calls it the “Response Economy” – and I think he’s right.

Below is a table I put together to compare and contrast traditional Google with the new LLM answer engines.

Explaining the Five Factors

1. The interaction model Google requires users to enter specific search terms and refine their queries repeatedly. In contrast, LLMs provide a conversational experience, allowing users to express their needs naturally while the model processes and adapts its responses over time.
2. Result format Google search results are lists of links that users must evaluate individually. However, LLMs synthesize information from multiple sources into coherent and relevant answers, reducing the need for further interpretation.
3. efficacy With Google, users often sift through several pages of search results to find actionable information. LLMs eliminate this step, providing concise and relevant information immediately, thereby increasing productivity.
4. Depth of Insight Google Search primarily aggregates and displays existing content, offering little beyond what is readily available on the web. LLMs analyze data, derive meaning, and explain concepts in depth, providing superior insights into complex questions.
5. Personalization Google results are often standardized if they are not influenced by the user’s history or location. However, LLMs dynamically adapt to the user’s tone, purpose, and knowledge level, creating a personalized experience that matches individual needs.

This shift from question-based searches to response-driven interactions reflects a paradigm shift, enabling faster and 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 consider three critical questions

Action 1: See how your product or service appears in popular patterns.

Pete’s research has shown that consumers are already using LLM 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 lets you, if you subscribe for $19/month, search six LLMs simultaneously. I place the following request on www.chathub.gg:

You are an expert in baby products and I would like to know what is the best and eco-friendly diaper? Choose the first three, give me your top choice, and defend your answer.

Then I added, Which is more cost effective?

The table below shows how this dialogue not only lays out a set of considerations, but allowed me to reorder it with just one more question.

Everyone’s product or service will be found and rated by the new response engines. Start reviewing where you need to look now and start implementing methods to improve your condition.

Action 2: Map out key trends for your customer.

I asked all six engines what the top issues are for the AI ​​leader in 2025, and then put them all in another LLM to come up with a list of the top seven, which isn’t bad at all

1. UA Governance Rationale and Ethical Alignment: Tightening global regulations such as the EU AI Act and increased public scrutiny require strong ethical frameworks and compliance measures.
2. Reasoning for the competition of computing resources: The rise of the “computing arms race” driven by the massive resource demands of advanced AI models, coupled with semiconductor shortages and rising cloud costs.
3. Rationale for talent acquisition and retention: A major shortage of AI specialists is predicted by 2028, with fierce competition leading to unsustainable compensation packages, especially for senior roles.
4. Data privacy and security rationale: The exponential growth in data processing creates increased privacy and security risks, requiring strong protection measures amid growing cyber threats.
5. Reasoning AI explainability and transparency: Only 22% of organizations report high confidence in AI transparency, creating critical challenges for high-stakes applications in healthcare and finance.
6. ROI and value demonstration rationale: Organizations struggle to demonstrate sustainable value from AI investments, seeking clearer governance and measurement frameworks.
7. Bias and Fairness in the Rationality of AI Systems: Documented biases in facial recognition, employment, and healthcare applications highlight the urgent need for fairness in AI development and deployment.

This fresh perspective on what’s important to your customer is an ongoing dialogue that all customer-facing leaders should benefit from. They are incredibly easy to use and can talk you through why they think these are critical trends. You can ask them to debate with themselves – eg. give me the main reasons that these are the wrong trends, etc. When trying to anticipate customer needs and wants, models are great conversational partners.

Action 3: Imagine you are looking for the best place to work

I asked the six answer engines as follows:

Which is the best of the big 4 to work as a 25 year old with an MBA and accounting degree. Choose one and protect your choice.

When I asked chathub.gg, all six models said Deloitte, and that bothered me a bit. I enjoyed my 8 years at PwC and although I have no ongoing financial relationship with the firm, I will certainly tell my friends there that they should look at how to improve their position in the answer engines. Every leader must ask: Where does our firm rank?

In short, we are moving from a question economy to an answer economy. Every business needs to look today at how their product or service is ranking, what do the models think are the key customer trends and where do they stand in the talent market? These are just the beginning of the implications of having answer engines everywhere.