Technology

E-commerce keeps growing and with it, the number of products moving through warehouses every day. Items vary more than ever — different shapes, seasonal packaging, limited editions and constantly updated designs. At the same time, many logistics centers are dealing with labour shortages and rising pressure to automate.

But today’s image-recognition AI isn’t built for this level of change. Most systems rely on deep-learning models that need to be adjusted or retrained whenever new products appear. Every update — whether it’s a new item or a packaging change — adds extra time, energy use and operational cost. And for warehouses handling huge product catalogs, these retraining cycles can slow everything down.

KIOXIA, a company known for its memory and storage technologies, is working on a different approach. In a new collaboration with Tsubakimoto Chain and EAGLYS, the team has developed an AI-based image recognition system that is designed to adapt more easily as product lines grow and shift. The idea is to help logistics sites automatically identify items moving through their workflows without constantly reworking the core AI model.

At the center of the system is KIOXIA’s AiSAQ software paired with its Memory-Centric AI technology. Instead of retraining the model each time new products appear, the system stores new product data — images, labels and feature information — directly in high-capacity storage. This allows warehouses to add new items quickly without altering the original AI model.

Because storing more data can lead to longer search times, the system also indexes the stored product information and transfers the index into SSD storage. This makes it easier for the AI to retrieve relevant features fast, using a Retrieval-Augmented Generation–style method adapted for image recognition.

The collaboration will be showcased at the 2025 International Robot Exhibition in Tokyo. Visitors will see the system classify items in real time as they move along a conveyor, drawing on stored product features to identify them instantly. The demonstration aims to illustrate how logistics sites can handle continuously changing inventories with greater accuracy and reduced friction.

Overall, as logistics networks become increasingly busy and product lines evolve faster than ever, this memory-driven approach provides a practical way to keep automation adaptable and less fragile.

When British-Canadian cognitive psychologist and computer scientist Geoffrey Hinton joked that his ex-girlfriend once used ChatGPT to help her break up with him, he wasn’t exaggerating.  The father of deep learning was pointing to something stranger: how machines built to mimic language have begun to mimic thought — and how even their creators no longer agree on what that means.

In that one quip — part humor, part unease — Hinton captured the paradox at the center of the world’s most important scientific divide. Artificial intelligence has moved beyond code and circuits into the realm of psychology, economics and even philosophy. Yet among those who know it best, the question has turned unexpectedly existential: what, if anything, do large language models truly understand?  

Across the world’s AI labs, that question has split the community into two camps — believers and skeptics, prophets and heretics. One side sees systems like ChatGPT, Claude, and Gemini as the dawn of a new cognitive age. The other insists they’re clever parrots with no grasp of meaning, destined to plateau as soon as the data runs out. Between them stands a trillion-dollar industry built on both conviction and uncertainty.

Hinton, who spent a decade at Google refining the very neural networks that now power generative AI, has lately sounded like a man haunted by his own invention. Speaking to Scott Pelley on the CBS 60 Minutes interview aired October 8, 2023, Hinton said, “I think we're moving into a period when for the first time ever we may have things more intelligent than us.” . He said it not with triumph, but with visible worry.

Yoshua Bengio, his longtime collaborator, sees it differently. Speaking at the All In conference in Montreal, he told TIME that future AI systems "will have stronger and stronger reasoning abilities, more and more knowledge," while cautioning about ensuring they "act according to our norms". And then there’s Gary Marcus, the cognitive scientist and enduring critic, who dismisses the hype outright: “These systems don’t understand the world. They just predict the next word.”    

It’s a rare moment in science when three pioneers of the same field disagree so completely — not about ethics or funding, but about the very nature of progress. And yet that disagreement now shapes how the future of AI will unfold.

In the span of just two years, large language models have gone from research curiosities to corporate cornerstones. Banks use them to summarize reports. Lawyers draft contracts with them. Pharmaceutical firms explore protein structures through them. Silicon Valley is betting that scaling these models — training them on ever-larger datasets with ever-denser computers — will eventually yield something approaching reasoning, maybe even intelligence.

It’s the “bigger is smarter” philosophy, and it has worked — so far. OpenAI’s GPT-4, Anthropic’s Claude, and Google’s Gemini have grown exponentially in capability  . They can write code, explain math, outline business plans, even simulate empathy. For most users, the line between prediction and understanding has already blurred beyond meaning. Kelvin So, who is now conducting AI research in PolyU SPEED, commented  , “AI scientists today are inclined to believe we have learnt a bitter lesson in the advancement from the traditional AI to the current LLM paradigm. That said, scaling law, instead of human-crafted complicated rules, is the ultimate law governing AI.”  

But inside the labs, cracks are showing. Scaling models have become staggeringly expensive, and the returns are diminishing. A growing number of researchers suspect that raw scale alone cannot unlock true comprehension — that these systems are learning syntax, not semantics; imitation, not insight.  

That belief fuels a quiet counter-revolution. Instead of simply piling on data and GPUs, some researchers are pursuing hybrid intelligence   — systems that combine statistical learning with symbolic reasoning, causal inference, or embodied interaction with the physical world. The idea is that intelligence requires grounding — an understanding of cause, consequence, and context that no amount of text prediction can supply.

Yet the results speak for themselves.  In practice, language models are already transforming industries faster than regulation can keep up. Marketing departments run on them. Customer support, logistics and finance teams depend on them. Even scientists now use them to generate hypotheses, debug code and summarize literature. For every cautionary voice, there are a dozen entrepreneurs who see this technology as a force reshaping every industry. That gap — between what these models actually are and what we hope they might become — defines this moment. It’s a time of awe and unease, where progress races ahead even as understanding lags behind.  

Part of the confusion stems from how these systems work. A large language model doesn’t store facts like a database. It predicts what word is most likely to come next in a sequence, based on patterns in vast amounts of text. Behind this seemingly simple prediction mechanism lies a sophisticated architecture. The tokenizer is one of the key innovations behind modern language models. It takes text and chops it into smaller, manageable pieces the AI can understand. These pieces are then turned into numbers, giving the model a way to “read” human language. By doing this, the system can spot context and relationships between words — the building blocks of comprehension.  

Inside the model, mechanisms such as multi-head attention enable the system to examine many aspects of information simultaneously, much as a human reader might track several storylines at once.

Reinforcement learning, pioneered by Richard Sutton, a professor of computing science at the University of Alberta, and Andrew Barto, Professor Emeritus at the University of Massachusetts, mimics human trial-and-error learning. The AI develops “value functions” that predict the long-term rewards of its actions.  Together, these technologies enable machines to recognize patterns, make predictions and generate text that feels strikingly human — yet beneath this technical progress lies the very divide that cuts to the heart of how intelligence itself is defined.

This placement works well because it elaborates on the technical foundations after the article introduces the basic concept of how language models work, and before it transitions to discussing the emergent behaviors and the “black box problem.”

Yet at scale, that simple process begins to yield emergent behavior — reasoning, problem-solving, even flashes of creativity that surprise their creators. The result is something that looks, sounds and increasingly acts intelligent — even if no one can explain exactly why.

That opacity worries not just philosophers, but engineers. The “black box problem” — our inability to interpret how neural networks make decisions — has turned into a scientific and safety concern. If we can’t explain a model’s reasoning, can we trust it in critical systems like healthcare or defense?

Companies like Anthropic are trying to address that with “constitutional AI,” embedding human-written principles into model training to guide behavior. Others, like OpenAI, are experimenting with internal oversight teams and adversarial testing to catch dangerous or misleading outputs. But no approach yet offers real transparency. We’re effectively steering a ship whose navigation system we don’t fully understand.  “We need governance frameworks that evolve as quickly as AI itself,” says Felix Cheung, Founding Chairman of RegTech Association of Hong Kong (RTAHK). “Technical safeguards alone aren't enough — transparent monitoring and clear accountability must become industry standards.”

Meanwhile, the commercial race is accelerating. Venture capital is flowing into AI startups at record speed. OpenAI’s valuation reportedly exceeds US$150 billion; Anthropic, backed by Amazon and Google, isn’t far behind.   The bet is simple: that generative AI will become as indispensable to modern life as the internet itself.

And yet, not everyone is buying into that vision. The open-source movement — championed by players like Meta’s Llama, Mistral in France, and a fast-growing constellation of independent labs — argues that democratizing access is the only way to ensure both innovation and accountability.   If powerful AI remains locked behind corporate walls, they warn, progress will narrow to the priorities of a few firms.

But openness cuts both ways. Publicly available models are harder to police, and their misuse — from disinformation to deepfakes — grows as easily as innovation does. Regulators are scrambling to balance risk and reward. The European Union’s AI Act is the world’s most comprehensive attempt at governance, but even it struggles to define where to draw the line between creativity and control.

This isn’t just a scientific argument anymore. It’s a geopolitical one. The United States, China, and Europe are each pursuing distinct AI strategies: Washington betting on private-sector dominance, Beijing on state-led scaling, Brussels on regulation and ethics. Behind the headlines, compute power is becoming a form of soft power. Whoever controls access to the chips, data, and infrastructure that fuel AI will control much of the digital economy.  

That reality is forcing some uncomfortable math. Training frontier models already consumes energy on the scale of small nations. Data centers now rise next to hydroelectric dams and nuclear plants. Efficiency — once a technical concern — has become an economic and environmental one. As demand grows, so does the incentive to build smaller, smarter, more efficient systems. The industry’s next leap may not come from scale at all, but from constraint.

For all the noise, one truth keeps resurfacing: large language models are tools, not oracles. Their intelligence — if we can call it that — is borrowed from ours. They are trained on human text, human logic, human error. Every time a model surprises us with insight, it is, in a sense, holding up a mirror to collective intelligence.

That’s what makes this schism so fascinating. It’s not really about machines. It’s about what we believe intelligence is — pattern or principle, simulation or soul. For believers like Bengio, intelligence may simply be prediction done right. For critics like Marcus, that’s a category mistake: true understanding requires grounding in the real world, something no model trained on text can ever achieve.

The public, meanwhile, is less interested in metaphysics. To most users, these systems work — and that’s enough. They write emails, plan trips, debug spreadsheets, summarize meetings. Whether they “understand” or not feels academic. But for the scientists, that distinction remains critical, because it determines where AI might ultimately lead.

Even inside the companies building them, that tension shows OpenAI’s Sam Altman has hinted that scaling can’t continue forever. At some point, new architectures — possibly combining logic, memory, or embodied data — will be needed. DeepMind’s Demis Hassabis says something similar: intelligence, he argues, will come not just from prediction, but from interaction with the world.  

It’s possible both are right. The future of AI may belong to hybrid systems — part statistical, part symbolic — that can reason across multiple modes of information: text, image, sound, action. The line between model and agent is already blurring, as LLMs gain the ability to browse the web, run code, and call external tools. The next generation won’t just answer questions; it will perform tasks.

For startups, the opportunity — and the risk — lies in that transition. The most valuable companies in this new era may not be those that build the biggest models, but those that build useful ones: specialized systems tuned for medicine, law, logistics, or finance, where reliability matters more than raw capability. The winners will understand that scale is a means, not an end.

And for society, the challenge is to decide what kind of intelligence we want to live with. If we treat these models as collaborators — imperfect, explainable, constrained — they could amplify human potential on a scale unseen since the printing press. If we chase the illusion of autonomy, they could just as easily entrench bias, confusion, and dependency.

The debate over large language models will not end in a lab. It will play out in courts, classrooms, boardrooms, and living rooms — anywhere humans and machines learn to share the same cognitive space. Whether we call that cooperation or competition will depend on how we design, deploy, and, ultimately, define these tools.

Perhaps Hinton’s offhand remark about being psychoanalyzed by his own creation wasn’t just a joke. It was an omen. AI is no longer something we use; it’s something we’re reflected in. Every model trained on our words becomes a record of who we are — our reasoning, our prejudices, our brilliance, our contradictions. The schism among scientists mirrors the one within ourselves: fascination colliding with fear, ambition tempered by doubt.

In the end, the question isn’t whether LLMs are the future. It’s whether we are ready for a future built in their image.

A new collaboration between ChainGPT, an AI company specialising in blockchain development tools and Secret Network, a privacy-focused blockchain platform, is redefining how developers can safely build smart contracts with artificial intelligence. Together, they’ve achieved a major industry first: an AI model trained exclusively to write and audit Solidity code is now running inside a Trusted Execution Environment (TEE). For the blockchain ecosystem, this marks a turning point in how AI, privacy and on-chain development can work together.

For years, smart-contract developers have faced a trade-off. AI assistants could speed up coding and security reviews, but only if developers uploaded their most sensitive source code to external servers. That meant exposing intellectual property, confidential logic and even potential vulnerabilities. In an industry where trust is everything, this risk held many teams back from using AI at all.

ChainGPT’s Solidity-LLM aims to solve that problem. It is a specialised large language model trained on over 650,000 curated Solidity contracts, giving it a deep understanding of how real smart contracts are structured, optimised and secured. And now, by running inside SecretVM, the Confidential Virtual Machine that powers Secret Network’s encrypted compute layer, the model can assist developers without ever revealing their code to outside parties.

“Confidential computing is no longer an abstract concept,” said Luke Bowman, COO of the Secret Network Foundation. “We've shown that you can run a complex AI model, purpose-built for Solidity, inside a fully encrypted environment and that every inference can be verified on-chain. This is a real milestone for both privacy and decentralised infrastructure”.

SecretVM makes this workflow possible by using hardware-backed encryption to protect all data while computations take place. Developers don’t interact with the underlying hardware or cryptography. Instead, they simply work inside a private, sealed environment where their code stays invisible to everyone except them—even node operators. For the first time, developers can generate, test and analyse smart contracts with AI while keeping every detail confidential.

This shift opens new possibilities for the broader blockchain community. Developers gain a private coding partner that can streamline contract logic or catch vulnerabilities without risking leaks. Auditors can rely on AI-assisted analysis while keeping sensitive audit material protected. Enterprises working in finance, healthcare or governance finally have a path to adopt AI-driven blockchain automation without raising compliance concerns. Even decentralised organisations can run smart-contract agents that make decisions privately, without exposing internal logic on a public chain.

The system also supports secure model training and fine-tuning on encrypted datasets. This enables collaborative AI development without forcing anyone to share raw data—a meaningful step toward decentralised and privacy-preserving AI at scale.

By combining specialised AI with confidential computing, ChainGPT and Secret Network are shifting the trust model of on-chain development. Instead of relying on centralised cloud AI services, developers now have a verifiable, encrypted environment where they keep full control of their code, their data and their workflow. It’s a practical solution to one of blockchain’s biggest challenges: using powerful AI tools without sacrificing privacy.

As the technology evolves, the roadmap includes confidential model fine-tuning, multi-agent AI systems and cross-chain use cases. But the core advancement is already clear: developers now have a way to use AI for smart contract development that is fast, private and verifiable—without compromising the security standards that decentralised systems rely on.

Managing vegetation around power lines has long been one of the biggest operational challenges for utilities. A single tree growing too close to electrical infrastructure can trigger outages or, in the worst cases, spark fires. With vast service territories, shifting weather patterns and limited visibility into changing landscape conditions, utilities often rely on inspections and broad wildfire-risk maps that provide only partial insight into where the most serious threats actually are.

Overstory, a company specializing in AI-powered vegetation intelligence, addresses this visibility gap with a platform that uses high-resolution satellite imagery and machine-learning models to interpret vegetation conditions in detail.Instead of assessing risk by region, terrain type or outdated maps, the system evaluates conditions tree by tree. This helps utilities identify precisely where hazards exist and which areas demand immediate intervention—critical in regions where small variations in vegetation density, fuel type or moisture levels can influence how quickly a spark might spread.

At the core of this technology is Overstory’s proprietary Fuel Detection Model, designed to identify vegetation most likely to ignite or accelerate wildfire spread. Unlike broad, publicly available fire-risk maps, the model analyzes the specific fuel conditions surrounding electrical infrastructure. By pinpointing exact locations where certain fuel types or densities create elevated risk, utilities can plan targeted wildfire-mitigation work rather than relying on sweeping, resource-heavy maintenance cycles.

This data-driven approach is reshaping how utilities structure vegetation-management programs. Having visibility into where risks are concentrated—and which trees or areas pose the highest threat—allows teams to prioritize work based on measurable evidence. For many utilities, this shift supports more efficient crew deployment, reduces unnecessary trims and builds clearer justification for preventive action. It also offers a path to strengthening grid reliability without expanding operational budgets.

Overstory’s recent US$43 million Series B funding round, led by Blume Equity with support from Energy Impact Partners and existing investors, reflects growing interest in AI tools that translate environmental data into actionable wildfire-prevention intelligence. The investment will support further development of Overstory’s risk models and help expand access to its vegetation-intelligence platform.

Yet the company’s focus remains consistent: giving utilities sharper, real-time visibility into the landscapes they manage. By converting satellite observations into clear and actionable insights, Overstory’s AI system provides a more informed foundation for decisions that impact grid safety and community resilience. In an environment where a single missed hazard can have far-reaching consequences, early and precise detection has become an essential tool for preventing wildfires before they start.

Speech and language delays are common, yet access to therapy remains limited. In Taiwan, only about 2,200 licensed speech-language pathologists serve hundreds of thousands of children who need support—especially those with autism spectrum disorders or significant communication challenges. As a result, many children miss crucial periods of language development simply because help isn’t available soon enough.

MIRDC’s new AI-powered interactive speech therapy system aims to close that gap. Instead of focusing solely on articulation, it targets a wider range of language skills that many children struggle with: oral expression, comprehension, sentence building and conversational ability. This makes it a more complete tool for childhood speech and language development.

The system combines game-based learning, AI-driven guidance and automated language assessment into one platform that can be used both in clinics and at home. This integrated design helps children practice more consistently, providing therapists and parents with clearer insight into their progress.

The interactive game modules are built around clinically validated therapy methods. Imitation exercises, picture cards, storybooks and conversational prompts are turned into structured game levels, each aligned with a specific developmental goal. This step-by-step approach helps children move from simple naming tasks to more complex comprehension and response skills, all within a sequenced curriculum.

A key differentiator is the system’s real-time AI speech interpretation. As the child talks, the AI analyzes the response and generates tailored therapeutic cues—such as imitation, modeling, expansion or extension—based on the conversation. These are the same strategies used by speech-language pathologists, but now children can access them continuously, supporting more effective at-home practice and reducing long gaps between sessions.

After each session, the system automatically conducts a data-driven language assessment using 20 objective indicators across semantics, syntax and pragmatics. This provides clinicians and families with measurable, easy-to-understand reports that show how the child is progressing and which skills need more attention—something many traditional tools do not offer.

By offering a personalized, scalable and clinically grounded solution, MIRDC’s AI therapy system helps address the ongoing shortage of speech-language services. It doesn’t replace therapists; instead, it extends their reach, allows for more consistent practice and helps families support their child’s communication at home.

As an added recognition of its impact, the system recently earned two R&D 100 Awards, including the Silver Award for Corporate Social Responsibility. But at its core, the project remains focused on a simple mission: making high-quality speech therapy accessible to every child who needs a voice.

Farms today are under pressure. Fields are getting bigger, workers are harder to find and many jobs still rely on long hours of manual labor. XAG’s new P150 Max agricultural drone is designed for exactly this reality. Instead of replacing farmers, it takes over the heavy, repetitive fieldwork that slows them down, making farm operations more efficient and more precise.

The P150 Max is built around one simple idea: a single machine that can handle multiple farming tasks. Most farm drones focus only on spraying or mapping, but this one is fully modular. With a quick switch of attachments, it can spray crops, spread seeds or fertilizer, map fields or transport supplies. This flexibility helps farmers keep up with changing tasks throughout the day without needing different machines, improving both productivity and cost-efficiency.

A key challenge in agriculture is that fields are rarely smooth or predictable. Tractors can get stuck, smaller drones can’t carry much and some areas—like orchards or hilly plots—are simply hard to reach. The P150 Max fills that gap with an 80-kilogram payload and fast flight speed, letting it cover more ground per trip. Fewer takeoffs mean less downtime and more work completed before weather or daylight cuts operations short.

When it’s time to spray, the drone uses a smart spraying system that allows farmers to adjust droplet size based on the crop’s needs. This matters because precise spraying reduces waste and improves targeting. With an output of up to 46 liters per minute, the drone can serve both large open fields and dense orchards where consistent coverage is traditionally difficult.

The spreading system applies the same logic. Instead of dropping seeds or fertilizer unevenly, the vertical mechanism spreads material smoothly and resists wind drift. This ensures uniform application across irregular or hard-to-reach land—an ongoing challenge for modern farms aiming for higher yield and better resource use.

Another everyday issue for farmers is understanding and surveying the land before working on it. The P150 Max helps here with a built-in mapping tool that covers up to 20 hectares per flight and instantly converts the images into detailed maps. With AI detecting obstacles like trees or irrigation lines, the drone can plan safe and efficient autonomous routes, reducing manual planning time.

Beyond spraying and spreading, the drone can transport tools, produce and farm supplies using a sling attachment. This is particularly helpful after heavy rain, when vehicles cannot easily move across muddy or flooded fields.

Under all these functions is XAG’s upgraded flight control system, which provides centimeter-level accuracy even when network signals are weak. Integrated sensors—including 4D radar and a wide-angle camera—help the drone recognize hazards such as poles and wires. Farmers can manage all operations through the XAG One app or a handheld controller, both of which automatically generate the best route based on field shape and terrain.

Since long field days require long operating hours, the fast-charging battery system can recharge in about seven minutes using a dedicated kit. This supports continuous drone use throughout the day with minimal interruptions.

After years of testing, the XAG P150 Max is essentially an effort to make practical, scalable farm automation more accessible. By combining spraying, spreading, mapping and transport into one heavy-duty platform, it offers a way to ease labor shortages while keeping operations efficient and sustainable. Instead of focusing on one task, the drone aims to take over the time-consuming physical work so farmers can focus on decisions, planning and crop management.

PowerBank Corporation and Smartlink AI, the company behind Orbit AI, are preparing to send a very different kind of satellite into space. Their upcoming mission, scheduled for December 2025, aims to test what they call the world’s first “Orbital Cloud” — a system that moves parts of today’s digital infrastructure off the ground and into orbit. While satellites already handle GPS, TV signals and weather data, this project tries to do something bigger: turn space itself into a platform for computing, artificial intelligence (AI) and secure blockchain-based digital transactions. In essence, it marks the beginning of space-based cloud computing.

To understand why this matters, it is helpful to examine the limitations of our current systems. As AI tools grow more advanced, they require massive data centers that consume enormous amounts of electricity, especially for cooling. These facilities depend on national power grids, face regulatory constraints and are concentrated in just a few regions. Meanwhile, global connectivity still struggles with inequalities, censorship, congestion and geopolitical bottlenecks. The Orbital Cloud is meant to plug these gaps by building a computing and communication layer above Earth — a solar-powered, space-cooled network in Low Earth Orbit (LEO) that no single nation or company fully controls.

Orbit AI’s approach brings together two new systems. The first, called DeStarlink, is a decentralized satellite network designed for global internet-style connectivity and resilient communication. The second, DeStarAI, is a set of AI-focused in-orbit data centers placed directly on satellites, using space’s naturally cold environment instead of the energy-hungry cooling towers used on Earth. When these two ideas merge, the result is a floating digital layer where information can be transmitted, processed and verified without touching terrestrial infrastructure — a key shift in how AI workloads and cloud computing may be handled in the future.

PowerBank enters the picture by supplying the electricity and temperature-control technology needed to keep these satellites running. In space, sunlight is constant and uninterrupted — no clouds, no storms, no nighttime periods where panels lie idle. PowerBank plans to provide high-efficiency solar arrays and adaptive thermal systems that help the satellites manage heat in orbit. This collaboration marks a shift for PowerBank, which is expanding from traditional solar and battery projects into the realm of digital infrastructure, AI energy systems and next-generation satellite technology.

Describing the ambition behind this move, Dr. Richard Lu, CEO of PowerBank, said: “The next frontier of human innovation isn't just in space exploration, it's in building the infrastructure of tomorrow above the Earth”. He pointed to a future market that could surpass US$700 billion, driven by orbital satellites, AI computing in space, blockchain verification and solar-powered data systems. Integrating solar energy with orbital computing, he said, could help create “a globally sovereign, AI-enabled digital layer in space, which is a system that can help power finance, communications and critical infrastructure”.

Orbit AI’s Co-Founder and CEO, Gus Liu, describes their satellites as deliberately autonomous and intelligent. “Orbit AI is creating the first truly intelligent layer in orbit — satellites that compute, verify and optimize themselves autonomously”, he said, “The Orbital Cloud turns space into a platform for AI, blockchain and global connectivity. By leveraging solar-powered compute payloads and decentralized verification nodes, we are opening an entirely new, potentially US$700+ billion-dollar market opportunity — one that combines energy, data and sovereignty to reshape industries from finance to government and Web3. PowerBank's expertise in advanced solar energy systems will be significant in supporting this initiative."

This vision is not isolated. Earlier this year, Jeff Bezos echoed a similar idea at Italian Tech Week, saying: “We will be able to beat the cost of terrestrial data centres in space in the next couple of decades. These giant training clusters will be better built in space, because we have solar power there, 24/7 — no clouds, no rain, no weather.  The next step is going to be data centres and then other kinds of manufacturing.” His comments reflect a growing industry belief that space-based data centers will eventually outperform those on Earth.

The idea gains traction because the advantages are practical. Space offers free, constant solar power. It provides natural cooling, which is one of the costliest parts of running data centers on Earth. And above all, satellites in low-Earth orbit operate beyond national firewalls and political boundaries, making them more resilient to outages, censorship and conflict. For industries that rely heavily on secure connectivity and real-time data — finance, defense, AI, blockchain networks and global cloud providers — this could become an important alternative layer of infrastructure.

The upcoming Genesis-1 satellite is designed as a demonstration mission. It will test an Ethereum wallet, run a blockchain verification node and perform simple AI tasks in orbit. If the technology works as expected, Orbit AI plans to add several more satellites in 2026, expand into larger networks by 2027 and 2028 and begin full commercial operations by the decade’s end.

To build this system, Orbit AI plans to source technologies from some of the world’s most influential players: NVIDIA for AI processors, the Ethereum Foundation for blockchain tools, Galaxy Space and SparkX Satellite for satellite components, Galactic Energy for launch systems and AscendX Aerospace for advanced materials.

If successful, the Orbital Cloud could become the first step toward a world where part of humanity’s data, computing power and digital services run not in massive buildings on Earth, but in clusters of autonomous satellites illuminated by constant sunlight. For now, the journey begins with a single launch — a test satellite aiming to show that space can do far more than connect us. It may soon help power the systems that run our economies, technologies and global communication networks.

Atomathic, the company once known as Neural Propulsion Systems, is stepping into the spotlight with a bold claim: its new AI platforms can help machines “see the invisible”. With the commercial launch of AIDAR™ and AISIR™, the company says it is opening a new chapter for physical AI, AI sensing and advanced sensor technology across automotive, aviation, defense, robotics and semiconductor manufacturing.

The idea behind these platforms is simple yet ambitious. Machines gather enormous amounts of signal data, yet they still struggle to understand the faint, fast or hidden details that matter most when making decisions. Atomathic says its software closes that gap. By applying AI signal processing directly to raw physical signals, the company aims to help sensors pick up subtle patterns that traditional systems miss, enabling faster reactions and more confident autonomous system performance.

"To realize the promise of physical AI, machines must achieve greater autonomy, precision and real-time decision-making—and Atomathic is defining that future," said Dr. Behrooz Rezvani, Founder and CEO of Atomathic. "We make the invisible visible. Our technology fuses the rigor of mathematics with the power of AI to transform how sensors and machines interact with the world—unlocking capabilities once thought to be theoretical. What can be imagined mathematically can now be realized physically."

This technical shift is powered by Atomathic’s deeper mathematical framework. The core of its approach is a method called hyperdefinition technology, which uses the Atomic Norm and fast computational techniques to map sparse physical signals. In simple terms, it pulls clarity out of chaos. This enables ultra-high-resolution signal visualization in real time—something the company claims has never been achieved at this scale in real-time sensing.

AIDAR and AISIR are already being trialled and integrated across multiple sectors and they’re designed to work with a broad range of hardware. That hardware-agnostic design is poised to matter even more as industries shift toward richer, more detailed sensing. Analysts expect the automotive sensor market to surge in the coming years, with radar imaging, next-gen ADAS systems and high-precision machine perception playing increasingly central roles.

Atomathic’s technology comes from a tight-knit team with deep roots in mathematics, machine intelligence and AI research, drawing talent from institutions such as Caltech, UCLA, Stanford and the Technical University of Munich. After seven years of development, the company is ready to show its progress publicly, starting with demonstrations at CES 2026 in Las Vegas.

Suppose the future of autonomy depends on machines perceiving the world with far greater fidelity. In that case, Atomathic is betting that the next leap forward won’t come from more hardware, but from rethinking the math behind the signal—and redefining what physical AI can do.

Bindwell, a San Francisco–based ag-tech startup using AI to design new pesticide molecules, has raised US$6 million in seed funding, co-led by General Catalyst and A Capital, with participation from SV Angel and Y Combinator founder Paul Graham. The round will help the company expand its lab in San Carlos, hire more technical talent and advance its first pesticide candidates toward validation.  

Even as pesticide use has doubled over the last 30 years, farmers still lose up to 40% of global crops to pests and disease. The core issue is resistance: pests are adapting faster than the industry can update its tools. As a result, farmers often rely on larger amounts of the same outdated chemicals, even as they deliver diminishing returns.

Meanwhile, innovation in the agrochemical sector has slowed, leaving the industry struggling to keep up with rapidly evolving pests. This is the gap Bindwell is targeting. Instead of updating old chemicals, the company uses AI to find completely new compounds designed for today’s pests and farming conditions.  

This vision is made even more striking by the people leading it. Bindwell was founded by 18-year-old Tyler Rose and 19-year-old Navvye Anand, who met at the Wolfram Summer Research Program in 2023. Both had deep ties to agriculture — Rose in China and Anand in India — witnessing up close how pest outbreaks and chemical dependence burdened farmers.  

Filling the gap in today’s pesticide pipeline, Bindwell created an AI system that can design and evaluate new molecules long before they hit the lab. It starts with Foldwell, the company’s protein-structure model, which helps map the shapes of pest proteins so scientists know where a molecule should bind. Then comes PLAPT, which can scan through every known synthesized compound in just a few hours to see which ones might actually work. For biopesticides, they use APPT, a model tuned to spot protein-to-protein interactions and shown to outperform existing tools on industry benchmarks.

Bindwell isn’t selling AI tools. Instead, the company develops the molecules itself and licenses them to major agrochemical players. Owning the full discovery process lets the team bake in safety, selectivity and environmental considerations from day one. It also allows Bindwell to plug directly into the pipelines that produce commercial pesticides — just with a fundamentally different engine powering the science.

At present, the team is now testing its first AI-generated candidates in its San Carlos lab and is in early talks with established pesticide manufacturers about potential licensing deals. For Rose and Anand, the long-term vision is simple: create pest control that works without repeating the mistakes of the last half-century. As they put it, the goal is not to escalate chemical use but to design molecules that are more precise, less harmful and resilient against resistance from the start.

Hong Kong is entering a new phase of competition as mainland platforms accelerate their expansion into the city, turning it into a frontline testing ground for Chinese companies preparing to push into global markets. With retail, logistics and food-delivery businesses all reshaped in the past year, Hong Kong has become the closest international environment where mainland firms can experiment with pricing, supply chains and customer behaviour under a familiar regulatory and cultural framework.

The shift became especially clear this week. At HKTVmall’s Vision Day on November 11, 2025, CEO Ricky Wong warned that Hong Kong’s traditional retail model is facing its toughest moment yet. He said the biggest threat is not mainland competitors like Taobao, JD.com or Pinduoduo entering Hong Kong, but the city’s longstanding dependence on physical shopping. If local retailers do not evolve, he said, they risk becoming “very easy to die of thirst in the desert”. Wong even welcomed the rise of mainland e-commerce giants, arguing that the more players enter the city, the faster consumers will shift online — a transition HKTVmall relies on for growth.  

Yet his optimism is layered over a challenging reality. HKTVmall’s own numbers reflect pressure from competition and changing consumer habits. The company reported average daily GMV of HK$22.2 million during the latest shopping festival season — up 2.8% month-on-month but still down 4.3% compared year-on-year — showing that even established online platforms are struggling to maintain momentum as mainland entrants squeeze prices and widen product selection.

The city’s food-delivery market illustrates the shift even more sharply. Deliveroo, once the fastest-growing platform in Hong Kong and at one point holding more than half of the market, officially shut down in April this year after a long decline. Its trajectory mirrored the sector’s upheaval: the company surged during the pandemic but lost ground after restrictions eased, first overtaken by Foodpanda and then pressured heavily by Meituan-backed Keeta, which entered Hong Kong in 2023 and quickly seized about 30% of citywide orders.

Deliveroo’s exit and the handover of parts of its business to Foodpanda did little to stabilise the market. Keeta’s rapid expansion instead pushed Foodpanda onto the defensive, leaving two major players competing in a market shaped by mainland-style pricing and operations. Hong Kong’s delivery sector, once dominated by global firms, is increasingly defined by Chinese platforms optimizing speed and efficiency at a scale few competitors can match.

These changes are unfolding as Chinese companies shift their focus toward new global markets.  

With China reducing its reliance on the US and EU and exports steadily moving toward ASEAN, Hong Kong has become a strategic launchpad. The city’s proximity, language familiarity and regulatory structure make it the nearest international setting where Chinese firms can test overseas strategies before expanding into Southeast Asia, the Middle East or Latin America. The result is a competitive intensity that local companies have rarely experienced. Retailers face price pressure they can’t match, local platforms are losing ground to mainland giants and global players are struggling to stay in the game.

Consumers benefit from lower prices, faster delivery and wider choice — but for Hong Kong businesses, the landscape has turned unforgiving. Mainland companies are not treating Hong Kong as a final destination but as the first stop in a broader global push. That positioning is reshaping the city’s entire consumer economy. As more mainland firms look outward, Hong Kong’s role as a testing ground will only deepen and the first players to feel the impact will be those operating closest to the consumer.