Rob Gonda is a sought-after leader in innovation, AI, Web3; serial-entrepreneur, CDO/CTO fortune exec, faculty at Singularity U, CEO at DVX.
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For decades, searching for information on the internet followed a consistent model. You typed keywords into a search box and the engine returned relevant web pages. This process of querying keywords and browsing websites is being fundamentally reshaped by the rise of generative AI.
Large language models like ChatGPT can now directly provide human-like answers and content in response to natural language queries. This AI revolution catalyzes a seismic shift from the paradigm of "searching" to simply "asking" with profound implications for search engines, websites, e-commerce and digital business models.
The Shift From Search To Ask
The first major impact of generative AI is the transition from using fragmented keywords to conversational, natural language for queries. Instead of typing "best cameras 2023," you can ask, "What are the highest-rated cameras I should consider for travel photography this year?"
AI models can understand the intent behind the full question and the context around photography and travel usage, providing a tailored, coherent response pulling in relevant information. This represents an evolution from the constrained "10 blue links" search model to an "ask experience" powered by advanced language AI.
Examples like Google’s Search Generative Experience, Microsoft’s integration of ChatGPT into Bing and the AI-powered search engine Perplexity.ai demonstrate how major search platforms are pivoting to this new paradigm. Perplexity.ai completely reimagines the search interface as an interactive conversational experience fronted by an AI assistant.
However, it’s important to understand the profound implications of this shift. While generative AI can provide fluent, contextual responses, a core challenge is determining the truth and trustworthiness of the information.
With the proliferation of content across the internet, neither humans nor current AI can definitively validate the veracity of data they encounter. As humans, we go through the labor-intensive process of vetting information, confirming sources, assessing publisher credibility and mentally scoring what we deem truthful based on accumulated evidence.
As we transition to an "ask" model powered by AI, those AI assistants will need robust capabilities to determine the trustworthiness of information sources, validate claims, verify facts and, ultimately, surface truthful answers. Simply retrieving and repackaging data is insufficient if the underlying information lacks truthfulness.
AI systems will require advanced veracity engines to thoroughly analyze where information comes from, assess the credibility of sources and determine the truth when faced with conflicting claims. Without this capability, the trend of turning everything into searchable queries could greatly worsen the spread of false information.
Maintaining rigorous standards for information integrity and empowering AI to be arbiters of truth will be critical challenges as we navigate this technological shift. Only then can the full power and potential of AI assistants be realized in this new era.
Impact On Website Traffic And Digital Publishing
One potential disruption is the possible decline in website traffic as more searches get directly answered by AI without clicking through sites. Some estimates predict that upwards of 25% of traffic could be lost, posing a potential threat to the ad revenue model of digital publishing.
To adapt, websites can explore alternatives to search traffic by focusing on channels like email, social, video platforms and owned properties. I believe establishing first-party data and diversifying into areas like e-commerce and subscriptions will be crucial.
The Continued Role Of Websites And Search Engines
Generative AI still depends significantly on retrieving information from the open web indexed by search engines. High-quality, authoritative websites are essential for providing the reliable data needed to generate accurate AI outputs.
Websites will likely optimize their content strategies to match how people ask natural language questions rather than focusing solely on keywords. Establishing topical credibility that AI can recognize will be crucial. Search engines will need to adapt their ranking systems to better serve AI’s consumption of website data.
Ultimately, a symbiotic relationship will continue between AI, which provides direct answers, and websites/search engines, which supply the underlying data. I believe the most powerful experiences will blend AI-generated content with human-authored sites.
The Rise Of AI Agents
AI is already making a massive impact within commerce, from product recommendations to smart searches and chatbots to conversational commerce. According to Bloomreach, with the boom of generative AI, the value of the e-commerce sector has grown to $5.92 trillion as retailers "rush to level up their current chatbots with new functionality." Amazon was one of the first to rush into conversational commerce, launching Rufus, an AI that understands their entire catalog. However, this, of course, means the AI will only push Amazon results, presenting a clear bias.
That’s just the tip of the iceberg. AI assistants such as Amazon Alexa, Google Assistance and Siri all have access to vast amounts of personal data, preferences and context and could streamline the way we buy. Unlike Rufus, they could likely help find products based on personal needs, analyzing options to present personalized recommendations.
I believe a major role will be handling the entire shopping journey through natural conversations, from initial product research and comparing options to making the final purchase decision and transaction. Just as e-commerce sites today invest in search engine optimization (SEO), optimizing for visibility on marketplace platforms or their app placement in app stores, I see AI optimization strategies increasing in importance. Here are a few ways I believe this could be approached.
• Provide accurate, structured product data formatted for AI ingestion.
• Align product descriptions with natural language queries.
• Enable seamless AI agent integration with platforms.
• Develop AI-friendly schemas for features, specs and compatibility.
• Signal authority signals that AI models can detect.
Rather than searching disparate sites, a successful pairing between e-commerce and an AI agent could act as the centralized interface, automating the entire process.
Conclusion
The rise of generative AI catalyzes a monumental shift in how we seek information and leverage the internet. We’re transitioning from manually searching and browsing websites to asking AI for insights, answers and task completion.
This "askification" creates opportunities but also risks for websites, businesses and the open web ecosystem. While AI can directly answer queries, it increases the importance of authoritative online sources to fuel these AI models. Because of this, an optimized content strategy for both human readers and algorithmic AI consumption will be vital—evolving format, style and structured data to align with natural language queries. Establishing topical expertise recognizable to AI will also be important.
Businesses are already investing in AI technologies, conversational interfaces and enterprise knowledge models to power customer experiences. As they continue this shift, it’s important to preserve the open web’s democratized value rather than enabling centralized control by a few AI companies. We may see decentralized AI agents emerge as knowledgeable intermediaries curating trusted web sources.
Regardless, businesses should prioritize an AI strategy to stay ahead of this tectonic "ask" shift redefining how we interact with technology and complete goals. The future is rapidly moving from searching to simply asking AI to understand our needs, a revolution shaped by actions taken today.
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