It’s possible that I shall make an ass of myself. But in that case one can always get out of it with a little dialectic. I have, of course, so worded my proposition as to be right either way (K.Marx, Letter to F.Engels on the Indian Mutiny)
Monday, August 10, 2026
Q&A: Is robotics replacing AI as the next investment darling?
Humanoid robot Alter-Ego is designed to perform basic tasks to free up healthcare workers – Copyright AFP MARCO BERTORELLO
Growing up in the ‘80s and watching the Rosey the Robot clean, cook and fold laundry for the Jetson family, the thought of having a personal robot maid was nothing more than science fiction. Then, everything started to change in 2002, when we were introduced to the iRobot Roomba vacuum. Today, we have “smart” appliances/homes, autonomous drone delivery and self-driving cars. So, what’s next for personal home robotics?
Digital Journal sat down with one of the leading experts in robotics, Andrew Kang, CEO of RoboStrategy (NASDAQ BOT), the first publicly traded fund focused exclusively on robotics and physical AI, to discuss where the robotics industry stands today, and why physical AI could reshape nearly every sector of the economy.
Digital Journal: Many people view AI software as the next technology revolution. Why do you believe robotics deserves equal attention?
Andrew Kang: Robotics represents AI moving from the digital world into the physical one. While software has already transformed knowledge work, robots have the potential to automate physical labour across industries. Unlike many physical technologies that solve a single problem, robots can automate physical work wherever labour is required. In many ways, robotics is productizing physical labour, much like cloud computing productized computing power. Every economy depends on people performing physical tasks, so the addressable market extends far beyond traditional industrial automation. In 2025, Morgan Stanley reported that the global market for humanoid robots could reach $5 trillion by 2050. As AI continues to improve, robots will become capable of handling increasingly complex work, dramatically expanding the number of use cases. Today the sector is still relatively small compared with other technology markets, but innovation is happening rapidly among private companies. That combination of early-stage development and enormous long-term opportunity makes robotics one of the most compelling areas to watch over the next decade.
DJ: When do you expect robots to become a common part of everyday life?
Kang: As with all new technologies, widespread adoption will likely happen gradually rather than all at once. Industrial deployments are already scaling, particularly where labour shortages exist, and we will see exponential growth there in the coming years. I expect consumer adoption to take only slightly longer, primarily because robots operating around families require higher safety standards and far greater production capacity. However, I expect robots to start becoming more commonplace in homes, businesses and public settings as early as 2030. Meanwhile, the next several years will focus on scaling manufacturing and refining the technology.
DJ: What are the biggest technical hurdles standing between today’s prototypes and large-scale deployment?
Kang: Software continues to improve rapidly, but manufacturing remains one of the biggest bottlenecks. Producing millions of robots requires supply chains that don’t yet exist at that scale. Components such as actuators, sensors and specialized mechanical systems must be manufactured reliably and economically in enormous volumes. Building that industrial infrastructure will take time, but it’s a challenge measured in years rather than decades.
DJ: What characteristics do you look for when evaluating early-stage robotics companies for investment?
Kang: Our first step is to determine whether the market opportunity is large enough to support the business that is likely disruptive and transformational. Then, since revenue is usually non-existent at that stage, the next focus would be on the quality of the founding team. It is critical to study founders’ track records, their ability to recruit exceptional technical talent and whether they’ve consistently executed against ambitious goals. Those qualities often prove to be stronger indicators of long-term success than any short-term financial metrics.
DJ: Which industries do you believe will adopt robotics first, and why?
Kang: The earliest large-scale adoption will likely occur in industrial environments where robots perform repetitive, well-defined tasks. Today’s AI models are becoming increasingly capable, but they’re still most effective when operating within structured settings. Factory work, warehouse operations and manufacturing often involve repeating the same motion thousands of times, making them ideal applications for robotics. As AI models become more generalized and capable of handling greater complexity, robots will gradually expand into healthcare, hospitality and eventually everyday household tasks.
DJ: Looking ahead five years, where do you see the greatest opportunities within robotics?
Kang: Robotics is unique because it has applications across virtually every industry. AI software continues to grow rapidly, but physical AI hasn’t yet experienced the same level of commercialization. Robots essentially transform physical labour into a scalable technology platform, opening opportunities in manufacturing, logistics, healthcare, energy, hospitality and even space exploration. Because the potential use cases are so broad, we’re only beginning to understand how significant the market could eventually become.
DJ: Defense spending on autonomous systems is increasing around the world. How do you see military demand influencing robotics development?
Kang: Defence will undoubtedly become an important application for robotics because governments are naturally interested in technologies that strengthen capabilities. At the same time, like many innovations, many robotics functionalities are inherently dual-use, meaning they can serve both civilian and military purposes. While commercial markets such as manufacturing and consumer applications remain enormous opportunities, companies should recognize that government interest will likely accelerate development in certain technologies, even if that wasn’t their original intention.
DJ: Which areas of robotics do you believe remain the most underserved today?
Kang: Industrial robotic arms and collaborative robots represent a major opportunity, particularly if countries like the United States want to expand domestic manufacturing. Not every task requires a humanoid robot capable of walking. Many valuable jobs are stationary, whether in factories, laboratories, hospitals or commercial kitchens. Developing flexible robotic systems that can automate these environments could have an enormous economic impact while helping manufacturers address ongoing labour shortages.
DJ: Our final question… If you had a robot in your home, what would you name it?
Kang: Charles. That sounds like a good name for a butler-bot!
Google DeepMind co-founder Demis Hassabis is transitioning into a new role that’s more focused on “long-term strategy” – Copyright AFP/File Karl Mondon
Google’s head of AI, Demis Hassabis, will step down from his current role to become Alphabet’s chief scientist amid a reshuffling at DeepMind that will also include a key engineer’s departure.
Hassabis will take on two new titles, Alphabet announced on Wednesday: chair of DeepMind and chief scientist of Alphabet.
The change “will allow me to focus on long-term strategy, and accelerating scientific breakthroughs, including leaning into my work at Isomorphic to help cure disease,” Hassabis wrote in a social media post.
Isomorphic Labs is an AI-powered drug discovery lab that spun out from DeepMind in 2021. In 2024, Hassabis won a Nobel Prize in chemistry.
As part of the shake up, DeepMind’s chief technology officer and chief AI architect Koray Kavukcuoglu will become the division’s senior vice president and oversee its operations including the development of Google’s flagship frontier models and products which are known as Gemini.
Longtime Google engineer Jeff Dean is also leaving the company after nearly three decades to “try something new, and we’re excited to support him in that,” Google CEO Sundar Pichair said in a blog post announcing the news.
Hassabis co-founded DeepMind in 2010, and four years later, Google bought the research lab for $650 million, according to reports at the time. His co-founder, Mustafa Suleyman, is currently the chief of Microsoft’s AI segment.
Google is competing for customers alongside other major AI developers including Microsoft, OpenAI, Anthropic and Meta, as well as DeepSeek and Moonshot in China.
The industry is also chasing a theoretical milestone known as advanced general intelligence (AGI), which is a point when AI software matches the capabilities of human thinking.
“I’ve been working towards AGI my whole life and now, like many of you, I feel it is close at hand,” Hassabis wrote in Wednesday’s blog post.
“With this backdrop, I’ve decided that now is the right time for me to hand over my day-to-day operational responsibilities at (Google DeepMind), so that I have the time and space to focus on the big picture and help influence what is to come,” Hassabis continued.
Observers have been waiting for Google to announce a more powerful version of its AI model, called Gemini 3.5 Pro, which was supposed to launch in June but appears to be delayed.
Its shares closed 4 percent down Wednesday afternoon.
The leadership changes come amid a wider brain drain at Google.
In June, a top AI and engineering executive, Noam Shazeer, left for OpenAI, while senior researcher John Jumper, who shared the 2024 Nobel Prize in chemistry with Hassabis, jumped to Anthropic.
Why human approval is not enough: The growing need for AI agent observability
OpenAI says it is building a ‘superapp’ that combines ChatGPT, a coding tool, online search, and AI agent capabilities – Copyright AFP SEBASTIEN BOZON
As artificial intelligence continues its rapid progression from chatbot to autonomous digital worker, a growing question faces enterprises: when an AI agent makes a decision, who is really in control? Many organizations assume that inserting a human approval step into an automated workflow creates sufficient oversight. A procurement recommendation, compliance action, customer response, or financial transaction is generated by an AI agent and then presented to a human for approval.
However, a growing body of AI governance experts argue that such approval checkpoints can create the appearance of control while offering little genuine oversight. If a reviewer cannot see what information the AI accessed, what rules it applied, what systems it interacted with, or what actions it has already taken, then the human approver may become little more than a ceremonial signatory.
As AI agents become increasingly capable of executing complex, multi-step workflows, the concept of agent observability is emerging as a critical component of enterprise governance.
The illusion of human oversight
Organisations have long relied on human review as a risk-control mechanism. Whether signing off deviations in pharmaceutical manufacturing, approving financial transactions, or authorizing changes to IT systems, human checkpoints are intended to ensure accountability and judgment. The challenge with modern AI agents is that they often operate across multiple systems simultaneously.
An agent tasked with processing a customer complaint may search internal documentation, access customer relationship management databases, generate a proposed resolution, and update records. By the time a human reviewer receives a recommendation, significant activity may already have occurred.
Without visibility into the decision process, the reviewer may only see a summary and a request for approval. This creates what governance specialists increasingly describe as an accountability gap. The human remains responsible for the outcome but may lack the evidence necessary to evaluate whether the recommendation is correct. How AI agents differ from traditional software
Traditional software applications generally follow predictable rules. Input data enters a defined process, producing an expected output. AI agents are fundamentally different.
Agents are designed to reason, plan, choose tools, retrieve information, and adapt their behaviour according to objectives. Microsoft’s guidance on agentic AI describes agents as systems capable of independently determining which actions are required to complete tasks rather than merely responding to prompts. Microsoft’s Agentic AI framework emphasises planning, memory, tool use, and autonomous execution capabilities.
As a result, understanding the final recommendation alone may not be sufficient. This is because organisations need to understand what data was accessed, which systems were queried, and what prompts or instructions were followed, among other things. What is AI agent observability?
Observability is not a new concept. IT teams have long used observability tools to monitor system performance, network traffic, and application reliability. Agent observability extends this principle to AI decision-making. Instead of simply measuring system uptime or execution speed, agent observability provides a detailed audit trail of an agent’s behaviour.
In effect, observability creates a transparent record of how the agent reached a conclusion.
This enables human reviewers to challenge, validate, override, or escalate decisions when necessary. Without such information, approvals may become little more than administrative formalities.
One of the biggest governance risks associated with AI deployment is the potential for reviewers to become passive approvers. This phenomenon is sometimes referred to as automation bias, where humans place excessive trust in automated recommendations. Research by the U.S. National Institute of Standards and Technology (NIST) highlights the importance of human oversight and understandability within trustworthy AI frameworks. Organizations are encouraged to ensure users can appropriately supervise AI systems rather than simply accepting recommendations at face value.
A reviewer presented with a concise recommendation may be inclined to approve it, particularly when workloads are high and time pressures exist.
Paradoxically, the presence of a human checkpoint can create a false sense of security for executives, auditors, regulators, and stakeholders. The organisation can state that “a human approved the decision” while overlooking whether the individual had sufficient information to provide meaningful scrutiny.
The challenge facing enterprises is not whether AI agents should be autonomous.
In many cases, autonomy delivers substantial business benefits through increased productivity, faster decision-making, and improved operational efficiency. Instead, organisations must determine which decisions require full automation or human review. In this context, not every decision carries the same level of risk.
For example, an AI agent scheduling meetings may require minimal oversight whereas an AI agent modifying financial records, approving suppliers, updating quality documentation, or processing healthcare information may require extensive governance controls. This is where escalation thresholds matter the most and organisations need predefined criteria that identify when an agent must pause and seek additional human involvement. Such thresholds help ensure that human involvement is reserved for situations where judgment genuinely adds value.
The issue is particularly relevant for highly regulated sectors. Pharmaceutical companies, for example, operate under strict expectations surrounding data integrity, traceability, auditability, and documented decision-making. For instance, a quality assurance professional would not normally approve a manufacturing deviation without reviewing supporting evidence. Similarly, financial organizations require transaction records before authorizing significant movements of funds. The same expectations should increasingly apply to AI agents.
If an agent recommends a corrective action, supplier approval, compliance determination, or process change, reviewers should be able to see the evidence trail supporting that recommendation. In many respects, agent observability resembles traditional audit trail requirements already familiar to regulated industries. The difference is that the audit trail now captures not just system activities but elements of machine reasoning and decision context.
Hence, the future of enterprise AI depends on trust. Trust does not emerge simply because a human clicks an approval button. Instead, trust develops when organizations can demonstrate transparency, accountability, and traceability throughout the decision-making process.
AI model captures how humans read, paving the way to personalised text and better augmented reality
Researchers now understand not just how our eyes move when we read, but also how we build meaning from text
Researchers at Aalto University, together with international partners, have developed the most accurate model yet of how humans read. The new model uses reinforcement learning, a type of AI used in robotics, to explain—and recreate—the choices readers make as they move through text.
‘For the first time we’ve used AI methods to understand—not just mimic—how people read,’ says Professor Antti Oulasvirta from Aalto University. In a study to be published on Monday, August 10, in Nature Human Behaviour, researchers say the model could power smarter Augmented Reality (AR) displays and tailor complex texts to different readers and everyday situations.
Earlier models learned from large datasets pairing text snippets with eye tracking data, then mimicked human behaviour, but they lacked true understanding of the content and didn’t generalise well across languages or contexts, explains Oulasvirta. In contrast, the new model follows the psychological mechanisms readers use to direct attention, revealing how understanding is built as the eyes move through words, sentences and paragraphs.
Understanding how human memory serves reading is the key to unlocking enormous potential for customisable apps, services or products, according to Oulasvirta.
‘We read all the time, yet throughout written history we have read texts that have been produced for mass use and not for an individual person and a specific situation,’ he says. ‘Now we are in a position to change that.’
How it works
The new model is guided by resource rationality—the idea that while reading, we constantly decide where to look next to improve our understanding as much as possible within the time available. Decisions about gaze allocation are made at three levels: word, sentence and text. They are influenced by factors such as a reader’s language, memory capacity and their vision and eye speed. For example, a fast reader with a good memory may jump briskly from one paragraph to the next, whereas a reader with a poorer memory is more likely to loop back.
‘Reading feels effortless, but your brain is constantly deciding where to look, what to skip, and when to backtrack—spending attention like a budget to maximize understanding,’ says Professor Shengdong Zhao from City University of Hong Kong.
The researchers added reader characteristics as parameters so that each could be adjusted, then let the model learn for itself the best strategy for directing attention.
‘We placed the model in a world with millions of texts. Then, using AI-based reinforcement learning, we trained it to optimise eye movements so that it truly understands what it reads,’ Oulasvirta explains.
As it reads, the model forms a condensed description of the text’s content. When a crucial word or clause is missing, the gaze can be directed to gather that information. The model’s understanding can be tested by asking what it retained from the text within the given time and constraints.
When the researchers compared the model’s attention-allocation decisions with real human eye-tracking data they found that its decisions mirrored readers’ behaviour. In practice, they had succeeded in building a model of an average reader that can be tailored to different reader profiles.
What’s next?
The development paves the way to new reading support tools and personalised text design. For example, the model could be used to enable smart glasses that pace and lay out on-screen text to fit the situation and the user’s needs, or to customise texts to suit users.
‘We could take the same source text—say, a convoluted piece of legal writing—and with little effort produce versions that are more comprehensible for different readers,’ Oulasvirta says.
The next step for the team will be to evaluate how the model can be used to help individuals suffering from dyslexia and low language proficiency.
‘We want to help users in real-time situations, for example, by designing text that helps drivers without distracting them,’ says Oulasvirta. ‘Now we have this new understanding of something that’s so central to our lives, it’s just a matter of exploring all the possibilities.’
In addition to Aalto University, the study involved researchers from The Hong Kong University of Science and Technology, City University of Hong Kong, and the National University of Singapore.
Hierarchical Resource Rationality Explains Human Reading Behavior
Article Publication Date
10-Aug-2026
Governor Hochul announces Empire AI Beta fully online as federal government takes inspiration from New York to launch state and regional AI infrastructure hubs
New York's Empire AI served as model for new national science foundation to build out regional AI research infrastructure
New York's New $40 Million Supercomputer Gives Researchers Across New York Access to World-Class AI Computing Power
New York's Empire AI Served as Model for New National Science Foundation To Build Out Regional AI Research Infrastructure
Governor Kathy Hochul today announced that Empire AI Beta is officially online, giving researchers at New York's leading public and private universities access to the most powerful academic research computer in the country and marking a major milestone in New York's effort to lead the nation in responsible artificial intelligence for the public good. As convened by Governor Hochul and consortium partners, the Empire AI initiative is already serving as a national model for public-interest AI use. As the federal National Science Foundation has announced a major investment to support regional AI infrastructure and shared research capacity through their new State and Regional AI Infrastructure Hubs initiative, Empire AI is already powering world-class research and serving academics, students and communities across the state.
"New York State built Empire AI to show that artificial intelligence can be developed for the public good and with Empire AI Beta officially online, New York is giving our researchers the most powerful academic AI research computer in the country," Governor Hochul said. "The National Science Foundation's new hubs embrace the same core principle behind Empire AI — when government, universities, philanthropy and industry come together, they can deliver outstanding results."
Empire AI Board Chairman Tom Secunda said, "Empire AI is showing the nation what dedicated partners across government, research institutions, and philanthropy can build together: a scientific asset no institution could create on its own, advancing the public good. Thanks to Governor Hochul's leadership and vision, New York is setting the standard for how the United States can build and maintain AI infrastructure by researchers and for researchers."
SUNY Chancellor John B. King Jr. said, "Empire AI Beta is a testament to Governor Hochul's leadership and the power of New York State higher education to lead the way in the use of AI to accelerate research that saves lives and strengthens our communities. Thanks to Empire AI, SUNY's researchers are making advances every day in fields like health care, public safety and emerging technologies, all while demonstrating responsible environmental stewardship."
Empire AI Research Computing Director Kiran Keshav said, "Turning on Beta is a major leap forward for Empire AI and for academic research across New York. Researchers who were once limited by access to computing power can now ask bigger questions, test more ambitious ideas and move faster from theory to discovery. From medical diagnostics and climate modeling to safer infrastructure and more trustworthy AI systems, this system will help New York's researchers do work that would not otherwise be possible."
State Senator April N.M. Baskin said, "Having the most powerful academic research computer in the country at the University at Buffalo is a tremendous achievement for Western New York. Empire AI will expand opportunities for students and researchers to lead groundbreaking discoveries while ensuring artificial intelligence is developed responsibly and for the public good. I'm proud that the University at Buffalo is at the center of this effort, helping shape the future of AI for the benefit of all New Yorkers as Empire AI continues to grow."
State Senator Jeremy Zellner said, "Innovation and responsibility go hand in hand. Empire AI shows that New York can lead the world in artificial intelligence by investing in public research, supporting our universities, and ensuring these technologies are developed in ways that benefit everyone. I applaud Governor Hochul for her leadership in making this investment and for putting New York at the forefront of AI innovation."
Assembly Majority Leader Crystal Peoples-Stokes said, "I am excited to see Empire AI Beta come online. New York has no shortage of challenges where Empire AI can offer analyzed solutions to address societal ills. With over 300 projects currently queued up, I look forward to seeing Empire AI in action through our partners in research and higher education and am confident in Empire AI's ability to help Governor Hochul, her administration and the State Legislature effectuate leadership for the greater good of New York State."
Housed at the State University of New York at Buffalo, Empire AI Beta is a $40 million NVIDIA-powered supercomputer that dramatically expands the computing power available to academic researchers across New York State. The system delivers an 11-fold increase in AI training capacity, a 40-fold boost in AI inference and an 8-fold expansion in data storage compared to Empire AI Alpha, the consortium's initial system launched in 2024. With over 300 research projects already queued up to use the system, Beta will accelerate work across fields including health care, climate science, advanced manufacturing, education, cybersecurity, public safety and other areas that directly benefit New Yorkers.
The launch of Beta represents the next major step in Empire AI's phased buildout. Alpha, the consortium's initial system made possible by philanthropic support from the Simons Foundation, has already supported more than 130 research projects and hundreds of researchers across New York. Beta now brings a transformative increase in capacity, while construction continues on Empire AI's permanent, full-scale Gamma facility at the University at Buffalo, which is expected to be completed by the end of 2027.
Once complete, the Gamma facility will also be the most efficient high-powered computing center in the nation. By integrating into University at Buffalo's buildout of a thermal energy network in a closed loop system, process heat from Empire AI will be used to heat buildings on campus, dramatically improving the school's ability to meet net zero goals.
Empire AI member institutions include the State University of New York, the City University of New York, Columbia University, Cornell University, New York University, Rensselaer Polytechnic Institute, the University of Rochester, Rochester Institute of Technology, the Icahn School of Medicine at Mount Sinai and the Flatiron Institute at the Simons Foundation.
The Governor announced Empire AI in her 2024 State of the State to create a state-of-the-art artificial intelligence center at the State University at Buffalo to be used by New York's leading institutions to promote responsible research and development, create jobs, and unlock AI opportunities focused on public good. With Empire AI Beta fully online, New York is already delivering on the computing power, institutional partnership and research capacity that NSF is looking to replicate.
Empire AI is backed by more than $500 million in public and private funding, and is made up of 10 member universities and research institutions. In May 2025, Governor Hochul secured funding to expand access for SUNY researchers at the State University of New York at Albany, State University of New York at Binghamton, State University of New York at Buffalo and State University of New York at Stony Brook, and support the addition of new members including the University of Rochester, the Rochester Institute of Technology, and the Icahn School of Medicine at Mount Sinai. They joined the seven founding members of Empire AI: SUNY, CUNY, Columbia University, Cornell University, New York University, Rensselaer Polytechnic Institute and the Flatiron Institute.
In her 2026 State of the State agenda, Governor Hochul proposed the launch of Empire AI Beta, which will accelerate Empire AI's performance to 11 times its former scale, making it the world's most advanced academic supercomputer. Governor Hochul also announced a record-breaking gift to the State University of New York at Binghamton to create the first independent university AI research center in the United States, the Center for AI Responsibility and Research at Binghamton University. The $30 million philanthropic gift, the largest academic gift in the university's history, is coupled with a $25 million research capital investment by SUNY.
About the State University of New York The State University of New York is the largest comprehensive system of higher education in the United States, and more than 95 percent of all New Yorkers live within 30 miles of any one of SUNY’s 64 colleges and universities. Across the system, SUNY has four academic health centers, five hospitals, four medical schools, two dental schools, a law school, the country’s oldest school of maritime, the state's only college of optometry, 12 Educational Opportunity Centers, over 30 ATTAIN digital literacy labs, and manages one US Department of Energy National Laboratory. In total, SUNY serves about 1.7 million students across its portfolio of credit- and non-credit-bearing courses and programs, continuing education, and community outreach programs. SUNY oversees nearly a quarter of academic research in New York. Research expenditures system-wide are nearly $1.5 billion in fiscal year 2025, including significant contributions from students and faculty. There are more than three million SUNY alumni worldwide, and annually one in three New Yorkers who earn a college degree is a SUNY alum. To learn more about how SUNY creates opportunities, visit suny.edu.
Q&A: Is robotics replacing AI as the next investment darling?
Humanoid robot Alter-Ego is designed to perform basic tasks to free up healthcare workers – Copyright AFP MARCO BERTORELLO
Google DeepMind co-founder Demis Hassabis is transitioning into a new role that’s more focused on “long-term strategy” – Copyright AFP/File Karl Mondon
OpenAI says it is building a ‘superapp’ that combines ChatGPT, a coding tool, online search, and AI agent capabilities – Copyright AFP SEBASTIEN BOZON
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