Google just redesigned the search box for the first time in 25 years — here’s why it matters more than you think.
For a quarter century, the Google search box has been one of the most recognizable interfaces in computing: a thin white rectangle, a blinking cursor, a few typed words, and a list of blue links. On Tuesday, Google will formally retire that paradigm.
At its annual I/O developer conference, Google announced a sweeping redesign of the search box itself — the literal text field where billions of queries begin every day — transforming it from a simple keyword input into a dynamic, AI-driven conversation starter that can accept text, images, PDFs, videos, and even open Chrome tabs as inputs. The company is also merging its AI Overviews and AI Mode features into a single, seamless search flow, eliminating the friction that previously forced users to choose between a traditional results page and an AI-forward experience.
Liz Reid, Google's vice president and head of Search, called it "the biggest upgrade to our iconic search box since its debut over 25 years ago" during a press briefing on Monday.
The announcement arrived alongside a blizzard of other news — new Gemini models, a personal AI agent called Spark, an intelligent shopping cart, a reimagined developer platform — but the search box redesign may prove to be the most consequential. It is the clearest signal yet that Google views the future of its flagship product not as a place where users type fragmented keywords, but as an interface where they hold open-ended, multimodal conversations with an AI system backed by the entire web.
The new search box expands, accepts files, and coaches you on what to ask
The changes show a fundamental shift in how Google expects people to interact with the product that generates the vast majority of Alphabet's revenue.
The box itself now dynamically expands to accommodate longer, more conversational queries. Where the old interface subtly encouraged brevity — a narrow field suited to two- or three-word keyword strings — the new design invites users to fully articulate complex questions in granular detail. It also now supports multimodal inputs directly. Users can upload images, PDFs, files, and videos, or drag in content from Chrome tabs, right from the main search interface. Previously, some of these capabilities existed in AI Mode, but reaching them required extra steps. Now they sit at the primary entry point.
Google is also deploying what it describes as an AI-powered query suggestion system that "goes beyond autocomplete." Rather than simply predicting the next word a user might type based on popular searches, the system helps users formulate complex, nuanced queries — essentially coaching them toward the kind of detailed questions that AI Mode handles best.
The new search box is starting to roll out immediately in all countries and languages where AI Mode is available.
Google is merging AI overviews and AI mode into one seamless experience
Perhaps more significant than the box itself is the architectural change happening behind it. Google is unifying AI Overviews — the AI-generated summary panels that appear atop traditional search results — with AI Mode, the more immersive conversational search experience the company launched at I/O one year ago.
Starting Tuesday, this merged experience will be live across mobile and desktop worldwide. A user can type a question, receive an AI Overview alongside traditional results, and then continue directly into a back-and-forth AI Mode conversation to ask follow-up questions — all without navigating to a separate interface.
Reid explained the logic during the press briefing: the new AI search box is "an upgrade of our traditional search box, and so the results take you directly to main search rather than AI mode." She noted that while some power users actively sought out AI Mode, "for most users, they don't actually want to have to think about, do they want more of a traditional page or an AI-forward search experience."
The goal, she said, was to ensure that "for most users, they don't have to think about where to go, they can just go to the search box they're familiar with, and it feels like they get the best experience afterwards."
One billion users and doubling queries reveal how fast search behavior is shifting
Google's decision to redesign the foundational interface of its most important product did not happen in a vacuum. The company shared a set of usage statistics during the briefing that reveal just how rapidly user behavior is already changing.
AI Mode, which launched in the United States at I/O 2025, has surpassed one billion monthly users in its first year. AI Mode queries have been doubling every quarter since launch. AI Overviews, the lighter-weight AI summaries, now reach more than 2.5 billion monthly users. And overall search query volume hit an all-time high last quarter — a data point the company had previously disclosed on its earnings call.
Sundar Pichai, Google's CEO, framed these figures as evidence that AI features are additive, not cannibalistic, to search usage. "When people use our AI-powered features in search, they use search more," he said. He added that he loves "how search has become less about individual queries and feels more like an ongoing conversation, giving users deeper insights and connecting you with the vastness of the web."
Reid reinforced the point: "It's not just that people are searching more, it's that they're searching differently. They're fully expressing their questions in granular detail, asking those follow-up questions and searching across modalities."
Gemini 3.5 Flash gives Google's AI search the speed it needs to work at scale
Under the hood, the new search experience runs on Gemini 3.5 Flash, Google's newest AI model, which the company also introduced at I/O. Google upgraded AI Mode's underlying model to 3.5 Flash to deliver what Reid described as "an even more powerful AI search experience."
Gemini 3.5 Flash is the workhorse of this year's announcements. Google claims it outperforms its previous frontier model, Gemini 3.1 Pro, on nearly all benchmarks while running four times faster in output tokens per second than comparable frontier models. Pichai described it as being "in a league of its own in the top right quadrant" of the Artificial Analysis index, which plots intelligence against speed — meaning it delivers near-frontier quality at dramatically lower latency.
That speed matters enormously for search. A conversational AI search experience that feels sluggish would be dead on arrival for a product that serves billions of queries daily. By coupling the redesigned interface with a model optimized for both quality and throughput, Google is attempting to make AI-powered search feel as instantaneous as the old keyword experience — while being dramatically more capable.
Search can now build interactive visuals and custom mini apps on the fly
The redesigned search box is also the gateway to a set of new capabilities that push search far beyond text-based answers. Google announced what it calls "generative UI" — the ability for search to dynamically build custom widgets, interactive visualizations, and even mini applications in real time, tailored to a user's specific question.
Reid offered a concrete example during the briefing: a user could ask "How do black holes affect space time?" and receive an interactive visual in an AI Overview that brings the concept to life. Follow-up questions would trigger the system to dynamically generate entirely new visuals in real time. This is possible, she explained, because of "a novel real-time code generation system we built in partnership with the Google DeepMind team" that runs on Gemini 3.5 Flash. Generative UI capabilities will roll out to everyone this summer, free of charge.
But Google is going further still. For ongoing tasks — planning a wedding, organizing a move, tracking a fitness routine — users will be able to build what the company describes as customizable, stateful experiences within search, powered by its Antigravity development platform. These require no coding expertise. Users simply describe what they want in natural language, and search builds it. Those experiences will be available in coming months, starting with Google AI Pro and Ultra subscribers in the United States.
AI agents that monitor the web around the clock are coming to search results
The redesign also opens the door to what Google calls "information agents" — AI agents that users can configure directly within search to monitor the web 24/7 for specific conditions and deliver synthesized updates when those conditions are met.
A user could, for example, set up an agent to track market movements in a particular sector with specific parameters. The agent would create a monitoring plan, tap into real-time finance data, and proactively notify the user when conditions are met — complete with links and context for further research. Other use cases include apartment hunting, tracking sneaker drops, or monitoring any topic a user cares about. Information agents will launch first for Google AI Pro and Ultra subscribers this summer.
These agents sit within a much larger strategic pivot that Google articulated throughout the briefing: the company is going all-in on AI systems that don't just answer questions but proactively take actions on users' behalf. Beyond search, Google introduced Gemini Spark, a 24/7 personal AI agent that runs on dedicated virtual machines in Google Cloud. It unveiled the Universal Cart, an intelligent cross-merchant shopping cart. It announced the Agent Payments Protocol for agents to make secure purchases. And it expanded its Antigravity developer platform into a full ecosystem for building autonomous AI agents.
Publishers, advertisers, and SEO professionals face a new reality
The redesign raises profound questions for the sprawling ecosystem — publishers, advertisers, SEO professionals — that has been built around the old model of keyword search and blue links.
If users increasingly express their needs as full, conversational sentences rather than fragmented keywords, the entire discipline of search engine optimization will need to evolve. Keyword-density strategies become less relevant when the AI is parsing natural language intent rather than matching strings. Content that answers deep, nuanced questions in authoritative ways becomes more valuable; content engineered to rank for two-word keyword fragments becomes less so.
For publishers, the stakes are existential. AI Overviews already synthesize information from across the web and present it directly in search results, reducing the need for users to click through to source material. The new seamless AI Mode integration deepens that dynamic: users can now get an AI-generated answer and ask multiple follow-up questions without ever leaving the search page. Google has consistently maintained that its AI features drive more traffic to publishers, but the redesign puts that claim under renewed scrutiny as the search results page becomes more self-contained.
For advertisers — who fund the vast majority of Google's revenue — the shift from keywords to conversations changes the calculus of ad targeting. Conversational queries contain richer intent signals, which could make ad targeting more precise and valuable. But they also create new ambiguities: when a user is in the middle of a multi-turn conversation with AI Mode, where does an ad naturally fit? Google did not detail changes to its advertising model during the briefing, but the structural shift in the interface will inevitably reshape how ads are surfaced and measured.
The search box was always more than a product — it was a habit for billions of people
There is a reason Google chose to redesign the search box rather than simply adding new features behind it. The search box is not just a product element at this point; it is a cultural artifact — one of the few pieces of digital infrastructure used by essentially the entire internet-connected world. Changing it sends an unmistakable message about where the company believes computing is headed.
For 25 years, the search box trained billions of people to think in keywords — to compress their curiosity into the shortest possible string of words. The new box invites them to do the opposite: to think out loud, to upload what they're looking at, to ask follow-up questions, to let an AI system handle the compression.
Pichai tied the company's broader ambitions to a striking statistic: Google's surfaces now process over 3.2 quadrillion tokens per month, up seven-fold from a year ago. The company expects capital expenditures of approximately $180 to $190 billion in 2026 — roughly six times the $31 billion it spent four years ago — largely to support the infrastructure required for this AI transformation. When asked about the future of traditional search, he was direct. "Search is the most used AI product in the world," he said.
The blinking cursor in Google's search box still invites you to type. But after 25 years of teaching the world to speak in keywords, Google is now asking it to speak in sentences — and betting roughly $190 billion that it will.
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Railway secures $100 million to challenge AWS with AI-native cloud infrastructure
Railway, a San Francisco-based cloud platform that has quietly amassed two million developers without spending a dollar on marketing, announced Thursday that it raised $100 million in a Series B funding round, as surging demand for artificial intelligence applications exposes the limitations of legacy cloud infrastructure.
TQ Ventures led the round, with participation from FPV Ventures, Redpoint, and Unusual Ventures. The investment values Railway as one of the most significant infrastructure startups to emerge during the AI boom, capitalizing on developer frustration with the complexity and cost of traditional platforms like Amazon Web Services and Google Cloud.
"As AI models get better at writing code, more and more people are asking the age-old question: where, and how, do I run my applications?" said Jake Cooper, Railway's 28-year-old founder and chief executive, in an exclusive interview with VentureBeat. "The last generation of cloud primitives were slow and outdated, and now with AI moving everything faster, teams simply can't keep up."
The funding is a dramatic acceleration for a company that has charted an unconventional path through the cloud computing industry. Railway raised just $24 million in total before this round, including a $20 million Series A from Redpoint in 2022. The company now processes more than 10 million deployments monthly and handles over one trillion requests through its edge network — metrics that rival far larger and better-funded competitors.
Why three-minute deploy times have become unacceptable in the age of AI coding assistants
Railway's pitch rests on a simple observation: the tools developers use to deploy and manage software were designed for a slower era. A standard build-and-deploy cycle using Terraform, the industry-standard infrastructure tool, takes two to three minutes. That delay, once tolerable, has become a critical bottleneck as AI coding assistants like Claude, ChatGPT, and Cursor can generate working code in seconds.
"When godly intelligence is on tap and can solve any problem in three seconds, those amalgamations of systems become bottlenecks," Cooper told VentureBeat. "What was really cool for humans to deploy in 10 seconds or less is now table stakes for agents."
The company claims its platform delivers deployments in under one second — fast enough to keep pace with AI-generated code. Customers report a tenfold increase in developer velocity and up to 65 percent cost savings compared to traditional cloud providers.
These numbers come directly from enterprise clients, not internal benchmarks. Daniel Lobaton, chief technology officer at G2X, a platform serving 100,000 federal contractors, measured deployment speed improvements of seven times faster and an 87 percent cost reduction after migrating to Railway. His infrastructure bill dropped from $15,000 per month to approximately $1,000.
"The work that used to take me a week on our previous infrastructure, I can do in Railway in like a day," Lobaton said. "If I want to spin up a new service and test different architectures, it would take so long on our old setup. In Railway I can launch six services in two minutes."
Inside the controversial decision to abandon Google Cloud and build data centers from scratch
What distinguishes Railway from competitors like Render and Fly.io is the depth of its vertical integration. In 2024, the company made the unusual decision to abandon Google Cloud entirely and build its own data centers, a move that echoes the famous Alan Kay maxim: "People who are really serious about software should make their own hardware."
"We wanted to design hardware in a way where we could build a differentiated experience," Cooper said. "Having full control over the network, compute, and storage layers lets us do really fast build and deploy loops, the kind that allows us to move at 'agentic speed' while staying 100 percent the smoothest ride in town."
The approach paid dividends during recent widespread outages that affected major cloud providers — Railway remained online throughout.
This soup-to-nuts control enables pricing that undercuts the hyperscalers by roughly 50 percent and newer cloud startups by three to four times. Railway charges by the second for actual compute usage: $0.00000386 per gigabyte-second of memory, $0.00000772 per vCPU-second, and $0.00000006 per gigabyte-second of storage. There are no charges for idle virtual machines — a stark contrast to the traditional cloud model where customers pay for provisioned capacity whether they use it or not.
"The conventional wisdom is that the big guys have economies of scale to offer better pricing," Cooper noted. "But when they're charging for VMs that usually sit idle in the cloud, and we've purpose-built everything to fit much more density on these machines, you have a big opportunity."
How 30 employees built a platform generating tens of millions in annual revenue
Railway has achieved its scale with a team of just 30 employees generating tens of millions in annual revenue — a ratio of revenue per employee that would be exceptional even for established software companies. The company grew revenue 3.5 times last year and continues to expand at 15 percent month-over-month.
Cooper emphasized that the fundraise was strategic rather than necessary. "We're default alive; there's no reason for us to raise money," he said. "We raised because we see a massive opportunity to accelerate, not because we needed to survive."
The company hired its first salesperson only last year and employs just two solutions engineers. Nearly all of Railway's two million users discovered the platform through word of mouth — developers telling other developers about a tool that actually works.
"We basically did the standard engineering thing: if you build it, they will come," Cooper recalled. "And to some degree, they came."
From side projects to Fortune 500 deployments: Railway's unlikely corporate expansion
Despite its grassroots developer community, Railway has made significant inroads into large organizations. The company claims that 31 percent of Fortune 500 companies now use its platform, though deployments range from company-wide infrastructure to individual team projects.
Notable customers include Bilt, the loyalty program company; Intuit's GoCo subsidiary; TripAdvisor's Cruise Critic; and MGM Resorts. Kernel, a Y Combinator-backed startup providing AI infrastructure to over 1,000 companies, runs its entire customer-facing system on Railway for $444 per month.
"At my previous company Clever, which sold for $500 million, I had six full-time engineers just managing AWS," said Rafael Garcia, Kernel's chief technology officer. "Now I have six engineers total, and they all focus on product. Railway is exactly the tool I wish I had in 2012."
For enterprise customers, Railway offers security certifications including SOC 2 Type 2 compliance and HIPAA readiness, with business associate agreements available upon request. The platform provides single sign-on authentication, comprehensive audit logs, and the option to deploy within a customer's existing cloud environment through a "bring your own cloud" configuration.
Enterprise pricing starts at custom levels, with specific add-ons for extended log retention ($200 monthly), HIPAA BAAs ($1,000), enterprise support with SLOs ($2,000), and dedicated virtual machines ($10,000).
The startup's bold strategy to take on Amazon, Google, and a new generation of cloud rivals
Railway enters a crowded market that includes not only the hyperscale cloud providers—Amazon Web Services, Microsoft Azure, and Google Cloud Platform—but also a growing cohort of developer-focused platforms like Vercel, Render, Fly.io, and Heroku.
Cooper argues that Railway's competitors fall into two camps, neither of which has fully committed to the new infrastructure model that AI demands.
"The hyperscalers have two competing systems, and they haven't gone all-in on the new model because their legacy revenue stream is still printing money," he observed. "They have this mammoth pool of cash coming from people who provision a VM, use maybe 10 percent of it, and still pay for the whole thing. To what end are they actually interested in going all the way in on a new experience if they don't really need to?"
Against startup competitors, Railway differentiates by covering the full infrastructure stack. "We're not just containers; we've got VM primitives, stateful storage, virtual private networking, automated load balancing," Cooper said. "And we wrap all of this in an absurdly easy-to-use UI, with agentic primitives so agents can move 1,000 times faster."
The platform supports databases including PostgreSQL, MySQL, MongoDB, and Redis; provides up to 256 terabytes of persistent storage with over 100,000 input/output operations per second; and enables deployment to four global regions spanning the United States, Europe, and Southeast Asia. Enterprise customers can scale to 112 vCPUs and 2 terabytes of RAM per service.
Why investors are betting that AI will create a thousand times more software than exists today
Railway's fundraise reflects broader investor enthusiasm for companies positioned to benefit from the AI coding revolution. As tools like GitHub Copilot, Cursor, and Claude become standard fixtures in developer workflows, the volume of code being written — and the infrastructure needed to run it — is expanding dramatically.
"The amount of software that's going to come online over the next five years is unfathomable compared to what existed before — we're talking a thousand times more software," Cooper predicted. "All of that has to run somewhere."
The company has already integrated directly with AI systems, building what Cooper calls "loops where Claude can hook in, call deployments, and analyze infrastructure automatically." Railway released a Model Context Protocol server in August 2025 that allows AI coding agents to deploy applications and manage infrastructure directly from code editors.
"The notion of a developer is melting before our eyes," Cooper said. "You don't have to be an engineer to engineer things anymore — you just need critical thinking and the ability to analyze things in a systems capacity."
What Railway plans to do with $100 million and zero marketing experience
Railway plans to use the new capital to expand its global data center footprint, grow its team beyond 30 employees, and build what Cooper described as a proper go-to-market operation for the first time in the company's five-year history.
"One of my mentors said you raise money when you can change the trajectory of the business," Cooper explained. "We've built all the required substrate to scale indefinitely; what's been holding us back is simply talking about it. 2026 is the year we play on the world stage."
The company's investor roster reads like a who's who of developer infrastructure. Angel investors include Tom Preston-Werner, co-founder of GitHub; Guillermo Rauch, chief executive of Vercel; Spencer Kimball, chief executive of Cockroach Labs; Olivier Pomel, chief executive of Datadog; and Jori Lallo, co-founder of Linear.
The timing of Railway's expansion coincides with what many in Silicon Valley view as a fundamental shift in how software gets made. Coding assistants are no longer experimental curiosities — they have become essential tools that millions of developers rely on daily. Each line of AI-generated code needs somewhere to run, and the incumbents, by Cooper's telling, are too wedded to their existing business models to fully capitalize on the moment.
Whether Railway can translate developer enthusiasm into sustained enterprise adoption remains an open question. The cloud infrastructure market is littered with promising startups that failed to break the grip of Amazon, Microsoft, and Google. But Cooper, who previously worked as a software engineer at Wolfram Alpha, Bloomberg, and Uber before founding Railway in 2020, seems unfazed by the scale of his ambition.
"In five years, Railway [will be] the place where software gets created and evolved, period," he said. "Deploy instantly, scale infinitely, with zero friction. That's the prize worth playing for, and there's no bigger one on offer."
For a company that built a $100 million business by doing the opposite of what conventional startup wisdom dictates — no marketing, no sales team, no venture hype—the real test begins now. Railway spent five years proving that developers would find a better mousetrap on their own. The next five will determine whether the rest of the world is ready to get on board.
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Agent autonomy without guardrails is an SRE nightmare
João Freitas is GM and VP of engineering for AI and automation at PagerDuty
As AI use continues to evolve in large organizations, leaders are increasingly seeking the next development that will yield major ROI. The latest wave of this ongoing trend is the adoption of AI agents. However, as with any new technology, organizations must ensure they adopt AI agents in a responsible way that allows them to facilitate both speed and security.
More than half of organizations have already deployed AI agents to some extent, with more expecting to follow suit in the next two years. But many early adopters are now reevaluating their approach. Four-in-10 tech leaders regret not establishing a stronger governance foundation from the start, which suggests they adopted AI rapidly, but with margin to improve on policies, rules and best practices designed to ensure the responsible, ethical and legal development and use of AI.
As AI adoption accelerates, organizations must find the right balance between their exposure risk and the implementation of guardrails to ensure AI use is secure.
Where do AI agents create potential risks?
There are three principal areas of consideration for safer AI adoption.
The first is shadow AI, when employees use unauthorized AI tools without express permission, bypassing approved tools and processes. IT should create necessary processes for experimentation and innovation to introduce more efficient ways of working with AI. While shadow AI has existed as long as AI tools themselves, AI agent autonomy makes it easier for unsanctioned tools to operate outside the purview of IT, which can introduce fresh security risks.
Secondly, organizations must close gaps in AI ownership and accountability to prepare for incidents or processes gone wrong. The strength of AI agents lies in their autonomy. However, if agents act in unexpected ways, teams must be able to determine who is responsible for addressing any issues.
The third risk arises when there is a lack of explainability for actions AI agents have taken. AI agents are goal-oriented, but how they accomplish their goals can be unclear. AI agents must have explainable logic underlying their actions so that engineers can trace and, if needed, roll back actions that may cause issues with existing systems.
While none of these risks should delay adoption, they will help organizations better ensure their security.
The three guidelines for responsible AI agent adoption
Once organizations have identified the risks AI agents can pose, they must implement guidelines and guardrails to ensure safe usage. By following these three steps, organizations can minimize these risks.
1: Make human oversight the default
AI agency continues to evolve at a fast pace. However, we still need human oversight when AI agents are given the capacity to act, make decisions and pursue a goal that may impact key systems. A human should be in the loop by default, especially for business-critical use cases and systems. The teams that use AI must understand the actions it may take and where they may need to intervene. Start conservatively and, over time, increase the level of agency given to AI agents.
In conjunction, operations teams, engineers and security professionals must understand the role they play in supervising AI agents’ workflows. Each agent should be assigned a specific human owner for clearly defined oversight and accountability. Organizations must also allow any human to flag or override an AI agent’s behavior when an action has a negative outcome.
When considering tasks for AI agents, organizations should understand that, while traditional automation is good at handling repetitive, rule-based processes with structured data inputs, AI agents can handle much more complex tasks and adapt to new information in a more autonomous way. This makes them an appealing solution for all sorts of tasks. But as AI agents are deployed, organizations should control what actions the agents can take, particularly in the early stages of a project. Thus, teams working with AI agents should have approval paths in place for high-impact actions to ensure agent scope does not extend beyond expected use cases, minimizing risk to the wider system.
2: Bake in security
The introduction of new tools should not expose a system to fresh security risks.
Organizations should consider agentic platforms that comply with high security standards and are validated by enterprise-grade certifications such as SOC2, FedRAMP or equivalent. Further, AI agents should not be allowed free rein across an organization’s systems. At a minimum, the permissions and security scope of an AI agent must be aligned with the scope of the owner, and any tools added to the agent should not allow for extended permissions. Limiting AI agent access to a system based on their role will also ensure deployment runs smoothly. Keeping complete logs of every action taken by an AI agent can also help engineers understand what happened in the event of an incident and trace back the problem.
3: Make outputs explainable
AI use in an organization must never be a black box. The reasoning behind any action must be illustrated so that any engineer who tries to access it can understand the context the agent used for decision-making and access the traces that led to those actions.
Inputs and outputs for every action should be logged and accessible. This will help organizations establish a firm overview of the logic underlying an AI agent’s actions, providing significant value in the event anything goes wrong.
Security underscores AI agents’ success
AI agents offer a huge opportunity for organizations to accelerate and improve their existing processes. However, if they do not prioritize security and strong governance, they could expose themselves to new risks.
As AI agents become more common, organizations must ensure they have systems in place to measure how they perform and the ability to take action when they create problems.
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