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Tag: agentic A.I.

  • 2026: The Year Retail Stops Searching and Starts Thinking

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    A.I.-native commerce is collapsing the traditional funnel and forcing brands to rethink visibility, trust and control. Unsplash+

    For the past decade, it seems that while technology has become increasingly advanced, the online shopping experience has remained largely the same: endless scrolling, reviews we don’t fully trust and price comparisons that often create more confusion than clarity. Despite improvements in logistics and payments, the core workflow—search, scroll, compare, repeat—has barely evolved. With the rise of A.I., that equilibrium is finally breaking. 

    2026 marks the first true departure from the e-commerce model most consumers have grown accustomed to. For the first time, shopping journeys are no longer anchored in static catalogs or keyword searches. They’re increasingly mediated by intelligence systems that can interpret intent, synthesize options and act on behalf of the consumer.

    The rise of A.I.-native shopping, accelerated and exemplified by the first truly agentic holiday shopping season, has made one thing clear: it’s no longer enough for brands to optimize for human shoppers alone. They must also optimize for the A.I. agents that increasingly discover, compare, validate and transact on those shoppers’ behalf. Retail has acquired a new operating system, and it’s powered by agency rather than search.

    Agentic commerce becomes retail’s new OS

    Agentic commerce represents a structural shift far beyond chatbots or plugins. Intelligent, merchant-guided agents replace the old “search-scroll-compare” workflow with curated, intent-driven journeys—cutting down on browsing time, reducing decision fatigue and unlocking conversion rates that traditional e-commerce simply can’t deliver. 

    This shift addresses a well-documented pain point. A recent Accenture survey showed that 74 percent of consumers abandoned their shopping baskets in the previous three months because they felt “bombarded by content, overwhelmed by choice and frustrated by the amount of effort they need to put into making decisions.” When shoppers delegate tedious tasks to A.I. agents, the effects compound. They buy faster, return less and feel more confident in their decisions. For retailers, this does not represent incremental optimization; it is a new operating system that fundamentally changes how value is created and captured. 

    The first true A.I.-powered holiday season proves the shift

    The 2025 holiday season serves as a clear inflection point. Shoppers finally experienced, at scale, the convenience of A.I. handling discovery, comparison and curation, while retailers, in turn, received an unmistakable signal that the traditional commerce funnel is dissolving. One in three shoppers, and a majority of Gen Z, used A.I. tools to generate gift ideas, compare prices across stores, style outfits or build personalized wishlists. What used to require 30 open tabs now happens inside a single, adaptive conversation.

    At the platform level, the signals were equally strong. A.I.-powered assistants expanded into more than 180 countries, as camera-based shopping tools reached tens of millions of users. Discovery no longer begins with a homepage or a search bar. It begins with conversations. 

    Investors are taking note: more than $90 million in funding has already flowed into A.I.-commerce startups, signaling what many call the next great platform wave—one that merges the personalization of 2015’s DTC boom with the scale of 2020’s marketplace era.

    The 6 trends that will define retail in 2026

    GEO supplants SEO

    The decline of traditional search is already underway. As A.I. agents become the primary gateway to product discovery and checkout, keyword-driven SEO will lose its central role. What matters instead is whether an A.I. system can understand a product in context—how it fits a user’s needs, preferences and constraints.

    This is Generative Engine Optimization (GEO), and it will define competitive advantage for the next decade. Brands that structure their data, imagery and metadata for machine interpretation, not just human browsing, will retain visibility. Those that don’t will increasingly disappear from consideration. 

    Virtual try-on and A.I. twins become the standard

    Virtual try-on (VTO) isn’t a novelty anymore. Consumers are already building A.I.-powered avatars of themselves to preview outfits, assemble lookbooks and refine style preferences with automated precision. In 2026, retailers will be expected to meet shoppers inside these environments. The primary “fitting room” will be a digital twin informed by measurements, purchase history and aesthetic signals.  

    Authenticity verification becomes non-negotiable

    As A.I.-generated content floods retail media, trust becomes a prerequisite for discovery and recommendations. Watermarking, credentialing and authenticity scoring will increasingly determine whether a product is surfaced by A.I. engines at all. In an A.I.-mediated retail ecosystem, unverified products lose both credibility and distribution. Trust becomes a non-negotiable, not a differentiator. 

    Returns enter their A.I. era

    With returns expected to exceed $850 billion, the days of blanket free return policies are becoming unsustainable. A.I.-driven sizing recommendations, personalized return policies, predictive risk scoring and agent-guided resolution flows will become standard and essential to protect loyalty without eroding margins. The goal shifts from discouraging returns to preventing avoidable ones. 

    Resale continues to surge

    As economic pressure and cultural values converge, the resale business will continue to explode. With authenticated buyback programs, trade-in incentives and recommerce-led gifting, resale has outpaced traditional apparel by approximately five times. 

    This aligns with generational preferences: 64 percent of Gen Z consumers say they are willing to pay more for environmentally sustainable products, marking resale a commercial strategy rather than a nice ethical play. 

    Physical retail will evolve into A.I.-powered showrooms

    Physical retail will continue its reinvention. By 2027, stores will function as data-rich, immersive showrooms where A.I. agents guide in-store paths, surface personalized recommendations and stitch together online-to-offline journeys seamlessly. The store becomes both a sensory brand experience and a fulfillment node in a unified agentic commerce system.

    Where this leaves retailers

    Together, these shifts point to a single conclusion: retailers now serve two customers—the human who ultimately makes the purchase and the A.I. system that helps them decide. 

    Brands that go all-in on agentic commerce will regain control of the shopping experience, with agentic tools allowing them to embed their own voice, priorities and merchandising strategy directly into A.I.-guided journeys. Those that resist will increasingly compete on price alone, surfaced only when an algorithm deems them interchangeable. When merchants embrace the fact that the most important buyer in the market is no longer a person, but the A.I. that earns that person’s trust, they move back in the driver’s seat. 

    2026: The Year Retail Stops Searching and Starts Thinking

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    Sam Atkinson

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  • Jensen Huang Shakes Vegas With Nvidia’s Physical A.I. Vision at CES

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    Jensen Huang opened CES 2026 with a 90-minute keynote on Nvidia’s latest innovations. Patrick T. Fallon / AFP via Getty Images

    Nvidia CEO Jensen Huang is the biggest celebrity in Las Vegas this week. His CES keynote at the Fontainebleau Resort proved harder to get into than any sold-out Vegas shows. Journalists who cleared their schedules for the event waited for hours outside the 3,600-seat BleauLive Theatre. Many who arrived on time—after navigating the sprawling maze of conference venues and, in some cases, flying in from overseas to see the tech king of the moment—were turned away due to overcapacity and redirected to a watch party outside, where some 2,000 attendees gathered in a mix of frustration and reverence.

    Shortly after 1 p.m., Huang jogged onto the stage, wearing a glistening, embossed black leather jacket, and wished the crowd a happy New Year. He opened with a brisk history of A.I., tracing the last few years of exponential progress—from the rise of large language models to OpenAI’s advances in reasoning systems and the explosion of so-called agentic A.I. All of it built toward the theme that dominated the bulk of his 90-minute presentation: physical A.I.

    Physical A.I. is a concept that has gained momentum among leading researchers over the past year. The goal is to train A.I. systems to understand the intuitive rules humans take for granted—such as gravity, causality, motion and object permanence—so machines can reason about and safely interact with real environments.

    Nvidia enters the self-driving race

    Huang unveiled Alpamayo, a world foundational model designed to power autonomous driving. He called it “the world’s first reasoning autonomous driving A.I.”

    To demonstrate, Nvidia played a one-shot video of a Mercedes vehicle equipped with Alpamayo navigating busy downtown San Francisco traffic. The car executed turns, stopped for lights and vehicles, yielded to pedestrians and changed lanes. A human driver sat behind the wheel throughout the drive but did not intervene.

    One particularly interesting thing Huang discussed was how Nvidia trains physical A.I. systems—a fundamentally different challenge from training language models. Large language models learn from text, of which humanity has produced enormous quantities. But how do you teach an A.I. Newton’s second law of motion?

    “Where does that data come from?” Huang asked. “Instead of languages—because we created a bunch of text that we consider ground truths that A.I. can learn from—how do we teach an A.I. the ground truths of physics? There are lots and lots of videos, but it’s hardly enough to capture the diversity of interactions we need.”

    Nvidia’s answer is synthetic data: information generated by A.I. systems based on samples of real-world data. In the case of Alpamayo, another Nvidia world model—called Cosmos—uses limited real-world inputs to generate far more complex, physically plausible videos. A basic traffic scenario becomes a series of realistic camera views of cars interacting on crowded streets. A still image of a robot and vegetables turns into a dynamic kitchen scene. Even a text prompt can be transformed into a video with physically accurate motion.

    Nvidia said the first fleet of Alpamayo-powered robotaxis, built in the 2025 Mercedes-Benz CLA vehicles, is slated to launch in the U.S. in the first quarter, followed by Europe in the second quarter and Asia later in 2026.

    For now, Alpamayo remains a Level 2 autonomous driving system—similar to Tesla’s Full Self-Driving—which requires a human driver to remain attentive behind the wheel at all times. Nvidia’s longer-term goal is Level 4 autonomy, where vehicles can operate without human supervision in specific, constrained environments. That’s one step below full autonomy, or Level 5.

    “The ChatGPT moment for physical A.I. is nearly here,” Huang said in a voiceover accompanying one of the videos shown during the keynote.

    Jensen Huang Shakes Vegas With Nvidia’s Physical A.I. Vision at CES

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    Sissi Cao

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  • Sierra CEO Bret Taylor Predicts A.I. Agents Will Redefine Business Like the Internet

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    OpenAI chairman Bret Taylor has held many notable titles in tech. Katelyn Tucker/ Slava Blazer Photography

    A.I. agents are the next big platform shift in tech, on par with the dawn of the internet 30 years ago and the rise of mobile apps a decade ago, according to OpenAI chairman Bret Taylor, who also runs his own A.I. startup, Sierra. Speaking at the Skift Global Forum in New York City yesterday (Sept. 18), the tech executive argued that enterprises are now racing to adopt A.I. agents much like they once scrambled to build websites or launch mobile apps.

    “I think this is an opportunity that, probably, the closest catalog would be the birth of the internet,” Taylor said during an onstage interview.

    Taylor has seen several waves of disruption firsthand. At Google in the early 2000s, he helped launch Google Maps. He went on to serve as chief technology officer at Facebook (now Meta), co-CEO of Salesforce, and chair of Twitter’s board during Elon Musk’s tumultuous takeover. In 2023, he was tapped as chairman of OpenAI’s board after the ChatGPT-maker briefly ousted and reinstated CEO Sam Altman.

    Now, his focus is on Sierra, the conversational A.I. startup he co-founded two years ago with former Google colleague Clay Bavor. The company has quickly become a “decacorn,” hitting a $10 billion valuation earlier this month after raising $350 million from Greenoaks Capital. Sierra already counts hundreds of enterprise customers across financial services, health care and retail. A fifth of Sierra’s customers have annual revenue over $10 billion.

    Taylor insists that A.I. agents are more than just cost-cutting tools. Increasingly, they’re revenue drivers. Sierra’s platform is helping companies sell mortgages, make outbound sales calls and even manage payroll for small businesses. “These agents are not only doing services, but also doing sales,” he said.

    And the form factor is evolving. While chatbots dominate today’s landscape, Taylor believes voice-enabled A.I. is “as, or more important, of a channel than chat.” Multi-modal agents are also emerging. For instance, retailers are beginning to process warranty claims by analyzing photos of damaged products.

    Just as the internet gave rise to search engines and aggregation platforms, Taylor expects agentic A.I. to spawn entirely new business categories. The challenge will be ensuring that they meet consumer expectations as their desires inevitably evolve with the technology’s development. “Consumers are moving faster than most companies can make decisions,” Taylor warned, noting that ChatGPT became the fastest-growing consumer app in history. “It’s on all of us leaders to push decisively towards this new world.”

    Sierra CEO Bret Taylor Predicts A.I. Agents Will Redefine Business Like the Internet

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    Alexandra Tremayne-Pengelly

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  • A.I. Agents Are Here. But Who’s Accountable For Their Actions?

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    Without systems that tie A.I. agents back to real humans, autonomy risks becoming a recipe for manipulation and deniability. Unsplash+

    When a semi-autonomous A.I. bot called Truth Terminal sprang up on X, chirping about everything from crypto token prices to religion and philosophy, it kickstarted a new meta not only in the crypto industry but also in the larger tech ecosystem. Truth Terminal signaled the start of the agentic shift, a new era of collaboration between humans and A.I. 

    In the months since then, A.I. agents have multiplied and matured. Today, there are multitudes of A.I. agents that schedule meetings, manage crypto portfolios and act as virtual assistants. Yet as the autonomy of these assistants increases, so too does the surface area for risk and misalignment. The core dilemma remains: even though A.I. agents are making strides in their intelligence and capabilities, these systems cannot take accountability for their actions. So when an A.I. agent makes a costly mistake, who is responsible?The user or the creator? If we are to avoid dystopian effects in the future, this dilemma needs to be addressed. 

    Disembodied agents, disconnected responsibility

    Handing over human responsibilities to computer algorithms and machines brings obvious benefits like efficiency, scale and resource optimization. But it also poses significant risks. Machines have no identity, no legal standing and no way to be reprimanded for wrongdoing. Worse still, there is no existing infrastructure capable of stopping them or holding them accountable. 

    Traditional authentication mechanisms, such as passwords, API keys or OAuth tokens, were never designed for persistent, autonomous agents. They authenticate access, not intent. They validate keys, not accountability. And in an era where A.I. agents can be deployed, forked and redeployed across blockchains, platforms and protocols, this gap is no longer theoretical.

    A.I. agents can now spin up logic, influence financial decisions and shape social narratives. They can be duplicated, modified or spoofed, with the same core model existing under dozens of names or wallets—some malicious, some benign. When things go wrong, responsibility becomes impossible to pin down. Without intervention, we risk unleashing orphan agents, autonomous systems with no cryptographically provable ties to a real person, team or legal entity. 

    Identity as infrastructure for the agentic era

    Identification is merely the first step. The real challenge is making A.I. agents trustworthy. It’s become increasingly evident that the agentic age needs a foundational trust layer. Without it, we’re building systems that can act, transact and persuade, without a reliable way to trace accountability or verify authenticity.

    But we must be careful not to repeat the mistakes of the past. That layer should not rely on surveillance or centralized controls to instill trust or a level of safety. Rather, it should provide attestation and proof of agency: assurances that an agent is supervised by a human or entity who can be held to account. Luckily, such infrastructure is starting to emerge. Systems like Human Passport offer a new paradigm: decentralized identity that is portable, privacy-respecting and built for the realities of Web3 and A.I. Rather than broadcasting identity, these frameworks enable agents to present selective, verifiable proofs, showing that they’re tied to real, unique humans without revealing more than is necessary.

    What accountability looks like in practice

    So, what does accountability look like in a world filled with autonomous agents? A few models for assigning responsibility to machines and algorithms point the way:

    • Revocable credentials. Identity-linked attestations that are dynamic, not static. If an A.I. agent goes rogue or is compromised, the human or entity that authorized it can revoke its authority. These credentials provide a live connection between agents and their real-world sponsors.
    • Cryptographic delegation signatures. Provable claims that an agent is acting on behalf of a person or organization. This turns agents from black boxes into verifiable representatives. Just as SSL certificates confirm a website’s legitimacy, these signatures can verify that an agent’s actions were launched with intent, not spoofed or self-originated.
    • Human-verifiable audit trails. Tamper-proof, on-chain proofs of agency. Even if an agent executes a thousand micro-decisions autonomously, the trail of responsibility won’t vanish into the ether. The goal is to be able to trace accountability without violating privacy.

    It’s essential to act now while this technology is still in its nascent stage. Billions of dollars are flowing into the development and deployment of A.I. agents and with each passing month, these tools gain new capabilities, new wrappers and new interfaces. 

    Suppose we don’t build ownership and identity systems now. In that case, we are laying the foundation for a future defined by fraud, manipulation and deniability, one where synthetic agents operate at scale with no one to answer for them, no way to trace intent and no reliable signal of trust. Because in an agentic future, identity is no longer just about who you are. It’s about proving who acts for you, and when.

    We stand at a critical inflection point. The infrastructure we build now will determine whether this next wave of automation enhances human agency or erodes it beyond recognition.

    Empower, don’t panic

    We’re at the beginning of a new age, one where machines can act with growing independence. But if we fail to embed accountability now, we’ll spend the next decade trying—and likely failing—to fix it. Luckily, we have the tools. Systems like Human Passport give us a path forward where agents can act, but never act alone. Where every action carries a signature. Where autonomy is not the opposite of responsibility, but an extension of it. If we build wisely, the agentic era won’t be a loss of control, but a leap in capability.

    A.I. Agents Are Here. But Who’s Accountable For Their Actions?

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    Kyle Weiss

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  • Bill Gates Launches $1M A.I. Competition to Tackle Alzheimer’s

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    This isn’t the first time Bill Gates has poured money into Alzheimer’s research. Arun Sankar/AFP via Getty Images

    More than 7 million Americans are currently living with Alzheimer’s disease—a figure expected to rise as life expectancies increase. To help accelerate progress, Bill Gates and a coalition of partners are backing a new A.I. competition designed to spur breakthroughs in Alzheimer’s and related dementia research.

    Unveiled today (Aug. 19) by the Alzheimer’s Disease Data Initiative (AD Data Initiative),  the competition will award a $1 million prize to a team that successfully utilizes agentic A.I. to develop innovative solutions. The resulting tools will be made publicly available through the AD Data Initiative’s online research environment.

    “The Alzheimer’s Insights A.I. Prize is our call to the global innovation system to act with urgency,” said Niranjan Bose, interim executive director of the AD Data Initiative, in a statement. “A.I. has the potential to revolutionize the pace and scale of dementia research—providing an opportunity we cannot afford to miss out on, especially with so many lives at risk,” added Bose, who also serves as managing director for health and life sciences at Gates Ventures, the family office funding the competition.

    For Gates, the mission is deeply personal. He helped launch the AD Data Initiative in 2020, just months after his father died at age 94 from the disease. “We are closer than ever before to a world where no one has to watch someone they love suffer from this awful disease,” said Gates in a Father’s Day post this year, calling for faster progress in Alzheimer’s research.

    How can A.I. help?

    Alzheimer’s is a particularly complex disease, with multiple potential causes and a web of biological pathways that have long stymied researchers. Agentic A.I. is well-suited to tackling these challenges because it can autonomously analyze large amounts of data and catch insights that human researchers might miss, according to the AD Data Initiative.

    Beyond data analysis, A.I. could also transform the very nature of Alzheimer’s research. “A.I. is opening the door for a shift from reactive to predictive research—identifying novel biomarkers of early disease patterns, optimizing clinical trial designs, and revealing unexpected opportunities for drug creation and repurposing,” said Gregory Moore, senior advisor at both Gates Ventures and the AD Data Initiative, in a statement.

    Over the years, Gates has poured billions into public health initiatives via his charitable foundation. But his Alzheimer’s work has largely come from his personal fortune, which currently stands at around $118.3 billion. His donations include a $50 million gift to support novel treatments, another $50 million toward clinical trials and early detection and $30 million to create an initiative focused on improving diagnostics.

    Now, with the new competition, Gates is widening the call for innovation. Applications open today for A.I. and machine learning engineers, computational biomedicine experts, tech companies, clinical specialists and Alzheimer’s researchers. Semi-finalists will be announced in December, with finalists competing next March at the Alzheimer’s Disease and Parkinson’s Disease Conference in Copenhagen.

    Bill Gates Launches $1M A.I. Competition to Tackle Alzheimer’s

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    Alexandra Tremayne-Pengelly

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