IEEE Spectrum
In 2018, Amazon brought me in as the lead UX Sound Designer for Astro, their first consumer home robot. Astro used cameras and other sensors to map and navigate your home and workplace, and could proactively patrol, check up on loved ones, and transport small items using its built-in cargo bin. While there was a well-defined feature set and form factor, initially there was no character direction. In fact, even before Astro had a name, there were two main questions—was it simply Alexa on wheels, or was it a robot with its own character?The Astro team was divided. One option was to focus on Alexa, and treat the mobile robot simply as an added utility. I argued for Astro to not focus on Alexa, along with the majority of the UX team. Our belief was that a thing that moves through your home and turns toward you with intent can never be just an appliance. People would ascribe character to whether we wanted them to or not, and so the only question was whether we shaped that character or let it happen by accident.Ultimately, Astro became Astro rather than Alexa, and user testing backed up our decision. People didn’t see the robot as Alexa. They saw it as its own character, and that’s what they wanted it to be. Alexa on the device felt somewhat strange and creepy, but building Astro its own voice was too slow and expensive in 2018. So, we settled on Alexa as a supporting character that handled any actual talking, while Astro was the main character, communicating as much as it could without words, through sound, motion, and facial expressions.I had been brought on to the Astro team to define the robot’s sound design language and voice. But there was no one to flesh out the robot’s actual character. You cannot make a single real decision about a character without defining it first. Every choice about how Astro moved, sounded, paused, or reacted was a character choice, and those choices required all disciplines working together. As Sound Lead, I was weaving together sound, motion, and character, and how they played together inside each story moment. The animators, who programmed Astro’s motion and facial expressions, were extraordinary at what they did, but the emotional arc they were animating came from the sound (and therefore character) work first. So I stepped into that role, which is where my real work started. What I learned about building character for robots applies to nearly everything being built in embodied AI right now.Character Is a Design SystemDeveloping a character for Astro meant answering questions that had never been asked about a product at Amazon: What is the emotional range of this robot’s baseline state? How does this robot communicate uncertainty without eroding trust? Where is the line between being expressive and annoying? What are the vulnerabilities of this device’s character?These are design questions. They have real answers, and every team working on the product has to build from them. For example, Astro’s emotional range was designed to be relatively small at first. We never wanted Astro to get too sad or too angry. It could play sad, but would snap out of it quickly and end the reaction on a high note to keep things positive. Character leaks out of every seam and can create a disjointed experience if not defined correctly. Even if it’s just animation timing that’s slightly off, or a response that’s technically correct but contextually tone-deaf, users feel every one of these inconsistencies, even if they can’t name them. Watch what happens at the beginning and end of this Sing sequence: Astro goes from nothing, into the emotional moment, and then lands back on nothing. No build up, no cool down, no sense that the feeling came from somewhere or had anywhere to go. I pushed hard for better character stitching, the transitions in and out of expressive moments that make a performance feel continuous rather than assembled, but it never got implemented. The moment itself works. But without the stitching, it reads as a clip playing on a robot rather than coming from within the robot character itself.Story and Sound at the BeginningWe had decided that Astro would have no spoken dialogue, but it had something that functioned the same way: a vocabulary of sounds, tones, and rhythms that acted as its voice. This vocabulary became the leading output of the character’s personality. The robot’s motion and facial expressions were built around it.Astro’s wake-up sequence is a great example. Waking wasn’t just a boot animation on the screen; it was an entire performance. Slow and humble at first, the robot oriented itself quietly, then stretched its screen, checked its wheels, and finally, with an upward gesture toward its telescoping mast, it popped it up slightly, and did a little dance of joy. Sound, motion, and eyes hit every beat together in full choreography. The character’s output in that sequence was first written as a story. Astro is waking up in its new home for the first time. Its main aspiration is to be part of a family, so this is the moment it has been waiting for, this is its purpose. Being the responsible character that it is, it wants to make sure everything is good to go before it introduces itself and starts learning its new home.This narrative came first because it drove every other decision that we made. After the story was written, sound gave that story a metaphorical voice: the excited tones, the pacing as it checked its wheels, and the bright melodic phrase as Astro looked up at its new family for the first time and introduced itself. Once the sound was laid down, animation did their thing with motion and facial expressions, taking cues from the emotional arc the sound had established. Motion didn’t lead—it followed the feeling of the story and the sounds, the same way an animator follows a recorded vocal take.That wake up sequence became one of the most-discussed moments in early user testing. People described it as “alive.” What they were responding to wasn’t any single element. It was all three channels (sound, motion, and facial expressions) expressing the same defined character in harmony.Context Is Where Character Becomes RealThe most compelling characters are defined not by a fixed disposition but by how they respond to their environments and the people in them. They’re still recognizably themselves even as they adapt. This is what I call contextual character. A robot living in a home doesn’t occupy a single emotional state. It moves through rooms with different energy, encounters people in different moods, operates at different times of day, and responds to an endless range of social situations it was never explicitly designed for.We got close to a contextual character output with Astro’s sound. When a specific piece of environmental context was fed in, the system adapted beautifully, and Astro felt completely alive. But every state like this was still a prediction we made by hand—a situation we had to imagine in advance and design a response for. A random home throws more situations at a robot than anyone can possibly predict, so there was always a longer tail of moments the system was never prepared for.The difference between a product people describe as “smart” and one they describe as “aware” often comes down to this. Smartness is capability. Awareness is context. Presence is character. And character is always in reaction to the people around it, to its environment, to its own evolving state. That’s what makes it feel like something is emotionally present with you.This is where AI changes the game for character design in ways that go well beyond what was possible with Astro. AI-driven adaptation doesn’t require the contextual predictions that we relied on. It learns the specific rhythms, preferences, and emotional context of the people it lives and works with. The character doesn’t just respond to context. It grows into it.What Industry Is MissingThe character and soul of the impending wave of embodied AI products appears to almost always be an afterthought. And character defined late is character defined by default. It becomes the sum of a thousand small decisions made by different people thinking about anything but character. People project character onto devices whether you plan for it or not, especially if those devices move—a robot that moves is already a character. If nobody has designed this character, the result will be products that feel like nothing, or worse, feel confusing and not trustworthy. Technically impressive, but lifeless.We did not get this fully right with Astro. So many things were moving in parallel that character was rarely treated as a utility, and it made sense why. When you are building a first-of-its-kind product, the things that are the loudest are the ones that break, the deadlines, the costs, the features a customer can point to on a box. Character is quieter than all of that. It’s easy to assume it can come later. On a team as large as the Amazon Astro team, it’s lucky to get any idea onto the roadmap when it is competing with a hundred others that all feel more urgent in the moment. None of this came from people not caring. It came from character being the kind of thing that is hard to prioritize until you see what its absence costs you.My Asks to Product LeadersIf you are building a product that will share physical or conversational space with people, three things are worth considering:Define character before you define interactions. You need a defensible character with enough emotional logic to answer hard questions consistently. Find answers to character questions early, and have every discipline build from the same foundation.Build story and sound into the character pipeline, not the production pipeline. Story and sound developed alongside character definition has the chance to inform motion, expression, and interaction logic. This requires a different kind of collaboration, and a different kind of hire.Design for adaptation, not just consistency. A consistent character is necessary, but the products that will matter most in people’s lives are the ones that deepen through use. The infrastructure to support that is more and more accessible, but the design thinking to take advantage of it is still rare.An unabridged version of this story can be read on Medium.
Robotics Research News -- ScienceDaily
The race to build data centers in space is gaining momentum as AI drives unprecedented demand for computing power. Orbital facilities could tap into abundant solar energy and avoid many of the environmental challenges faced on Earth. Yet space remains a harsh and expensive place to operate, with major hurdles including cooling, maintenance, radiation exposure, and orbital debris.
IEEE Spectrum
On April 19, 2026, the Honor Lightning humanoid robot ran a half-marathon in 50 minutes and 26 seconds, beating the human world record by 7 minutes and the best robot time from 2025 by almost two hours.How did they do it? Is there some magical technology or technique that unlocked this performance? How did they beat the significantly better-known Unitree (who reportedly had to supply an ice backpack to try and complete the race without overheating)? My doctoral thesis involved building and controlling hopping and running robots, and since then I’ve tried to design and build efficient commercial legged robots, giving me a decent idea of the constraints involved. In this article, we take a look at the fundamental underlying constraints to try and answer these questions.The Physics of RunningRunning consists of alternating phases of a leg pushing against the ground (“stance phase”) and the body flying through the air (“aerial phase”). In the aerial phase, the body falls due to gravity, losing vertical momentum. The leg in stance phase pushes against the ground to redirect the vertical momentum upward, while the other leg swings forward to reposition for the next foothold.Electric motors use energy to produce torque- the higher the torque, the more energy lost as heat. Adding a geartrain after the motor amplifies its torque and reduces its speed. A large reduction helps with torque production, but since the rotor of the motor itself has to spin faster, it becomes very sluggish at accelerating its output. This is obviously bad for the swing phase described above. These competing effects mean that for a particular motor, there is usually a sweet spot for the gear ratio: The power consumed by a robot leg is minimized at an optimal gear ratio (30:1 in this example).Avik De/DatawrapperHow Honor Did ItWhile the Lightning’s motor specifications are not published, the hip and knee motors roughly have a 110-150mm outer diameter. For an approximate set of motor parameters, I looked to the ILM115x25 motor due to its relevant size and detailed specifications.We can use a simple physics model to estimate the power consumption for running at 7 m/s (the Lightning’s average half marathon speed) as gear ratio varies: The light blue curve shows how to pick the optimal gearing (45:1). The dark blue curve shows how much heat will be produced in the knee motor, ~150W for the optimal gearing.Avik De/DatawrapperWe see that the drivetrain is not magical: with a gear ratio chosen for this task (we’ll return to this below), the approximate robot power consumption would be a very reasonable 400W.However, the dissipated knee power ( typically the main thermal limiting factor) is ~150W. This is almost an unavoidable consequence — running at human speeds with a humanoid-sized robot will inevitably generate this amount of heat! Over a prolonged period, keeping the motor from overheating would be a challenge, but the Lightning has a trick up its sleeve:According to Honor, the liquid - cooling pipes penetrate deep into the motors like capillaries. The high - power liquid pump has a heat - exchange flow rate of more than 4 liters per minute. Each of the four drive motors in the lower limbs is equipped with an independent liquid - cooling circuit.Liquid cooling is not new, but it’s definitely not a commodity. It has shown up in research periodically, and on the commercial side Apptronik tried it for a few of their prototypes but (to my knowledge) does not use it on their main Apollo platform. Basic air convection-based cooling would not continuously be able to extract 150W out of the knee motor, and so the cooling technology is a key enabler of this type of performance.Why Others Couldn’t CompeteWhy did Honor’s competitors, including more established and widely-shipped humanoids such as from Unitree or Agibot, not compete as well?We can use the same model to generate an equivalent energetics plot for walking at 1.5 m/s, a much more modest but potentially more common activity for a commercial humanoid robot: The solid and dashed light blue lines show a running-optimized design, while green lines show a walking-optimized design. The optimal ratio for walking is much lower (30:1 vs 45:1). However, the power dissipated in the knee motor while running (dark blue) is much higher at 30:1 vs 45:1—the price to pay for running with a walking-optimized design.Avik De/DatawrapperThe plot adds a new green curve for the walking power, and the optimal gearing is significantly different!Let’s say you design your robot to excel at the normal walking task and choose the green design with 30:1 gearing. The knee motor power to run a half marathon is over 300W (red arrow), more than 2x what we had with the running-optimized design. It wouldn’t be so surprising to need ice packs!Conversely, visually following the green curve shows that the running-optimized robot wastes more power for walking. Using larger motors sized for running increases the weight of the robot and wastes power when it is standing or walking. The larger motors also pose practical issues like bumping into objects while operating in homes or factories.Closing ThoughtsHonor’s half marathon performance was an impressive engineering effort and result. It didn’t need any magical leaps in technology, but the deployment of the capillary motor cooling solution is a notable advance without which this running pace would have been unsustainable. The cooling, weight optimization, and robustness advances may well be useful for more practical purposes like carrying heavy payloads down the line. The Honor Lighting robot [right] has much larger motors driving its legs than the Unitree H1 robot [left], making it a more efficient runner but a less efficient walker.Left: Wei Zhiyang/Zhejiang Daily Press Group/VCG/Getty Images; Right: VCG/Getty ImagesHowever, the Lightning is not as well-suited to other tasks as a robot designed for greater versatility. Engineering is always characterized by tradeoffs, and making the correct ones separates good products from great ones. With consistently improving AI language models, this very human skill is becoming the most valuable one an engineer can have.The news coverage seemed to overly focus on the fact that the human half-marathon record had been broken by a robot. Machines and humans have very different capabilities and constraints, so why should we ever have expected the half marathon time for a robot and human to be related? As in Deep Blue’s 1997 defeat of Garry Kasparov in chess, where it couldn’t physically move the pieces, the Honor robot’s capabilities are much narrower than a human running elbow-to-elbow with other runners while visually navigating the course without GPS. Comparing the robot runner to a human runner is just an apples-to-oranges comparison, and only risks diminishing Honor’s engineering achievement on one hand, and human athletic achievement on the other.
IEEE Spectrum
This article is part of our exclusive IEEE Journal Watch series in partnership with IEEE Xplore.
As robots advance in terms of dexterity and other physical capabilities, it becomes more likely that humans may find themselves working alongside them. If that happens, how will robots’ emotional capabilities need to advance for them to successfully work with people?In a recent study, researchers trained collaborative robots to read human emotions by not only accounting for facial expressions, but also contextual factors in the interactions as well. Through experiments with 40 volunteers, the researchers then evaluated how a robot’s ability to read human emotions and adjust its behaviour in turn impacted a human’s perception of the robot and its capabilities as the two collaborated on tasks. The results—which show that the emotional capabilities of robots only go so far with humans—were published 18 May in IEEE Robotics and Automation Letters.Seung Chan Hong led the study as part of his undergraduate thesis while studying at the University of Melbourne, in Australia. He notes that, while there has been a lot of hype in the advancing physical abilities of robots, this is only one piece of the puzzle. “We need to also innovate when it comes to them actually interacting with humans, not just their physical capabilities,” he says.This prompted him to dig deeper into the emotional aspects of human-robot interactions. First, Hong and his co-authors decided to train a robot to read human emotions using a vision language model (VLM), which is similar to large language models such as ChatGPT, but which can also take visual inputs.Training VLMs for Human Emotion RecognitionTo train their VLM, the researchers had volunteers watch videos of robots handing over objects to humans—with varying degrees of success—and describe the emotions the humans were expressing. Importantly, the volunteers labeling these videos were able to take into account more context in these interactions, rather than reporting solely on the facial expressions of the humans in the video. For example, a person pausing to think with a furrowed brow may simply be concentrating on their task at hand, and not necessarily be angry. Contextual factors such as drumming their fingers, pursing their lips, or other behaviors can point to the real cause of a person’s furrowed brow.The researchers then compared their VLM to a conventional AI system which relies on standard facial analysis and object tracking that is used in human-robot interactions. They found that the VLM outperformed the traditional approach. On a scale from 0 (no similarity in meaning to the emotion identified by the human volunteers) to 1 (a perfect match in meaning), the conventional AI system achieved a score of 0.77. In comparison, the VLM achieved a score of 0.86.Hong says, “I think [the VLM] was able to align with what human observers were seeing a lot better, because it wasn’t just looking at the person’s face for a brief amount of time, but seeing the whole scene—where the person was and what they were doing, and how they were interacting with the robot.”In a second experiment, the research team asked 40 volunteers to interact with a robot using their VLM—but purposefully programmed the robot to make an error. The robot then had to offer either an emotionally adaptive apology that accounted for the human’s perceived response to the mistake, or a pre-scripted spoken apology.Participants overwhelmingly preferred the emotionally adaptive response, with 31 out of 40 people favouring this approach over a boilerplate apology.However, their survey responses underscored how this emotional adaptivity was far less important than the robot’s functionality. After collaborating with a robot that failed in its task, many participants ranked their trust in the robot as lower, regardless of how it apologized for its mistake. “A personalized apology acts as a social lubricant, but it cannot repair the trust lost by the robot failing its physical task,” Hong says.Interestingly, the VLM classified the emotions of its human partners similarly to human volunteers who observed an interaction from a third-party perspective. But when the VLM’s assessments were measured against humans’ self-reported emotions during the second experiment—the most accurate descriptions of their true emotions—its ability to accurately predict emotions dropped significantly.“While the VLM is a good observer of outward social cues, it isn’t a mind reader,” says Hong. “It matched third-person human observers well, but it didn’t always align with the user’s internal, self-reported feelings.”Together, these results show that robots are not perfect at reading human emotions. So while people might appreciate their efforts, they still ultimately will want competent co-workers.
Robotics Research News -- ScienceDaily
Scientists discovered that rice behaves in a highly unusual way: it weakens under rapid compression but stays stronger when pressure is applied slowly. Using this effect, they engineered a new material that reacts differently to gentle movements and sudden impacts. The material can adapt its stiffness automatically, opening the door to safer soft robots and protective equipment that responds instantly to collisions.
Robotics Research News -- ScienceDaily
Scientists found that transfer learning can make the search for new physics in the universe much faster, slashing the need for expensive simulations. Yet the approach can backfire when AI relies too heavily on familiar patterns, potentially missing evidence of something truly new.
Robotics Research News -- ScienceDaily
Researchers gave top AI models a classic attention test used in psychology and found a major flaw. While the models could correctly name colors in short lists, their performance deteriorated sharply as the task became longer and more complex. Some leading systems fell from over 90% accuracy to nearly complete failure.
AI | VentureBeat
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 askThe 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 experiencePerhaps 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 shiftingGoogle'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 scaleUnder 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 flyThe 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 resultsThe 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 realityThe 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 peopleThere 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.
robotics | TechCrunch
Samsung, Hyundai and LG just bet on the startup that wants to be robotics' data backbone.
robotics | TechCrunch
Genesis AI, a startup that raised a $105 million seed round to build foundational AI for robotics, has unveiled its first model, GENE-26.5, but also a demo showcasing a set of robotic hands performing complex tasks.
robotics | TechCrunch
Wonder wants to turn its robotic kitchens into AI-powered “restaurant factories,” letting anyone spin up a virtual food brand with a prompt.
robotics | TechCrunch
Lucid Bots has seen demand accelerate over the last year for its window cleaning drones and power washing robots.