AI is changing the internet forever. Here’s how

There’s a simple reason Google is making sweeping changes to its iconic, decades-old search engine: users are making complicated requests. “People are asking much longer and harder questions that no longer have a clear response anywhere on the internet,” said Robby Stein, vice president of product for Google Search. Stein spoke to CNN about a new feature that lets Google generate custom visuals, interactive graphics and even mini-apps running on Google’s search page in response to queries by piecing together sources from across the web. It’s one of many updates the internet giant announced at its annual conference this week. The most valuable real estate on the internet is evolving to reflect the new ways people find information online, the latest example of how artificial intelligence is changing the internet across search, social media, online shopping and more. People are starting to use longer, more specific search terms instead of succinct generic keywords, according to Google, and are increasingly beginning their searches in apps like ChatGPT, experts say. Fake, AI-generated influencers are causing a stir on social media. And people are increasingly using AI to compare and buy products. It’s getting impossible to avoid using the internet without somehow encountering AI, despite growing anxiety about the tech and its impact on jobs, safety and the environment. “After a while, it just becomes part of the way you live,” said Joseph Turow, a University of Pennsylvania media professor who will soon be releasing a book about AI’s impact on internet advertising. ChatGPT ‘trained’ people to search differently Google says its search box is getting its biggest upgrade in 25 years. The new search field expands to fit more text and makes it easier to add other media to a search — like photos, files and Chrome browser tabs. The goal is to shrink the number of steps for a user to complete a search, according to Stein. That includes tasks like performing a search based on a photo or switching to Google’s AI Mode before asking a follow-up question. Searches that involve questions based on snapping a photo or circling something on a phone screen are growing 60%, year-over-year, he said. Searches in AI Mode, or the version of Google tailored for back-and-forth interactions, have more than doubled every quarter since they launched a year ago, and AI Mode queries are triple the length of a regular search on average. Data from SEO and marketing firm Semrush indicates some people are starting to search Google the way they type to ChatGPT. Searches containing 11 words or more increased from 3.27% to 5.37%, and conversational queries jumped from 5% to 20%, while keyword-style searches decreased. Yet the median query still contains just three words, suggesting that most people still search the old-fashioned way. Robert Langenback, president of SEO marketing agency Eight Oh Two Marketing, said he’s observed people typing in more searches that range from three to five or five to 10 words instead of two to three words. That started before ChatGPT’s arrival in late 2022, although it’s ramped up significantly since then. “(AI has) really almost trained people how to search differently,” he said. People generally use a mix of AI apps like ChatGPT and Google. More than 20% of ChatGPT referral traffic goes to Google, Semrush found after analyzing 1 billion lines of US clickstream data, or “trails” of user activity across the web. Google is typically used for direct questions or transactions, while ChatGPT is used for summarizing information, making comparisons and drafting materials, Semrush said in an email to CNN. “There’s a lot of just, ‘I’m trying to find something and help me get to it right away,’ that is the bulk of the queries that have gone into Google over time,” said Leigh McKenzie, director of organic visibility at Semrush. The rise of AI influencers AI’s reach extends far beyond search. Take Aitana Lopez’s Instagram profile. Online she looks like any other social media influencer, photos showing her posing at glitzy events, hitting the gym and sharing beauty tips to nearly 400,000 followers. But she’s not real. Lopez is one of the most prominent AI-generated characters to rise to internet stardom, along with Lil’ Miquela, Lu do Magalu and Granny Spills. Nearly 80% of marketers have increased spending on creator content that uses generative AI in the last 12 months, according to social agency Billion Dollar Boy. There are even awards celebrating the best AI-generated internet personalities. AI personalities are appealing to brands because they’re typically cheaper than high-profile human influencers and can morph to fit specific campaigns, said Turow. Tech giants want to make AI an even bigger part of social media. Meta is integrating its Muse Spark model into apps like WhatsApp, Instagram and Facebook and is testing side chats with its AI assistant in group conversations. On Tuesday, Google announced Gemini Omni, a new AI model that people can use to generate realistic avatars of themselves. The race to own online shopping Traffic to US retail sites from AI services grew 393% year-over-year in the first three months of 2026, according to Adobe, with Meta, Amazon, Google and OpenAI all introducing AI shopping tools. Google this week introduced a new “universal” shopping cart that allows users to add items from different retailers across the web. Amazon recently folded its Rufus shopping assistant into a new tool called Alexa for Shopping, which incorporates the AI helper into the online retailer’s search bar so shoppers can ask it to compare products and pricing history, among other things. But even as AI directly answers shoppers’ questions at the top of Google, Stein says there’s still a need for quality websites created and maintained by humans. Google says it still send billions of clicks to websites every day, although Pew Research data last year found that Google users are less likely to click links when viewing an AI summarized answer. Langenback says that while his clients are seeing less traffic, the traffic they are getting is leading to higher engagement — completing a purchase, booking an appointment or requesting a quote. “You just have to be ready to adapt, because (search) could look a lot different six months or a year from now,” he said. By Lisa Eadicicco

Google is making its biggest change to the search bar in years

To get ahead in the new internet age, Google wants to help you google less. The company on Tuesday revealed a flurry of AI-powered features for its search engine, AI assistant Gemini and other services. It’s part of Google’s latest effort to revamp its decades-old business model to fit the era of artificial intelligence. Among those updates is a new version of the search bar that can crawl the web on a user’s behalf and a new mode in Gemini that can work autonomously over periods of time. The changes bring Google’s search engine closer to the likes of its biggest competitors today: Anthropic and OpenAI, whose sophisticated AI models have taken over some of the duties of search tools and web browsers. Revamped search Google for years has been moving away from delivering a list of blue links in response to search queries. But the refreshed search engine, which runs on the company’s new Gemini 3.5 Flash model, represents what may be its biggest shift yet toward AI and away from traditional search. The new search field expands to accommodate longer queries that are more conversational, aligning with the way one might type or talk into Gemini or ChatGPT. Users will be able to create “agents” in Google’s search engine that can track or research topics on their own. Google says it’s useful for tasks that require tracking and monitoring announcements and listings over time, like apartment hunting or new apparel releases. One can, for example, enter a query like “Keep me updated when any of my favorite athletes announce sneaker collabs or signature drops” to prompt Google to monitor announcements from notable athletes and brands, the company cited as an example in a press release. Google will also now generate custom visuals and mini apps in response to certain requests, such as creating a fitness tracker that incorporates a person’s location, weather data and apps connected to their Google account. A new Spark Since it launched the AI-powered Gemini, Google has struggled to differentiate the assistant from its main search engine. Spark, a new mode within Gemini that can work on tasks in the background, is its latest attempt to change that. Spark will be able to work on recurring long-term tasks like monitoring credit card statements and email inboxes for important updates and creating summaries or to-do lists. It can also reference content across certain apps, like compiling notes from Google Docs, Gmail and Slides, and the company says more third-party apps will be supported in the future. The company is also adding Spark to the Gemini app on Mac computers so that it can work with local files, and users will be able to monitor what their agent from their phones through a new feature called Android Halo. The agent will stay active even when the person’s laptop is closed or their phone is locked, Google says. The focus on autonomous features seems like a direct response to OpenClaw, the buzzy AI agent that made waves in Silicon Valley earlier this year for its ability to run programs and commands without constant prompting from the user. Building AGI Google has been pursuing AI agents for years, although use cases have mostly focused on specific tasks like shopping or email management and haven’t taken off with consumers broadly. That’s largely because the technology simply hasn’t been reliable enough. “I think there’s this uncanny valley where the models aren’t yet good enough, so you can’t trust them fully, and so you aren’t really sure what you can and cannot do,” Tulsee Doshi, senior director of product management at Google DeepMind, told CNN. Google hopes the updates will bring it closer to its big-picture goal of developing artificial general intelligence: a theoretical stage of AI in which the technology becomes as intelligent as a human at broad range of subjects. OpenAI, Meta and others are racing to be the first to get there. But AI will have to get better at updating its own intelligence before AGI is possible, said Koray Kavukcuoglu, chief technology officer at Google’s DeepMind AI lab and the company’s chief AI architect. “Right now, our models (have) some sort of capability in doing that, but you can imagine that they’re a little bit static in time,” he told CNN ahead of Google’s conference to announce the updates. DeepMind DeepMind is at the center of the company’s AI strategy and has become one of its biggest assets in the AI race. It’s Google’s “secret weapon in the AI wars” according to Dave McCarthy, an analyst covering cloud and infrastructure services for market research firm The International Data Corporation. Most tech companies don’t have massive consumer reach and direct access to a research lab and cloud systems. “Google is the only company that I can think of that actually has a play in every one of those areas,” McCarthy said. Yet Anthropic and OpenAI are largely perceived as being ahead of Google in AI business products; Anthropic has been releasing new models and AI agents for coding, finance and other office work at a rapid clip this year. Anthropic accounted for 34.4% of paid AI business subscriptions in the US in April while OpenAI accounted for 32.3% and Google’s share was just 4.5%. That’s according to finance platform Ramp, which analyzed contract and transaction data with AI companies from more than 50,000 American businesses. AI is also causing concerns over the future of jobs, safety and the impact of data center construction on local communities and the environment. Half of American adults say the increased use of AI in everyday life makes them feel more concerned than excited, according to Pew Research. But Google, like many companies, is staking its future on the technology. Gemini now has more than 900 million active users, and the company expects to spend about $180 to $190 billion this year on expenses related to AI infrastructure and chips, Alphabet CEO Sundar Pichai said in a press briefing ahead of the conference. And the technology will undoubtedly continue to move quicky. Varun Mohan, a director at Google DeepMind who works on Google’s Antigravity AI coding product, said they ship a new release “close to every day” for internal developers. “We’re open to the fact that we are going to need to make changes very quickly, because otherwise we are going to have a product that is old for our users,” he said. “And we’ll be doing our users a disservice if we just hold on to our ideals of what the product is today.” By Lisa Eadicicco

AI Tools Are Rewriting Business Security, and Not in a Good Way

AI is completely rewriting the script on how founders run their businesses. As founders implement more AI tools into their workflows, they need to understand the security of their AI software supply chain. Only recently, deployment platform Vercel suffered a massive security breach as the result of an employee connecting a third-party AI tool to their corporate Google account. Revolutionizing your business operations isn’t going to do much good if sensitive data is compromised. Before you roll out AI-powered tools, you must consider how they affect the entire software supply chain. What are the risks of an under-managed AI software supply chain? Currently, enterprise enthusiasm for AI adoption seems to be outpacing companies’ ability to enact meaningful security measures. According to a report by cloud and AI security solutions provider Wiz, while 87% of security professionals are using some type of AI service, only 13% have an AI-specific posture management security strategy. Twenty percent aren’t implementing any type of AI security strategy. Another 25% admit they don’t know which AI services are currently being used in their organization. The lack of information and oversight creates major challenges for founders. Reports have found that as many as 80% of workers use unvetted and unapproved AI tools on the job. This isn’t just among lower-level employees. Senior managers and executives often have even higher rates of unapproved AI usage. The problem? Unvetted AI tools often use open-source components which can house major security flaws. The flow of information to and from micro-services, LLMs and database servers can be difficult to track, with the potential for serious connections and permissions vulnerabilities. The Vercel breach exposed a huge amount of database credentials, API keys and third-party integrations. This happened simply because an AI tool was given permission to read software environment variables. In some cases, cyberattackers, who insert false or misleading information into the training data, intentionally “poison” public machine learning models. This can make the AI malfunction in ways that trigger it to provide wrong answers, leak sensitive information or behave in a biased way, even when the model seems to be functioning normally. As agentic AI becomes more widely used, the risks grow exponentially. Agentic AI’s capabilities to carry out complex series of tasks without oversight can be a boon for time-strapped founders. It also allows AI agents to be used for increasingly sophisticated and devastating attacks if they are compromised. Minimize the risks, and maximize the results. For founders, the same risks that exist from a “standard” cyberattack also exist within the AI software supply chain, but at scale — potential regulatory, legal and financial accountability, significant downtime, and lost trust. All of those become even greater risks when founders don’t do their due diligence on their entire AI software supply chain. So how do you minimize the risks for your organization? Start by comprehensively vetting tools your organization uses. Even basic steps such as reviewing terms of use and understanding how an AI tool may use data you feed into its system can help reduce risk. For businesses operating in privacy-focused industries, tools should also meet all relevant regulations. You should also carefully vet the developers behind these AI tools. Ask yourself: Has your AI developers regularly updated AI tools, especially for security? Does the developer provide testing and validation results? Are they transparent with how your data is used or stored? What is their reputation like? Even if a given AI system is deemed safe unto itself, it’s important to map out the downstream connections it has with your apps and servers. This ensures that you are managing all relevant identities and workflows safely. The best AI supply chain security strategies take all of these components into account. Securing AI workflows and access When you integrate AI into your stack, you should also adopt many of the same security practices you use with human employees. Zero trust governance, with strict access and authentication controls, can ensure that AI tools only access the information they need to perform their critical functions. Finally, you need to establish clear AI policies and make sure your entire team follows them, on the user’s end as well as the developer’s end. The 2025 Verizon Data Breach Investigations Report found that roughly 60% of breaches had a human element, usually employee error. Ongoing training regarding safe AI use will hopefully keep your team from using unauthorized AI tools that could compromise your systems. Do you know where your AI comes from? As AI adoption accelerates, ensuring it doesn’t compromise your security becomes an increasingly high-stakes game for founders. It’s great to unlock exciting automations and boost productivity. But you need to reduce risk wherever possible. A proactive and informed approach to your AI software supply chain will help you avoid becoming another cautionary tale. EXPERT OPINION BY HEATHER WILDE RENZE @HEATHRIEL

He Spent 18 Years as a Software Engineer. AI Replaced Him in Weeks—and Exposed the Reskilling Myth

For nearly two decades, David was the guy you called when the system crashed at 3 a.m. With 18 years of experience as a senior software engineer, he had seen the industry move from physical servers to the cloud, surviving bubble bursts and economic downturns. He assumed his deep expertise was his ultimate insurance policy. Then the email arrived. It was the standard “restructuring” notice that has become the soundtrack of the modern tech sector. But this time, the chairs weren’t just being rearranged. David’s entire department was being dismantled to make room for a new team of AI specialists. David wasn’t worried at first. He had spent his career learning new languages and frameworks. He bought the books, took the online courses, and prepared to “reskill” into the AI-driven future that every LinkedIn influencer and CEO was shouting about. The reality, however, was a cold shower. After applying to over 100 jobs, the feedback was consistently the same. Hiring managers told him his skills were obsolete. The very experience he viewed as an asset was now being treated as a liability, a relic of a pre-generative era. Today, David doesn’t spend his nights debugging code. He spends them working the night shift as a waiter at McDonald’s. His story is not an outlier. It exposes the reskilling lie. The myth of the easy pivot We have been told a comforting story about the AI revolution. The narrative suggests that while some jobs will disappear, a vast ocean of new roles will open up for those willing to learn. It sounds logical in a keynote presentation, but it falls apart on the ground. Midcareer professionals are being sold a bill of goods. The idea that a 45-year-old engineer with a mortgage and family can simply take a six-week boot camp and compete with 22-year-old AI natives is a fantasy. It ignores the structural ageism and economic realities of the hiring market. Companies aren’t looking for veterans who have “reskilled.” They are looking for specialists who have lived and breathed neural networks for their entire academic lives. The “pivot” is more of a leap across a widening chasm, and for many, the landing isn’t there. Part of the problem is they haven’t yet grasped the disruption confidence cycle that takes place every time a new technology comes along and changes the game. When experience becomes a liability In the traditional business world, 18 years of experience commands a premium. It represents depth of knowledge and a history of successful projects. In the AI-first world, that same history is often viewed as “legacy baggage.” Hiring managers are increasingly biased toward candidates who don’t have “pre-AI” habits. They want people who think in prompts, not in procedural logic. This creates a trap for midcareer professionals who find themselves overqualified for entry-level AI roles but “underskilled” for senior ones. The result is a talent graveyard. Brilliant minds are being discarded not because they can’t learn, but because the corporate machine doesn’t want to pay for the transition time. It is cheaper to hire a specialist than to wait for a veteran to adapt. The leadership failure This is not just a technology problem. It is a leadership failure. Executives are prioritizing short-term AI integration over long-term talent retention. By replacing veteran staff with specialized newcomers, they are hollowing out the institutional memory of their organizations. When a senior engineer leaves, they take more than just their coding skills. They take the knowledge of why certain decisions were made five years ago. They take the understanding of the client’s deep-seated needs. AI cannot replicate that context yet. Leaders who buy into the replace-and-reskill narrative are often surprised by the problems it creates. The new AI systems might be faster, but the loss of human oversight leads to hallucinations that can cost millions. Replacing a seasoned pro with a prompt engineer is a gamble that rarely pays off the way the spreadsheets predict. Seeking alternative paths For those caught in this transition, the traditional job market can feel like a closed door. This is why many are looking toward independent tools and solo ventures to reclaim their agency. Instead of waiting for a hiring manager to validate their AI skills, they are building their own workflows. Some have turned to platforms that simplify complex industries such as travel planning, financial management, legal paperwork, real estate, health care navigation, online education, and small-business operations. It allows them to leverage their organizational skills without needing a corporate badge to prove their worth. The goal for many is no longer about finding a new desk in a glass building. It is about creating a career moat that AI cannot easily bridge. This requires a move from being a worker to being a builder, regardless of the industry. The hidden cost of displacement The human cost of this displacement is staggering. When a senior professional is forced into low-wage service work, it isn’t just a loss of income. It is a loss of identity and social utility. The psychological toll of being told you are “obsolete” after two decades of high-level performance is profound. This creates a ripple effect throughout the economy. Families lose their stability, and the community loses the tax base of high-earning professionals. The “reskilling” narrative acts as a convenient shield for companies to avoid the moral and economic responsibility of their hiring decisions. Redefining the promise If the reskilling lie is to be corrected, the conversation must change. We need to stop telling people that a pivot is easy and start talking about how to protect and adapt existing expertise. We need policies that incentivize companies to retain and transition their veteran staff. True reskilling requires time, investment, and a willingness to value experience. It isn’t a weekend workshop. It is a collaborative process between the individual and the organization. Without that partnership, the “pivot” will continue to be a cliff for many. For the Davids of the world, the McDonald’s shift isn’t a failure of effort. It is a failure of a system that promised a ladder and then pulled it away once he reached the middle. The AI era doesn’t have to be a zero-sum game between veterans and newcomers. But as long as we pretend that “learning to code” or “learning to prompt” is a magic bullet for a 45-year-old, we are setting millions up for disappointment. Leaders must be honest about the limitations of reskilling. They must acknowledge that experience still matters, even in an automated world. Until that happens, the night shift at McDonald’s will continue to be the unintended destination for some of our brightest minds. EXPERT OPINION BY JOEL COMM, AUTHOR AND SPEAKER @JOELCOMM