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AI Scholar Soumitra Dutta Says United States Is Positioned to Lead the Next Phase of Physical Intelligence
England, United KingdomRecently, Mind Robotics, a Palo Alto-based startup, closed a $500 million bet on machines that think and act in the physical world. It is one of dozens of signals that AI’s next phase has begun. Innovation experts such as Soumitra Dutta say the US is structurally better placed than anyone to lead it.
England, United Kingdom
For the past several years, the dominant paradigm in AI has been to train large language models on massive datasets and deploy them to generate text, process information, and write software. But there is growing consensus in research circles that LLMs represent only the first wave of a much deeper transformation.
The next frontier is physical intelligence—systems that understand and interact with the world beyond language. This includes guiding a robot through complex surgery, designing new materials at the molecular level, or modeling how physical systems respond to stress and uncertainty.
Signals of this shift are emerging across the field. Turing Award winner Yann LeCun has long argued that truly intelligent systems require world models—structured representations of physical reality that allow machines to reason about cause and effect. Deep learning pioneer Fei-Fei Li is pursuing spatial intelligence through her startup World Labs. Google DeepMind is developing simulated 3D environments for training embodied AI systems, while NVIDIA CEO Jensen Huang recently introduced Cosmos, a platform for training AI in virtual worlds before real-world deployment.
“Systems that understand causality, adapt to uncertainty, and recover from errors will define the next generation of intelligent technologies. The next era of AI is about turning perception into reasoning and imagination into action,” says Soumitra Dutta, former dean of Oxford Said Business School and AI scholar.
Why the Next Phase Favors the United States
The transition from digital AI to physical intelligence will require deeper integration with scientific research, industrial systems, and real-world environments. The structural advantages that gave the United States an edge in the first phase of AI may become even more decisive in the next.
“The next phase of AI is not just about better models—it is about integrating computation with the physical world. That requires ecosystems that combine science, engineering, capital, and institutions at scale. The United States is uniquely positioned to do this,” says Soumitra Dutta, who’s co-creator of the Global Innovation Index.
The Power of the Research Ecosystem
In the first phase of AI, universities provided foundational research and talent that the private sector scaled. In the next phase, progress will depend on deep interdisciplinary collaboration across robotics, physics, materials science, and biology.
This kind of knowledge cannot be concentrated in a single firm or built overnight.
Institutions such as Stanford University, Massachusetts Institute of Technology, and Carnegie Mellon University remain global leaders in AI research. The United States continues to produce the highest share of highly cited AI publications and attract top global talent.
Equally important is the porosity between academia and industry—the unusually short distance between discovery and commercialization.
“One of America’s enduring strengths is the fluid exchange between universities, startups, and large firms. Breakthroughs move quickly from the lab to the market, creating a continuous cycle of innovation that is difficult to replicate elsewhere,” says Soumitra Dutta, Oxford Dean (Former).
Capital for Long-Term Risk
Venture capital was critical in the first wave of AI. In the second wave, it becomes indispensable.
Physical AI—robotics, autonomous systems, AI-driven laboratories—requires patient capital with long development cycles and uncertain outcomes. These are not products that can be iterated in months.
The US remains one of the few ecosystems capable of funding such risk at scale. In 2024, private AI investment in the US reached $109.1 billion—nearly twelve times that of China.
Equally important is a cultural factor: the willingness to fund multiple competing bets, knowing that most will fail but a few will define the future.
National Labs and Scientific Infrastructure
Another underappreciated advantage lies in America’s national laboratories, including Argonne, Oak Ridge, and Lawrence Berkeley.
While these institutions played a limited role in the first phase of AI, they will be central to the next. Applications such as materials discovery, climate modeling, energy systems, and drug development require infrastructure—supercomputers, particle accelerators, genomic databases—that few private actors can replicate.
U.S. Department of Energy policy already positions AI as a transformative tool for scientific discovery. National labs are integrating AI into core missions such as fusion research and battery design.
China: A Formidable Rival
China remains the United States’ most serious competitor, particularly in industrial robotics and large-scale deployment.
Chinese firms accounted for over 90% of global humanoid robot sales in 2025, and the country has filed thousands of robotics patents in recent years. Its ability to integrate data from urban systems, manufacturing, and electric vehicles gives it a powerful advantage in applied AI.
At the same time, comparative analyses—such as a 2026 report by Morgan Stanley—suggest that the United States still leads overall, particularly in private investment, research institutions, and frontier innovation.
The Strategic Balance
The emerging AI landscape is not a simple race with a single winner. It is a competition across different layers: research, capital, infrastructure, and deployment.
“The United States leads in foundational innovation and ecosystem depth, while China excels in scale and rapid deployment. The outcome of the next phase of AI will depend on how these different strengths evolve—and how effectively each country connects them into a coherent strategy,” says Soumitra Dutta, Oxford Dean (Former).
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New 2026 Florida Building Code Updates Impact Commercial Roof Compliance: John Keller Roofing Helps Longwood Businesses Prepare
Longwood, FLMay 2026 — Florida’s 2026 building code updates are now in effect, bringing important changes that impact commercial roofing systems across Central Florida. To help local businesses stay compliant and avoid unexpected costs, John Keller Roofing, a trusted commercial roofing contractor serving the Greater Longwood area for more than three decades, is offering guidance and […]
Longwood, FL
May 2026 — Florida’s 2026 building code updates are now in effect, bringing important changes that impact commercial roofing systems across Central Florida. To help local businesses stay compliant and avoid unexpected costs, John Keller Roofing, a trusted commercial roofing contractor serving the Greater Longwood area for more than three decades, is offering guidance and inspections tailored to the new requirements.
The updated codes introduce stronger wind‑resistance standards, revised fastening guidelines, and more detailed inspection documentation for commercial roofs. These changes aim to reduce storm‑related damage and improve long‑term building safety. However, many commercial property owners are unaware that their existing roofs may no longer meet the updated criteria.
“Commercial buildings in Central Florida deal with intense heat, heavy rain, and strong winds every year,” said John Keller, owner of John Keller Roofing. “These new code updates are designed to protect businesses, but they can be confusing if you’re not in the roofing industry. My goal is to make it easy for owners and managers to understand what’s required so they can stay compliant and avoid surprises.”
To support businesses throughout Longwood, Altamonte Springs, Lake Mary, and surrounding areas, John Keller Roofing is offering commercial roof evaluations focused on identifying potential compliance issues early. These assessments review roof membranes, drainage systems, flashing, fasteners, and structural components to determine whether updates or documentation changes are needed.
For many commercial properties—especially older buildings—proactive inspections can help prevent costly insurance complications, reduce the risk of storm damage, and ensure the building meets the 2026 standards before the peak weather season.
“Most people don’t realize that a roof that passed inspection years ago may not meet today’s requirements,” Keller added. “A quick check now can save thousands of dollars later, especially with hurricane season right around the corner.”
John Keller Roofing encourages commercial property managers, warehouse operators, retail centers, and office building owners to schedule a compliance review to ensure their roofs meet the 2026 Florida building code updates and are prepared for the months ahead.
For more information or to schedule a commercial roof evaluation, visit www.cflroofer.com
John Keller Roofing at 407‑332‑0345 for scheduling and service coordination.
Business address for reference: 1228 Bella Vista Circle, Longwood, FL 32779
Hours: Mon–Fri: 7:30 a.m.–5:00 p.m.; Sat: 9:00 a.m.–12:00 p.m.
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Deltologic Introduces DataDoe Amazon Seller MCP to Bring Enterprise Ecommerce Infrastructure to Smaller Teams
DOVER, DelDeltologic, a software and AI implementation company focused on ecommerce, is introducing DataDoe as a new way for ecommerce teams to build AI workflows on top of real marketplace data. For the last six years, Deltologic has built custom Amazon integrations, marketplace automation, reporting systems, internal tools and data infrastructure for ecommerce companies with complex […]
DOVER, Del
Deltologic, a software and AI implementation company focused on ecommerce, is introducing DataDoe as a new way for ecommerce teams to build AI workflows on top of real marketplace data.
For the last six years, Deltologic has built custom Amazon integrations, marketplace automation, reporting systems, internal tools and data infrastructure for ecommerce companies with complex operations. Many of these projects were built for larger teams that had the budget, time and technical resources to create custom systems from scratch.
DataDoe comes from that experience. The idea is simple: smaller ecommerce teams should not need a large engineering budget to build useful systems around their data. They should be able to connect their marketplace data once, use it safely, and build workflows with AI tools such as Gemini, ChatGPT, Cursor and Claude Code.
DataDoe is starting with Amazon because Amazon data is one of the hardest parts of ecommerce operations to organize well. Sellers and vendors work across Seller Central, Vendor Central, Amazon Ads, FBA inventory, fees, settlements, reimbursements, returns, listings, Brand Analytics and profit data. These sources are valuable, but they are usually disconnected.
That is where DataDoe’s Amazon Seller MCP comes in. It gives AI tools and development environments a structured way to work with Amazon seller data, instead of forcing teams to rely on exports, screenshots, disconnected spreadsheets or custom SP-API projects.
Teams can use DataDoe’s Amazon Seller Central MCP to support reporting, dashboards, internal tools, client summaries, profitability analysis, inventory workflows, advertising reviews and AI-assisted operations. Developers can build on top of structured Amazon data instead of starting every project by rebuilding the same data foundation.
“Smaller ecommerce teams should not need a massive engineering budget to build useful AI workflows,” said Kris Krokos, Co-Founder of Deltologic and DataDoe. “DataDoe gives them the data foundation to start.”
Security and reliability are important parts of the product because DataDoe is built by a team that has spent years working on production ecommerce systems, marketplace integrations and enterprise AI implementation. The platform is designed around permissioned access, structured data handling and a clear separation between the ecommerce data layer and the AI interface.
Deltologic sees this as part of a bigger shift in ecommerce software. In the past, smaller companies often had to choose between manual spreadsheets, generic SaaS dashboards or expensive custom development. MCP and AI are changing that model. With the right data layer, ecommerce teams can build more of their own workflows, reports, agents and internal tools without starting from zero.
DataDoe is starting with Amazon and is already testing additional marketplaces. The broader goal is to become a reliable ecommerce data layer for AI implementation across marketplaces, helping sellers, vendors, agencies and developers build AI-native operations on top of clean operational data. Teams using Claude can also explore how to connect Amazon seller data to Claude through DataDoe’s Claude integration page.
DataDoe is available for sellers, vendors, agencies, developers and ecommerce teams building AI workflows on top of marketplace data.
About DataDoe
DataDoe is built by Deltologic — a marketplace technology agency that’s spent six years shipping integrations, custom software, and AI infrastructure for brands across North America, Europe, and Asia.
Media Details
Organization: Deltologic
Website URL: https://www.datadoe.com/
Name: Jakob Wolitzki
Email Address: [email protected]
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Tips.GG Analyzes Champions League Finals: 11 Players Whose Triumphs Are Overshadowed by Defeats
LONDON, UKEvery one of the 11 footballers in history who reached four or more European Cup finals and won fewer than half — all eleven — ended their last final in defeat. This is just a fraction of the surprising findings Tips.GG discovered during their latest research. To compile the comprehensive “The Stigma of Losers,” the […]
LONDON, UK
Every one of the 11 footballers in history who reached four or more European Cup finals and won fewer than half — all eleven — ended their last final in defeat. This is just a fraction of the surprising findings Tips.GG discovered during their latest research.
To compile the comprehensive “The Stigma of Losers,” the sports data platform analyzed every European Cup and UEFA Champions League final from 1956 to 2025. By cross-referencing records from the UEFA archive, RSSSF, and Transfermarkt, the dataset isolates the rare and painful anomaly of players who reached the pinnacle of European football, only to lose more often than they won.
1 almost perfect starting XI: The 11 players on the list form an almost perfectly balanced football squad consisting of 1 goalkeeper, 3 defenders, 5 midfielders, and 2 forwards, meaning the historical dataset accidentally engineered a complete lineup.
“You make the run from the group stage to the final four times, meaning you knock out three top-tier opponents in the playoffs each time, even if you only lift the cup once. But a player who makes this run just once, and wins, is not considered a loser, while you are — it is a paradox,” said Anton Malyutin, CEO of Tips.GG.
Beyond the individual extremes, the full analysis breaks down the specific facts and match data that forged the rest of the ledger:
- 5 players from a single pre-Bosman squad: The structural immobility of the 1960s era meant a legendary Benfica core kept its squad together, resulting in five players accumulating multi-final negative records over an eight-season span.
- 3 negative records sealed on penalties simultaneously: Alessandro Del Piero, Gianluca Pessotto, and Edgar Davids all registered their final losses on the exact same night during the 2003 Old Trafford penalty shootout between Juventus and Milan.
- 4 players lost finals with multiple clubs: Patrice Evra, Edgar Davids, Didier Deschamps, and Edwin van der Sar endured the statistical anomaly of losing Champions League finals while playing for different finalist teams.
Media contact
Mykhaylo Zemlyankin
New 2026 Florida Building Code Updates Impact Commercial Roof Compliance: John Keller Roofing Helps Longwood Businesses Prepare
Deltologic Introduces DataDoe Amazon Seller MCP to Bring Enterprise Ecommerce Infrastructure to Smaller Teams
Tips.GG Analyzes Champions League Finals: 11 Players Whose Triumphs Are Overshadowed by Defeats
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