I spent two days at HumanX 2026 in San Francisco covering 27 sessions across AI infrastructure, enterprise adoption, agentic systems, security, creative, retail, and finance.

I heard about AI in marketing. AI in brand strategy. AI in customer service. AI in software development. AI in creative production. AI in financial infrastructure. AI in cybersecurity. AI in retail design. AI in financial crime. AI in compliance.

I did not hear a single session about the supply chain.

Not one.

For someone who came up through operations and supply chain, who has personally sat with demand forecasting spreadsheets that were wrong more often than right, navigated supplier disruptions without early warning, and made inventory decisions that were really just educated guesses dressed up in numbers. For an industry that sits at the core of the global economy, the silence was striking.

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What we mean when we say “supply chain.”

To be precise, this is not about last-mile logistics or route optimization—the areas that do get occasional attention. The Supply Chain word gets used loosely, usually.

This is about upstream decision-making: demand forecasting, supplier risk management, inventory positioning, and procurement intelligence.

Demand forecasting: predicting what you’ll need to sell or produce before you’ve sold or produced it.

Supplier risk management: identifying which suppliers are fragile before they fail you.

Inventory positioning: deciding how much of what to hold where, across a network that doesn’t sit still.

Procurement intelligence: understanding pricing patterns, lead time trends, and supplier behaviour across hundreds of relationships simultaneously.

These are decisions that get made every week in every company that makes or moves physical goods. They are made by people working with incomplete information, under time pressure, using tools that were not designed for the complexity of the problem. And they are decisions where being systematically better — not perfect, just better — compounds into enormous financial advantage over time.

Yet at a conference dedicated to AI’s transformation of business, they were absent. AI should be solving the industry’s problem right now.

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What I did hear

Here is what I did hear, to be fair.

P&G’s CIO Seth Cohen spent time on supply chain automation — specifically, unattended manufacturing scaled across nine locations, and molecular discovery work that cut development timelines from years to months. For a company like P&G’s scale, those results are genuinely significant. But they are the results of a decade-long data infrastructure investment that most companies, including most large companies, have not made.

Walmart’s Daniel Danker gave one supply chain example: a remote Canadian store used an internal tool called Code Puppy to combine weather data and ferry schedules for inventory planning. It’s a good story — a frontline associate solving a local operations problem with a tool headquarters gave them. But it’s a point solution, not a framework, and it was mentioned in passing in a session primarily about AI democratization.

That was essentially it. Two examples, mentioned briefly, in sessions about something else. For an industry representing somewhere between $15 and $20 trillion in global economic activity, that’s a remarkably thin presence.

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Why the supply chain is the most AI-ready industry nobody is talking about

What makes this gap surprising is that the supply chain is, on paper, one of the most AI-ready industries in existence.

It runs on data. Sensor data, transaction data, demand signals, supplier data, logistics data, and financial data. The data infrastructure in large supply chains is often more mature than in the marketing and creative functions that dominated the HumanX agenda.

The problems are structured. Unlike brand strategy or creative judgment, supply chain decisions are historically modelled, mathematically defined, and directly measurable. A demand forecast is either accurate or it isn’t. A stockout either happened or it didn’t. An inventory position either matched demand, or it left cash sitting on a shelf or a gap in a customer’s order. The feedback loops are tight, and the outcomes are concrete.

The stakes are direct. Having spent time inside these decisions, I can tell you that even small improvements in forecast accuracy at a meaningful scale translate into real money — in working capital, in margin, in customer retention. A 2% improvement in forecast accuracy at a company running substantial inventory isn’t a rounding error. It’s a financial event.

So why wasn’t it on stage?

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Three reasons for the gap

The first is audience composition. HumanX skews toward technology leaders, marketing executives, founders, and investors. Supply chain and operations leadership tends not to show up at general AI conferences. They attend industry-specific events — operational forums, ERP user conferences, sector trade shows. The gap in the room creates a gap in the agenda.

The second is vendor invisibility. The companies building AI for demand sensing, supplier risk, and logistics optimization are largely invisible outside their industry. They’re specialized, often enterprise-only, and they sell through procurement channels rather than developer communities. They don’t generate the kind of press coverage that gets sessions programmed at a conference like HumanX.

The third — and this is the one that matters most — is that the enterprise supply chain is genuinely hard to disrupt quickly. The data is siloed across ERP systems, warehouse management platforms, and procurement tools built over decades. Integration alone is a multi-year project. The risk tolerance for AI-driven decisions in the supply chain is low because the consequences of errors are operational and financial. You can course-correct a bad marketing campaign. You cannot easily recover from a production halt caused by a procurement decision that an AI made without the right context.

This is exactly why AI hasn’t disrupted the supply chain at the pace it’s disrupted other functions. And it’s exactly why, when it does, the value creation will be disproportionately large.

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What the conference did tell me

The sessions I attended gave me frameworks that apply directly to this gap, even though none of them were about the supply chain.

Databricks CEO Ali Ghodsi said something that stayed with me: current models are sufficiently capable, but they fail in enterprises because they lack context. The bottleneck isn’t the AI. It’s the organizational and data infrastructure around it. He expects enterprise AI adoption to take five to ten years.

That timeline maps almost exactly to where supply chain AI is in its adoption curve. The context problem is especially acute here. A demand forecasting model trained on historical sales data without context about upcoming promotions, competitor pricing moves, macroeconomic shifts, or supplier lead time changes will produce outputs that experienced planners will correctly distrust — because they know what the model doesn’t know. The data exists. The integration and context layer doesn’t.

The building of a trustworthy agentic AI session reinforced this from a different angle. Panellists from Dataiku made a distinction that matters enormously for supply chain: back-office decision agents — the ones affecting clinical outcomes, credit decisions, or supply choices — require far stronger testing and explainability than personal productivity tools. A demand forecasting agent that informs procurement isn’t a chatbot. It needs to be auditable, explainable, and designed to fail gracefully. Most current AI deployments are not built to that standard.

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What finance figured out that supply chain hasn’t yet

What I found most instructive at HumanX wasn’t what was said about supply chain. It was watching what happened to industries that got forced into AI governance before they were ready — and what that forced them to build.

The finance sessions were the clearest example. Multiple panels addressed AI compliance frameworks in regulated financial services — what one session called the “compliance flywheel.” The argument was that embedding compliance, risk, and governance early in AI product development actually accelerates innovation rather than slowing it. Shared semantic definitions, data lineage, and auditability become infrastructure that compounds over time. Organizations that treat compliance as an early-stage design constraint end up with more durable systems than those that bolt it on later.

The financial crime session added a sharper edge to this. Jonathan Levin of Chainalysis described how generative AI has dramatically lowered the barrier to entry for financial fraud — enabling impersonation, automation, and scale that wasn’t previously possible for lower-skill actors. The response from defenders has been to build proactive threat-hunting systems, intelligence-sharing networks, and AI agents that can process evidence and flag suspicious patterns faster than any human analyst.

I kept thinking about procurement fraud while sitting in those sessions. Supplier impersonation. Fake invoices. Bid manipulation. Ghost vendors. These are supply chain problems that have existed for decades and are about to get significantly harder to detect as AI makes fraudulent activity more convincing and more automated. The financial services industry is building defensive infrastructure right now because regulation and litigation forced the conversation. Supply chain hasn’t been forced there yet. But the same pressures — fraud escalation, operational failure, regulatory scrutiny, and eventually litigation — are coming.

The compliance frameworks being built in regulated finance are the template for what supply chain AI governance will eventually need to look like. The difference is in the timeline. Finance is building it now under pressure. Supply chain will build it later, under more pressure, starting from further behind.

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The investment angle

The publicly traded companies most exposed to the supply chain AI wave are not the model providers. They are the enterprise software platforms that own the data: the ERPs, the warehouse management systems, the supply chain visibility platforms, the procurement analytics tools. Every one of those companies is currently navigating the same question: do they build AI natively into their platforms, partner with AI providers, or get disrupted from below by AI-native startups that don’t carry decades of integration debt?

That question is not answered. The window where it remains unanswered is the window where the investment opportunity is most interesting — both in the incumbents navigating the transition and in the new entrants who might make the integration question irrelevant.

I track this closely because I sit at the intersection of it. I understand the operational problem from having lived inside it. I understand the AI capability layer from building tools on top of it. Those two lenses together are what make the gap visible.

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I came back from HumanX with a lot of material about where AI is moving and what serious operators and investors think. Most of it confirmed what I already believed about infrastructure, reliability, and the gap between demo performance and production reality.

The most valuable thing I came back with was silence.

Nobody talked about the supply chain. Not because it isn’t ready. Not because the problem isn’t large enough. Because the people who understand the problem and the people building the tools are not yet in the same room.

Finance got there first because it was forced. Supply chain will get there when it is forced to.

The question worth asking now — before the forcing event — is which companies are building the governance, the data infrastructure, and the AI capability to be ready when that moment arrives. Because the ones who are will look obvious in hindsight. They always do.

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Jinlu Wang has a background in supply chain, ERP implementation, and enterprise operations. She now builds automated trading systems and web applications and covers AI infrastructure, fintech, and enterprise technology for Trade with Harp, a paid investment research and trading community.

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I want to start with a confession.

I built trading bots that my community relies on every day. I’ve spent more hours than I can count debugging alert logic, fixing duplicate signals, tuning parameters, and rebuilding systems from scratch when they broke in ways I didn’t anticipate. I believe in what I’ve built.

And I still make bad trades.

Not because the tools don’t work. They work. But because somewhere between the alert firing and my finger hitting the button, I stopped being a systematic trader and became a human being with opinions, emotions, a position that was already down, and a completely irrational certainty that this next one was going to recover everything.

The tools didn’t fail me in those moments. I failed myself. And I’ve watched enough traders in my community make the same mistake often enough to know that what I’m about to say is the conversation the AI trading tools industry is actively avoiding — because it doesn’t help them sell subscriptions.

So let’s have it.

The alert is not the trade

This sounds obvious. It isn’t, apparently.

I built my swing bot to scan multiple timeframes, identify setups based on criteria I defined when I was calm and thinking clearly, and push alerts to my Discord. It does that job well. But I’ve sat in live sessions and watched members receive an alert and enter a position within seconds — no volume check, no broader market context, no consideration of whether they’d already taken two losses that morning and maybe shouldn’t be trading at all.

The bot flagged a setup. They heard a decision.

Those are not the same thing. An alert tells you something worth examining just happened. It says nothing about whether you should act, how large your position should be, what your stop is, or whether this particular day is a good day for you to be pulling triggers at all. A VWAP reclaim on above-average RVOL in the context of a strong sector tape means something different than the same signal on a news-driven spike in a choppy overall market. The bot can’t know that. You have to know that.

This is the gap nobody in the AI tools space wants to talk about. The tools are built to look impressive in demos — fast, confident, clean. What they’re not built to do is slow you down when you should be slowing down. And the moments that cost traders the most money are rarely the ones where they had bad information. They’re the ones where they had good information and moved on it before they’d thought it through.

AI amplifies what you already are

This is the thing I had to learn the expensive way.

There was a stretch where my swing bot’s signal quality was genuinely good — the setups it was flagging were clean, the follow-through was there, and the logic held up. And my account still underperformed during that period. Not because of the bot. Because I was also day trading separately, making impulsive entries on tickers that had nothing to do with my system, averaging into losers, ignoring the rules I’d set for myself and justifying every single exception in the moment.

The bot was doing its job. I wasn’t doing mine.

What that experience clarified — painfully, over multiple sessions — is that an AI tool is not a floor that catches you when you’re trading badly. It’s a multiplier on whatever you’re already doing. If your discipline is solid, your risk management is consistent, and you’re genuinely following a process, the right tool can sharpen your edge in real ways. If you’re chasing, overtrading, letting losses make you reckless — the tool just helps you do all of that with more information and faster execution.

I have a rule I keep for myself: by 10:30 AM on Fridays, I’m out of everything. No new entries. The week is done. It took a while to build that discipline. No bot gave it to me. I built it because I’d seen too many times what happened when I didn’t have it.

Rules like that — the ones you make when you’re clear-headed and enforce when you’re not — are the real edge. The tool is secondary to the trader running it.

What these tools are actually good for

I don’t want to be all doom about it, because there’s genuine value here if you understand what you’re working with.

The honest case for AI trading tools comes down to three things.

Consistency. My system runs the same scan in hour four of a session as it ran at open — same criteria, same logic, same output. I don’t. By mid-afternoon, I’m tired, I’ve already made decisions I’m second-guessing, and my pattern recognition is quietly degrading in ways I can’t fully perceive in the moment. The bot doesn’t have that problem. It doesn’t have a bad day. In a domain where emotional fatigue kills performance, that’s nothing.

Coverage. I physically cannot watch 200 tickers across multiple time frames simultaneously and catch every relevant setup. The bot can. It misses nothing on its watchlist, which means I catch opportunities I would have missed and pass on noise I would have rationalized into something.

Record-keeping. Every alert my system generates is logged. Every condition that triggered it is documented. Over time, that data becomes something genuinely valuable — an honest performance record, stripped of the selective memory that leads most traders to overestimate their win rate because they remember the good trades more vividly than the bad ones. Automated systems don’t have selective memory. Mine has shown me setups I thought were working that weren’t, and setups I’d been dismissing that were consistently cleaner than I’d given them credit for.

All of that is real. None of it decides for you.

A word on bad products

I know traders who have been burned badly enough by AI tools that they’ve written off the whole category. Some of those experiences were completely legitimate — there are genuinely bad products in this space. Backtests built on look-ahead bias. Parameters curve-fitted to a specific period of market history that fall apart the moment conditions change. Marketing copy that implies a documented edge the underlying system has never actually demonstrated in live conditions.

Healthy skepticism toward specific products is rational. Dismissing everything because one tool was garbage is like avoiding all cars because one had faulty brakes.

The right question about any tool isn’t “can AI work in trading” — the evidence that systematic, data-driven approaches generate an edge is extensive and goes back decades. The right question is whether this specific tool, built on this specific logic, by these specific people, actually does what it claims when real money is on the line. Ask for a live track record. Not a backtest. A live track record, with drawdowns, with losing streaks, with all the ugly periods included.

If they don’t have one, or won’t show you one, that’s your answer.

The part nobody wants to hear

AI is not going to make trading easier.

The market at the margin is zero-sum. The participants on the other side of your trades are also using AI — many of them with better data, more compute, lower latency, and teams of quants who do nothing else. Retail AI tools don’t close that gap. They never will.

What they can do, in the right hands, is make you more consistent. More disciplined. Better at identifying the setups that actually fit your process and filtering out the ones that don’t. That’s real, and for the kind of systematic swing and position trading where I do my best work, it genuinely matters.

But it only works if you show up as the trader the system needs you to be. Not the trader who sees an alert and goes straight to the order ticket. Not the trader who blames the bot when a setup doesn’t follow through. The trader who built the rules understands why they exist and has enough self-awareness to know when they’re about to break them.

I built the tools. I still have to do that work.

So do you.

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*Jinlu Wang is AI Editorial Strategist with Ubiq Broadcasting Corp, builds automated trading systems and web applications for financial markets. She runs Harp’s Trading, a paid investment research and trading community, and publishes institutional-style research covering AI infrastructure, energy, and commodity-linked technology themes.*

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San Francisco:

Artificial intelligence investment surged to a record $211 billion in 2025, nearly doubling the $114 billion deployed in 2024, according to a new report released by HumanX in partnership with Crunchbase. The figure now represents roughly half of all global venture capital, underscoring AI’s dominance in the technology investment landscape.

The AI Funding Report 2025 signals a structural shift in investor behaviour—from speculative experimentation to what analysts describe as a “disciplined march to value,” with capital increasingly directed toward infrastructure and enterprise-grade applications.

At the center of this transformation is the San Francisco Bay Area, which the report identifies as the world’s “global control center” for artificial intelligence. The region attracted approximately $126 billion—60% of total global AI funding—while an overwhelming 81% of all startup capital within the Bay Area flowed into AI ventures.

Megadeals and Market Maturity

Large-scale funding rounds dominated the market. Deals exceeding $100 million accounted for $163 billion, or 77% of total AI investment, reflecting a concentration of capital among fewer, high-confidence bets.

Meanwhile, companies developing foundation models—such as OpenAI and Anthropic—saw funding jump 180% to $87 billion, reinforcing their position at the core of the AI ecosystem.

However, investment is no longer limited to model development. Nearly 59% of total funding flowed into the broader AI stack, including infrastructure (19%), deep tech and robotics (11%), and sector-specific applications in healthcare and security (15%).

Diversity Gains Ground

The report also highlights growing momentum among female-founded companies. In North America and Europe, 47% of AI funding—equivalent to $84.7 billion—went to startups with at least one female founder, signalling incremental progress in a historically male-dominated sector.

IPO Wave on the Horizon

Using predictive analytics, Crunchbase forecasts a significant wave of exits in 2026. Of the 138 private companies scheduled to appear at this year’s HumanX summit, 27 are classified as “probable or very likely” IPO candidates, while another 30 are considered strong acquisition targets.

“Every AI cycle brings speculation about bubbles, but the data tells a more nuanced story,” said Stefan Weitz, co-founder and CEO of HumanX. “Capital is increasingly flowing toward companies solving complex, high-value problems with long-term durability.”

Jager McConnell, CEO of Crunchbase, added that the market is entering a more disciplined phase. “Investors are no longer funding anything labelled AI. But our data suggests many companies in this ecosystem are poised for significant growth rounds—and potentially public listings—as early as 2026.”

HumanX 2026: A Launchpad for AI Leaders

More than 130 companies are set to present at HumanX in San Francisco this year, collectively raising over $72 billion since 2018. Featured participants include industry heavyweights such as Databricks, Cerebras Systems, and CoreWeave, alongside innovators like Runway, Synthesia, Cohere, and Inflection AI.

Also taking the stage are high-profile technology firms, including Figma, Chime, and Replit, reflecting the growing convergence between AI and mainstream digital platforms.

A Market Still in Early Innings

Despite record-breaking investment levels, industry leaders caution that the AI boom is far from maturity.

“We’re only in the first inning of the AI game,” McConnell said, pointing to the accelerating pace of innovation and the increasing role of predictive intelligence in shaping investment decisions.

The full report is available via HumanX.

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This summit will bring together leaders from the cultural sector, government, the technology sector, academia and civil society. They will  explore the interactions between AI and culture and their policy implications, connect the cultural sector and Canada’s artificial intelligence research and business ecosystem. It will  demonstrate opportunities for the cultural sector to adopt responsible Canadian AI solutions and explore the AI talent, skills and training needs for the cultural sector.

“AI is deeply changing the way we create, deliver and consume culture, and Canada needs a national approach on artificial intelligence and culture that drives responsible and productive AI adoption, facilitates innovation and competition, protects human creativity, and ensures cultural and economic sovereignty. The National Summit on AI and Culture is an important step in this vital work, keeping a responsible and human-centred approach along the way.” Said  Marc Miller, Minister of Canadian Identity and Culture and Minister responsible for Official Languages.

A recent 30-day national sprint to inform a new National AI Strategy included input from an AI Strategy Task Force and an open consultation that gathered over 11,000 submissions from the public. In 2026, the government will launch a renewed AI strategy to maintain Canada’s AI advantage and unleash its potential for economic growth and adoption.

“Banff Centre is proud to host this vital gathering with the Government of Canada. As an institution, based in an extraordinary natural setting, that is dedicated to creative problem solving in arts and leadership, we’re an ideal setting for the deep reflection this work requires.”said Chris Lorway, President and Chief Executive Officer, Banff Centre for Arts and Creativity.

Banff Centre, founded in 1933, is a post-secondary institution focused on artistic and leadership development. The Centre strives to encourage people to unleash their creative potential and realize their unique contributions and to build an innovative, inspiring future through education, performances and public outreach. It is a world leader in the arts, culture and creative decision making across dozens of disciplines, from the fine arts to Indigenous wise practices.

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“Most B2B companies already know who they want to sell to. The challenge is executing GTM consistently at scale,” said Jin-Seong Kim, CEO of DeepSales. “We built DeepSales as a solution that RevOps teams can operationalize immediately—an AI BDR that runs playbooks, not a platform that needs to be orchestrated.”

The recognition was presented at the Global Business Innovation Summit, a CES-adjacent event that brings together policymakers, investors, and business leaders to accelerate innovation through trade and technology. The certificate was issued by Steven Horsford, whose district plays a central role in Southern Nevada’s economic development initiatives.

DeepSales was recognized for building an AI BDR solution that translates go-to-market strategy into repeatable revenue execution inside RevOps environments. Rather than operating as a marketplace or multi-sided platform, the company positions its product as a deployable solution that functions as an autonomous BDR layer—designed to run defined GTM motions end to end.

The AI BDR agent supports core RevOps workflows, including ICP definition, account prioritization, outbound decision-making, and pipeline creation, allowing B2B teams to scale GTM execution without adding sales headcount or fragmenting their tool stack.

During CES week, DeepSales worked closely with U.S. mid-market companies and export-oriented businesses seeking to improve outbound efficiency and predictability amid tightening sales budgets and rising expectations around revenue efficiency.

DeepSales’ recognition reflects a broader shift in B2B software toward execution-focused AI solutions embedded in RevOps, where autonomous agents are increasingly viewed as core revenue infrastructure rather than experimental productivity tools.

As CES continues to evolve into a convergence point for technology, capital, and policy, DeepSales’ inclusion highlights growing institutional interest in AI-driven GTM execution as a foundational layer of modern B2B growth.

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At CES 2026, LG demonstrated how its AI systems sense conditions, process data, and execute actions in real environments – what the company describes as a Sense-Think-Act operating model. Rather than limiting AI to software interfaces, LG is applying it across robots, appliances, vehicles and TVs to enable adaptive, context-aware operations.

Greeting visitors at the booth entrance, LG introduced the “In Tune” Monument – a large-scale installation composed of 38 LG OLED evo W6 TVs (Wallpaper TV), each measuring just 9 millimetres class thin. The screens were arranged to appear as if floating in midair, seamlessly synchronized to create one continuous visual canvas. When the individual screens united into a single, dynamic image, they conveyed  the message of “Innovation in tune with you.”

 Designed as a home-specialized AI robot with safety and reliability at its core, LG CLOiD operates seamlessly within real living environments. The demonstrations highlighted LG CLOiD’s physical manipulation capabilities, including finger-level dexterity and full-arm movement, as well as its integration with LG’s AI Home Platform, ThinQ. The zone also featured the enhanced ThinQ UP and ThinQ Care services. The coordinated, proactive care that these solutions provide helps transform the home into an enriched and empathetic environment that understands and responds to residents’ evolving needs.

The LG OLED evo Wallpaper TV delivered the full intensity of LG OLED TV’s new Hyper Radiant Colour Technology picture quality while highlighting its ultra-slim Wallpaper Design – made possible through True Wireless connectivity. The zone also introduces LG Gallery+, a webOS-based service that displays curated art and imagery based on user preferences.

LG also presented its new flagship OLED evo G6 and Micro RGB evo TVs powered by the α (Alpha) 11 AI Processor Gen3, which featured Dual Super Upscaling to simultaneously process two types of AI upscaling. This enhances sharpness while delivering natural and balanced images that offer the highest level of clarity and immersion. The zone also featured the 136-inch AM Micro LED TV AI, which served as a platform for large-format premium content.

The LG SIGNATURE refrigerator incorporates LLM-based conversational AI for natural-language interaction and tailored suggestions.¹ The LG SIGNATURE Smart InstaView refrigerator streamlines food management through ThinQ Food, which uses an internal camera to identify stored items, suggest recipes and recommend substitutions based on the items inside.

The LG SIGNATURE Oven Range offers functions such as Gourmet AI, which used an AI camera inside the oven to identify more than 80 dishes and automatically select the ideal cooking settings. The AI Browning feature monitors bread as it bakes and sends a notification through the ThinQ app when it reaches the preset level of doneness.

LG has also created a dedicated exhibition space titled In Tune for Everyone, which connects the company’s ESG vision of Better Life for All. The exhibit features the Comfort Kit, designed to help people of all ages and abilities use LG appliances with greater ease, along with Easy-to-Read Books, developed to help slow learners and children with developmental disabilities better understand appliance function and usage.

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Opening the session, Lokwon Kim, Founder and CEO of DEEPX, emphasized that Physical AI is no longer a futuristic concept but an immediate reality.

“The center of gravity in AI is shifting from the cloud to the physical world,” Kim stated. “To accelerate this transition, we must achieve a seamless integration of hardware and software that can withstand real-world constraints.”

The panel featured senior executives from Hyundai Motor Group Robotics Lab, Baidu, Edge AI Foundation, Ultralytics, and Wind River, representing a diverse ecosystem spanning robotics, edge platforms, open-source frameworks, and mission-critical systems.

Themed “The Unstoppable Rise of Physical AI,” the panel brought together industry heavyweights actively deploying mass-production AI. Discussions centered on the practical challenges and critical requirements for operating AI reliably in real-world physical environments.

Dongjin Hyun, Executive Director at Hyundai Motor Group Robotics Lab, remarked: “Robotics is becoming core infrastructure for both society and industry. Physical AI enables robots to make autonomous decisions on-device, even in unstable network environments. Through our collaboration with DEEPX, we have validated on-device AI capabilities in real-world conditions and plan to deploy these solutions in our next-generation robots and security systems starting in 2026.”

Addressing the software ecosystem, Raine Hua, Global Ecosystem AI Manager at Baidu, noted: “In the era of Physical AI, success isn’t just about model performance; it’s about consistency. Models must run reliably anywhere. Our collaboration with DEEPX offers a streamlined path to deploy models at the edge without the need for repeated redesigns.”

Pete Bernard, CEO of Edge AI Foundation, highlighted the structural limitations of current architectures: “Data-center-centric AI faces clear constraints regarding cost, power, and latency. DEEPX is establishing Physical AI as a foundational infrastructure by combining tangible performance with robust ecosystem development.”

Sandeep Modhvadia, CPO at Wind River, offered a perspective on mission-critical systems: “For AI to serve as infrastructure in defense, aerospace, and industrial sectors, security, predictability, and long-term stability are non-negotiable. DEEPX’s solutions enable trusted systems that operate reliably in the field.”

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At the center of Aiper’s CES presence are CES 2026 Innovation Award Honorees the Scuba V3 Ultra and the EcoSurfer Senti, marking the company’s fourth consecutive year recognized by the Consumer Technology Association (CTA) for excellence in innovation, design and engineering. With innovations ranging from cognitive AI Powered robotic pool cleaners, autonomous pool robots, advanced water quality management, and smart irrigation systems, Aiper is shaping the future of carefree, sustainable outdoor living.

Aiper’s cognitive AI technology powers the Scuba V3 Series – Scuba V3, Scuba V3 Pro, and Scuba V3 Ultra – introducing a new generation of robotic pool cleaners that provide a truly effortless cleaning experience. By leveraging advanced cognitive AI Navium mode and AI Patrol Cleaning, the Scuba V3 Series can automatically optimize the cleaning path, frequency, and suction power by analyzing each pool’s unique environment – including size, shape, cleaning history, and even weather conditions. The result is a true set-it-and-forget-it experience, allowing pool owners to enjoy crystal-clear water with minimal effort.

Aiper is the No.1 smart robotic pool cleaner brand and a leader in reliable, intelligent solutions for the smart yard ecosystem. From crystal-clear pools to balanced water and smarter irrigation, Aiper’s ecosystem simplifies care across your entire yard. Trusted by more than 3 million users worldwide, Aiper is available in over 7,000 stores across 50+ countries and regions. Renowned for excellence, Aiper products have earned prestigious honors including the Red Dot Design Award, the iF Design Award, and editorial awards from USA Today and TWICE, and have been recognized as a CES Innovation Awards Honoree from 2023 through 2026. Driven by smart automation, adaptive AI, and sustainable design, Aiper delivers a truly carefree experience, giving you more time to enjoy life’s best moments.

Separate from the Scuba V3 Series of robotic pool cleaners, Aiper’s most advanced innovation yet, the Autonomous Pool Robot, takes pool care to the next level by eliminating manual tasks. Featuring onshore self-cleaning and self-recharging, this robot delivers the ultimate hands-free experience. Powered by the world’s first Unified Multi-Sensor AI Perception System, the robot seamlessly integrates multiple sensors and sonars for 360-degree awareness, ensuring a zero-blind-spot cleaning experience throughout the pool and across the water surface. The system also enables precise returns to its charging station, allowing the robot to integrate seamlessly into a smart-powered home environment. Debuting at CES, this future-ready pool robot is poised to set a new standard in fully autonomous pool maintenance, with pricing and availability to be announced.

Aiper is transforming lawn care with the IrriSense 2, the world’s first 4-in-1 multi-zone smart irrigation system. By combining a controller, sprinklers, electric valve, and nutrient feeder into one compact solution, the IrriSense 2 offers unparalleled efficiency and sustainability. Through the app, users can easily schedule watering, create up to 10 customized zone mappings, and monitor water usage data in real time.

Its Weather-Sense Response system automatically adjusts watering schedules based on environmental conditions, conserving up to 40% of water while providing precise, plant-specific multi-zone care. EvenRain technology delivers gentle, rainfall-style watering across yards up to 445 square meters, while SoilPulse, a microbial organic soil amendment, is designed to improve soil health, moisture retention, and root development – transforming how homeowners approach sustainable lawn care.

Aiper is redefining pool water management with the EcoSurfer Senti, a solar-powered skimmer that combines AI-driven surface cleaning with continuous water quality monitoring, offering a proactive and sustainable solution for maintaining clear, healthy pool water. The award-winning EcoSurfer Senti is the flagship product in this category, delivering cutting-edge, sustainable pool maintenance, and will be available in Spring 2026 with an MSRP of $799.

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 Since its release, KaniTTS has been downloaded over 15,000 times on Hugging Face, drawing international attention for its combination of speed, expressivity, and accessibility. The model is capable of generating 15 seconds of natural speech in just 1 second on a consumer-grade NVIDIA RTX 5080 GPU, enabling true real-time performance without cloud-scale infrastructure.

 KaniTTS delivers real-time, human-like voice generation that is up to three times faster and up to ten times cheaper than current state-of-the-art commercial TTS services such as ElevenLabs, OpenAI (TTS), and Google (Gemini TTS).

KaniTTS matches the performance of leading commercial voice models from OpenAI, Google, Microsoft, and Hume AI, while remaining fully open and free under the Apache 2.0 license. The base model currently supports eight languages: English, German, Korean, Arabic, Chinese, Spanish, Kyrgyz and Japanese.

Built on its open-source foundation, KaniTTS has already been adopted by external developers, who have used the model to create text-to-speech systems for Vietnamese, Urdu, Creole, and Hausa (Africa). This demonstrates how an open, high-performance voice model can empower communities working with underrepresented languages to develop their own AI solutions. By lowering technical and cost barriers, KaniTTS enables local teams to build speech technologies tailored to their languages and markets — expanding access to AI far beyond traditionally supported languages.

Alongside KaniTTS, the team also presented Kyrgyz Whisper, an open-source automatic speech recognition model fine-tuned on OpenAI’s Whisper. Trained on approximately 2,000 hours of Kyrgyz audio, the model achieved a dramatic improvement in recognition quality, reducing Word Error Rate for Kyrgyz from nearly 100% to 0.2, addressing one of the most persistent gaps in global AI -support for underrepresented languages.

The High Technology Park of the Kyrgyz Republic (HTP) is a government-backed legal and tax framework created for technology companies and startups working for international markets.  Companies from anywhere in the world can register in the Kyrgyz Republic, become HTP residents, and operate under a preferential tax regime designed specifically for the tech sector.

The Kyrgyz Republic has become one of the fastest-growing technology exporters in its region. In 2024, Kyrgyz IT specialists provided services to clients in more than 63 countries, generating $130 million in IT exports. The United States ranked first, accounting for around 40% of total exports, with an export volume exceeding $50 million, including cooperation with companies in Silicon Valley. Over the past five years, the country’s IT exports have grown 45-fold, marking a structural shift toward high-value digital services and proprietary technology development. HTP brings together a network of 500 software development companies and 3,000 developers, ready to meet the full range of client needs.

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As CES 2026 highlights a shift from single-purpose devices to integrated, environment-based wellness experiences, CERAGEM’s awards reflect a new generation of technologies designed to support physical recovery, mental relaxation, sleep quality, and personalized self-care—quietly, continuously, and as part of everyday living.

Founded in South Korea, CERAGEM is a global wellness technology company developing AI-powered, non-invasive therapeutic and wellness systems for the home. With operations in more than 70 countries and decades of experience in wellness innovation, CERAGEM continues to advance technologies that integrate healthcare insight, lifestyle design, and intelligent systems—supporting everyday wellbeing through human-centered, long-term solutions.

CERAGEM’s CES 2026 Innovation Awards recognize nine products that together demonstrate how AI-driven wellness technology is evolving from isolated functions into coordinated systems supporting the body and mind across daily routines.

Designed for time-efficient, immersive wellness, the MASTER AI Multi-Therapy Pod integrates sound therapy, aromatics, oxygen support, lighting, and physical therapy into a single AI-orchestrated experience – addressing the growing need for holistic recovery within modern, time-constrained lifestyles.

“CES 2026 reflects a fundamental shift in how wellness technologies are designed and experienced,” said Brian Yang, CEO of CERAGEM International Inc. “Wellness is no longer something people engage with occasionally—it is becoming part of everyday routines in modern homes. Our award-winning technologies are built around the idea that AI should work quietly in the background, supporting recovery, relaxation, and balance as a natural part of daily life.”

Building on CERAGEM’s long-standing expertise in spinal thermal therapy, these systems combine proven therapeutic mechanisms with refined ergonomics, enabling consistent, at-home physical recovery that fits naturally into residential environments.

Focused on relaxation, focus, and sleep quality, these products employ non-invasive, sensory-based approaches aligned with the industry’s move toward preventive, lifestyle-oriented mental wellness rather than episodic intervention.

 Utilizing real-time physiological data, these solutions dynamically adapt wellness sessions to support emotional balance and cognitive focus—reflecting a broader shift toward responsive, personalized wellness systems.

 Extending wellness into personalized aesthetic care, CERAGEM’s award-winning beauty technologies use intelligent analysis to tailor treatments to individual skin conditions and daily needs, reinforcing the convergence of health, beauty, and technology.

Together, these award-winning innovations form a cohesive ecosystem that positions the home as the center of preventive, restorative, and personalized wellness.

CES 2026 signals a turning point for wellness technology—moving beyond novelty toward solutions designed for long-term, everyday use. CERAGEM’s award-winning products reflect this evolution through an emphasis on trust, repeatability, and seamless integration into daily routines, rather than short-term experimentation.

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