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A Major Labor Boom in CNY? Not Here Yet
With so many major projects going on — Micron, I-81 viaduct and others — job growth is predicted to increase. But so far the number of jobs in the region has declined By Aaron Gifford [caption id=attachment_11065 align=alignright width=350] Amy Stage, deputy director at the Manufacturers Association of Central New York (MACNY): “For us, the conversation isn’t just about filling jobs. It’s about creating sustainable pathways that help people see a future for themselves in manufacturing.”[/caption] The I-81 viaduct project in Syracuse and Micron facility construction in Clay are expected to create a major labor boom for Central New York. But for the time being, the number of jobs here has actually decreased in the past year. The New York State Department of Labor reported that, for the annual comparison of June 2025 to this past June, private sector jobs decreased by 2,200 or less than 1% to 260,000. Job gains were concentrated in leisure and hospitality (+700), private education and health services (+400) and other services (+300). Job losses for that annual period were reported in the areas of trade, transportation and utilities (-1,500); professional and business services (-1,000); financial activities (-500); mining, logging and construction (-300); manufacturing (-200); and information (-100). The public sector job count rose over 12 months, with most growth concentrated in education, the state Department of Labor reported. Looking ahead, the Central New York economy is expected to benefit from both the Micron and I-81 viaduct projects over a period of several years. Initial job growth is foreseen in construction and computer and electronic manufacturing. Spin-off jobs will be created in the areas of professional and business services, health care and education, the state Department of Labor said in an email response to Oswego County Business magazine. “Population growth tied to the buildout of Micron Technology semiconductor complex in Clay is also expected to bring more workers and residents to the region, increasing demand for transportation services,” the agency said. New York Department of Labor highlighted current or projected job growth trends in various sectors. An increase in defense-related contracts at local high-tech employers has continued to support employment growth in the computer and electronic product manufacturing sector and Micron is expected to generate thousands of direct and indirect jobs as the project progresses. This sector paid an annual average wage of $117,500 in 2025 and employment in the sector is projected to grow by over 60% through 2032, making it the fastest-growing industry in the region. Typical occupations in this industry include electrical, electronic and electromechanical assemblers, software developers and industrial engineers, the state agency reported. Specialty trade contractors include several different types of tradespeople — electricians, plumbers, pipefitters and steamfitters and HVAC (heating, ventilation and air conditioning) mechanics and installers. Many of these positions are trained through apprenticeships, the state Department of Labor reported. The Interstate 81 construction project is generating local jobs, including construction laborers, projected to grow at a 6.4% rate, and operating engineers and other construction equipment operators, projected to grow at a 7.3% rate over the next several years. Construction of the Micron Technology facility is also “creating thousands of job opportunities that will continue over the next two decades,” the state agency reported. The specialty trade contractors sector paid an annual average wage of $86,200 in 2025. For Central New York, the challenge isn’t creating jobs, it’s filling them. Amy Stage, a deputy director at the Manufacturers Association of Central New York (MACNY), said meeting this unprecedented growth is a much larger task than just replacing retirees. “We’re seeing workforce gaps at every experience level,” she said. “Companies need entry-level employees, experienced technicians, supervisors, engineers and skilled trades professionals. The opportunity is tremendous, but we need more people entering these career pathways.” Employers are looking for people who can troubleshoot, read technical information, use computers and learn new processes, not just perform repetitive production work, Stage said. To address this demand, the MACNY created the Manufacturers Talent Institute, which aims to connect students and job seekers with meaningful career pathways while helping employers build their workforce. Twenty-one companies have so far signed up to provide opportunities for more than 800 students. “For us, the conversation isn’t just about filling jobs,” Stage said. “It’s about creating sustainable pathways that help people see a future for themselves in manufacturing.” Several manufacturing facilities in the region are in the midst of expansion: • TTM Technologies, a leading U.S. manufacturer of advanced electronics and interconnect solutions for aerospace, defense and high-technology markets, which has a plant in DeWitt, completed its buildout of an additional facility, adding 400 jobs and expanding production of high-tech circuit boards for defense and critical electronics applications. • In Cortlandville, the Byrne Hollow Crossing Plant, a dairy manufacturing and processing facility, is increasing its operations, creating 100 new jobs. • The Chobani yogurt company is building a 1.4-million square foot dairy processing facility in Oneida County. This will create more than 1,000 good-paying jobs and double Chobani’s New York state workforce, Stage said. Stage noted several ways that local, state and federal governments can help spur manufacturing job growth in this region. Elected leaders and public agencies could work with industry representatives to create pre-apprenticeship and registered apprenticeship programs seeking to upskill workers and improve employee retention. New York state has a variety of grants, tax incentives and financing programs to support site preparation, infrastructure, workforce development and capital investment. The U.S. Department of Labor has a pay-for-performance incentive program that provides employers $3,500 per new apprentice hired and retained for 90 days. MACNY was previously awarded an $87,000 federal grant that will cover 25 apprentices at nine companies. The manufacturing skills gap in the United States, at its current rate, could result in 2.1 million unfilled jobs by 2030, according to a study by Deloitte and The Manufacturing Institute. In New York state, that gap is on pace to exceed 100,000 unfilled positions within six years. Manufacturing companies will be counting on more women to help fill that gap. While women make up half of the overall U.S. workforce, they only account for 29% of the manufacturing workforce in this country, Stage said. “Pre-apprenticeship programs like Real Life Rosies help to bring awareness to the manufacturing industry to fill some of the gaps, but we have a long way to go and it starts with educating people at younger ages about careers in manufacturing and earn-and-learn opportunities,” she said. As for Micron, that $100 billion-plus project, considered the largest private investment in New York state history, is expected to eventually create up to 50,000 jobs throughout the region, factoring in the spin-off opportunities for construction and supply chains. The plant’s foundation was poured on July 9. “As America celebrates its 250th anniversary, data and memory are foundational to the modern economy — and Micron is increasing our U.S. investments to more than $250 billion through 2035 to meet that moment,” the company’s president and CEO, Sanjay Mehrotra, said in a news release. Bechtel, Micon’s construction partner in the project, reported that 4,500 people will be employed to help build the facility, including skilled craft professionals, local union members representing various trades and apprentices. The Syracuse-based CenterState CEO organization tallies the regional labor force at 397,582 workers (Onondaga, Cayuga, Cortland, Madison and Oswego counties). Job growth isn’t limited to the Central New York region. For the larger area covering portions of the Mohawk Valley, the North Country, the Southern tier, and the western part of the state, there are 155,601 employees in the advanced manufacturing sector, and 155,275 construction workers. The agency’s 2026 Economic Forecast report noted that 41% of community and business leaders surveyed said a shortage of skilled workers in this area remains a problem, though 77% shared a positive economic outlook for the year ahead. Beyond the semiconductor and advanced manufacturing areas, local business leaders also see growth in small manufacturing companies, storage businesses, housing, unmanned flight technology and general construction. “Productive reuse of property,” the report said, “will hopefully continue to preserve unique architecture while driving business.” The report identified several challenges for economic growth in the region, including an aging workforce, outdated equipment, the rising cost of raw materials and “the disconnect between the speed of business needs and the pace of talent readiness.”
Colleges raced to build massive computer-science departments. Now the boom is turning to bust.
“Computer science isn’t the ticket to the American dream like people thought it was.”
Mathematician Who Waited 50 Years for Proper Credit for Her Work Has Died
At the Los Alamos National Laboratory, Mary Tsingou Menzel’s computer programming helped set the stage for chaos theory and other fields.
COROS DURA 2 SOLAR Review: Big Upgrade, Bigger Price
Neil's latest review digs into his experience with the COROS DURA 2 Solar, a new bike computer that claims to improve on the first model...
Google Pledges Frontier Models and Compute to European Scientists
On October 14, 2026, Google announced that Google DeepMind and Google Cloud are providing up to 200 European scientific researchers with direct access to the company's frontier models, together with $3 million in compute...
FSU computer scientists appointed National Science Foundation program director
A Florida State University researcher is helping guide national research priorities in artificial intelligence, advanced computing and data systems in his
Before Computer Science Became a Boys’ Club, Margaret Hamilton Wrote the Code
The woman who helped put astronauts on the moon was a software engineer when the field was not prestigious or lucrative.
The Milky Way is one galaxy — but it used to be thousands, new simulations reveal
A new supercomputer simulation traces how thousands of small galaxies eventually became the Milky Way.
Companies pledge free software and computing for AI in science
The European Commission has announced that a group of companies and foundations will offer free services, such as software or access to supercomputers, to European scientists using AI in their research.Unveiled on October 15 at a conference in Dublin, the initiative is the latest step by the Commission to try to help researchers use the technology. Scientists have used forms of AI, such as machine learning, for decades. But advances in deep learning in the past decade, and the wider interest in large language models since the release of ChatGPT in 2022, have spurred governments to invest in new AI tools that could help the scientific process.In July, the US announced it is spending $5 billion on the Genesis Mission, which it describes as a “national effort to harness AI for science,” funding 278 projects across areas such as autonomous labs or new AI tools in astronomy. The Commission has also identified AI in science as a priority, but the €107 million it pledged from Horizon Europe in 2025 does not come close to US spending levels. That said, the vast majority of public research spending in the EU comes from national funds. Today’s announcement at the European AI in Science Summit doesn’t commit any new public money. Instead, it launches the AI in Science Enablers Initiative, a set of voluntary contributions from companies and foundations. The idea is that the companies also benefit by getting access to scientific ecosystems, and feedback on how their tools work. Google, for example, has put up €3 million of cloud computing credits to European researchers. The Novo Nordisk Foundation intends to make its supercomputer, Gefion, available to European researchers, according to the Commission statement. The chipmaker Nvidia said it would provide access to tools, compute, training and collaborative projects.The publisher Elsevier also offered complementary licenses to some of its AI tools, plus AI training for PhD, postdoctoral and master’s students. The French AI firm Mistral will provide the European research community with access to its open-weight models, Vibe seats and application programming interface credits, according to the Commission’s statement. Vibe is one of the firm’s chatbot models. The idea is that more firms and foundations will come on board in the future as the initiative grows.
Brookline computer repair shop seeing uptick in business as prices for parts soar
Science Corporation implants its vision-restoring chip in its first Stargardt disease patient
Max Hodak, the former president and co-founder of Neuralink, detailed on stage at TechCrunch Disrupt how his company's work with PRIMA is part of its path toward a novel brain-computer interface (BCI).
Unlock free AI tools for your small business through UH Maui College webinar
Small business owners and sole proprietors across Hawaiʻi are invited to learn how autonomous AI tools can streamline operations, save time and stretch limited resources in an upcoming free webinar offered by University of Hawaiʻi Maui College.
Margaret Hamilton, trailblazer whose software powered Apollo 11 moon landing, dies at 90
MIT computer scientist led development of software for moon mission and earned Presidential Medal of Freedom
Cybersecurity firm, also North Augusta Small Business of the Year, marks 25 years in the CSRA
Founder Russell Wyatt started PC Techware in 2001, fixing home computers across Augusta. Today, the company serves businesses throughout the CSRA as an all-commercial cybersecurity and IT provider.
Florida State University computer scientist wins $100,000 grand prize at Discovery Days event
A Florida State University computer scientist won a $100,000 grand prize from cybersecurity company ReliaQuest after delivering a winning pitch for Sage,
A supercomputer is about to launch to the moon for the 1st time, aboard a rover named FLIP
And yes, it has AI.
Agency as a New Organizing Abstraction for Computer Science
Considering agency is a candidate for organizing abstraction for structuring and reasoning about autonomous computational systems.
Scientists Build a World-First DNA Computer That Runs Without Continuous Power
Reusable for up to 25 different calculations, a DNA computer points toward possible uses in energy-efficient computing and long-term data storage.
SIU zoology, computer science researchers team up for IDNR deer density study
The study focuses on white-tailed deer, a familiar sight across Southern Illinois and a species whose population levels can affect everything from agriculture to highway safety.
Software advance brings complex quantum simulations to standard computers
A major software advance could aid the creation of bigger, more reliable quantum computers, a study suggests. Researchers have developed an algorithm that enables off-the-shelf computers to run quantum ...
Time at CSUSM Helps Computer Science Grad Make Forbes' List
Parents usually do anything they can to get their kids to take a break from playing video games. Emily Sorger’s parents took a different approach. “When I was younger, my dad would say, ‘No, you can't go outside and play until you finish this level’ or ‘You can't do your homework until you play this game and tell me if you like it or...
UNCP Computer Science Program earns ABET accreditation
Photo by UNCPUNC Pembroke’s Bachelor of Science in Computer Science program has earned initial accreditation from ABET’s Computing Accreditation Commission, providing independent recognition of the quality and rigor of the program as it prepares students for careers and advanced study in computing.The accreditation, which extends through Sept. 30, 2032, comes as UNCP continues to expand its computing programs amid rapid growth in artificial intelligence, cybersecurity, data science and other emerging technologies.ABET accreditation is a voluntary, peer-reviewed process that evaluates academic programs against established standards for quality. The review examines curriculum, student outcomes, faculty qualifications, assessment practices, student support and continuous improvement.“Earning initial ABET accreditation is a significant milestone for our Computer Science program because it provides external recognition of the quality of our program and validates our commitment to continuous improvement,” said Dr. Haitao Zhao, assistant professor in the Department of Mathematics and Computer Science, in a press release. “For our department, this achievement reflects the hard work and dedication of our faculty and staff and gives our students greater confidence in the quality of their education and preparation for careers and graduate study.”For prospective students, the accreditation provides independent assurance that UNCP’s program meets standards established for computing education and is preparing graduates to enter technical fields in the global workforce.Earning the accreditation required faculty and staff to conduct a comprehensive review of the program, including its curriculum, educational objectives, student learning outcomes and assessment processes. The review included a detailed self-study, documentation and an on-site evaluation.The accreditation arrives as UNCP looks toward the next phase of growth in computing. The department plans to continue strengthening its computer science curriculum while expanding hands-on learning and research opportunities for students. It also plans to pursue ABET accreditation for its information technology and cybersecurity programs.Future plans include developing new opportunities in artificial intelligence and graduate education, including an AI track and a Master of Science in Computer Science with a focus on AI and cybersecurity.
A tiny defect in a crystal reveals a big quantum surprise
Researchers have solved a long-standing challenge in understanding how tiny defects in materials like diamond and silicon carbide behave when exposed to light. Their new computational method revealed a surprisingly important energy-loss mechanism in silicon carbide that scientists had previously underestimated. The finding helps explain a mystery that has puzzled researchers for years and could improve the design of quantum computers and sensors.
SpaceX quarterly revenue jumps, beats estimates as AI business surges
Oct 20 (Reuters) - Elon Musk's SpaceX on Tuesday said its third-quarter revenue more than doubled, beating estimates as AI and Starlink grew, while capital spending nearly tripled as it invested in compute infrastructure, sending its shares up about 3%...
A Look at the Computer Science Degree in 2026
In 2018, a student published an article in this paper titled “YC’s Computer Science Major is on the Rise.” His piece proved prescient for the next four years, as enrollment in Intro to Computer Science continued to increase, indicating a growing interest in the field. Enrollment in this introductory course in Yeshiva College reached an […]
Best of Both Worlds: 'Hybrid' Quantum Computers Could Be Just Around The Corner, Researchers Say
We are so ready for this.
Scientists Just Built a Computer Out of DNA—and It Runs Without Electricity
A new DNA computer performed 700 computations across several experiments—without silicon or continuous energy input.
Dal researcher Dr. Rita Orji named Fellow of the International Science Council
Latest international recognition caps a remarkable stretch of honours and recognition for the Dal computer scientist, who has emerged as a global leader in responsible and human-centred AI.
Mac Mini (M6) Review: Still Great, But A Tougher Sell
Price hikes have come for the Mac mini, and while it's still a great miniature computer for those who want one, the value proposition isn't quite as strong.
Weekend reads: Computer science conferences impose caps; Lancet strikes to continue; Is this the ‘mathocalypse’?
If your week flew by — we know ours did — catch up here with what you might have missed. The week at Retraction Watch featured: A company named Springer that isn’t the publisher is offering authors…
Teradata Brings Elastic Python Compute to AI Studio, Unifying Data, Governance, and AI
New distributed Python capability accelerates large-scale AI operations while keeping data and compute within the Teradata platform SAN DIEGO, Oct. 14, 2026 — Teradata today announced the general availability of Elastic Python Compute within AI Studio. The capability gives enterprise data science teams a managed, distributed Python environment for scaling AI compute and machine learning […]
David Micklos receives 2026 Genetics Education Award
Cold Spring Harbor Laboratory DNA Learning Center (DNALC) Executive Director David Micklos has received the National Association of Biology Teachers’ 2026 Genetics Education Award. For Micklos, a lifelong science teacher and driving force behind the DNALC’s innovative, hands-on science education programs, the award is especially significant. “I thank the NABT for recognizing our accomplishments at...
Glendale store owner sues federal government over cash, computers seized in raid 20 months ago
A Glendale convenience store owner filed a federal lawsuit Tuesday demanding the return of cash, computers and phones agents seized from his money services business in February 2025.
Democrats Demand Tougher Standards for Companies Running Medicaid Systems
A year after launching an inquiry into companies, including Deloitte, that receive billions in taxpayer dollars to build complex computer systems determining Medicaid eligibility, senators are urging the federal government to impose stricter rules.
New Coros Dura 2 Solar features ultra-modern design and huge battery life, but it's the smaller bevel I love
Hands-on with the sharp-looking new bike computer from Coros
New England RetroFest celebrates vintage computers and techn...
New England RetroFest debuts in Concord on Saturday, bringing vintage computer displays, parts and a flea market for enthusiasts across the region.
Garmin launches Edge 1060 bike computer with voice control, alongside new Edge Explore computers and Tacx Nēra smart trainer
New Edge 1060 heads up trio of new computers and trainer refresh from Garmin
Over 700 researchers attend computer science conference at BGU, celebrating 'golden age' of tech
Mobileye CEO Prof. Amnon Shashua also attended the conference and delivered a guest lecture on the development of machine intelligence and its impact on scientists, researchers, and experts.
SpaceX turns a profit after a string of losses as its satellite and computer leasing businesses soar
SpaceX reported a bigger quarterly profit than expected Tuesday as its satellite communications operations and its business of leasing computing power to other companies boomed.
Yakima company combines fruit packing with cutting-edge technology
Automated cameras, computer programs and other split-second technology provide up to 270 photos of each apple as it races along conveyer belts.
Scientists Discover a Hidden Hydrogen Pathway That Could Transform CO2 Into Valuable Fuel
Scientists have uncovered an unexpected pathway for hydrogen transfer during catalytic CO2 conversion. A computer model designed to predict how carbon dioxide becomes useful chemicals produced a surprising error: It identified formic acid as the main product instead of methanol. When scientists expanded the model to account for thousands of previously overlooked reactions, its predictions
Following 'millions' of computations, a coder is convinced they've deciphered the mysterious plaque in Super Mario 64
Serious Computer Business
DOE Announces Science Applications for Quantum Computing
The U.S. Department of Energy (DOE) today announced the release of Quantum Genesis Priority Applications (QGPAs), a set of eight key scientific utility problems that will guide the Quantum Genesis’s goal of developing fault-tolerant scientifically relevant quantum computers. This effort directly supports the goals outlined in President Trump’s Executive Order Ushering in the Next Frontier of Quantum Innovation and builds on the recommendations made in the Blueprint for DOE Quantum Supercom
Is AI Leading to Declining Enrollment in Computer Science?
After years of seeing climbing enrollment in computer science, the Connecticut State Department of Education found enrollment going the other way every year since 2022, when ChatGPT was released to the public.
Advance Could Lead to Bigger, More Reliable Quantum Computers, University of Edinburgh Scientists Say
Researchers developed an algorithm that allows standard computers to simulate photonic quantum devices.
Florida woman charged in $330K fraud case targeting Hanover business’ bank account
A 41-year-old Florida woman is facing felony charges in “an elaborate computer fraud scheme” that left a Hanover County business out $330,000.
Microsoft AI CEO says new model beats OpenAI using less compute
Microsoft AI CEO said its latest AI model outperforms an OpenAI base model on certain pre-training metrics using a quarter of the compute.
Omata Classic Analog Cycling Computer is Back, Updated and Now Built by Silca!
The beautifully simple Omata analog GPS cycling computer is now owned & made by Silca in the USA in strictly limited edition numbers!
UTC computer science team’s AI research paper accepted to NeurIPS 2026
A research team in the University of Tennessee at Chattanooga Department of Computer Science and Engineering has had a paper accepted to the main track of NeurIPS 2026, one of the world’s leading conferences in artificial intelligence and machine learning.
City College’s computer science department adapts to AI technology
City College’s computer science courses and instructors have had to adapt to AI as they teach students how to use it properly and create a future curriculum for a potential AI major. Since 2022, schools have been concerned with the use of AI by students to cheat or plagiarize their assignments. In the last four...
South Brunswick BOE Approves High School Computer Science Research Study Involving ChatGPT
South Brunswick High School students will participate in research exploring how ChatGPT and AI tools can support computer science education.
SIU zoology, computer science researchers team up for IDNR deer density study
CARBONDALE — White-tailed deer are a staple of Southern Illinois culture. Pope County celebrates with a festival dedicated to the animal, hunters of all ages skip school or work for
AI deepfakes are getting more realistic. Scientists developed a better way to spot them
Hundreds of the nation's computer scientists gathered for a conference in Denver last fall, and Tuan Tran had a big test for them: Could they tell the difference between the real Brad Pitt and an AI-generated ...
DOE Announces Science Applications For Quantum Computing
The DoE released eight applications to guide the development and evaluation of fault-tolerant quantum computers for scientific use..
Margaret Hamilton, trailblazer whose software powered Apollo 11 moon landing, dies at 90
MIT computer scientist led development of software for moon mission and earned Presidential Medal of Freedom
Quantum magic state cleanup cuts errors 40-fold in first successful hardware test
Qubits, the quantum equivalent of digital bits, are fragile and prone to errors. That makes building reliable quantum computers extremely difficult. One fix has been to use traditional magic state distillation, ...
When computer science meets the alpaca immune system
After earning bachelor's and Ph.D. degrees in bioengineering with a focus on molecular biology, Will White, a postdoctoral scholar in the Department of Computer Science at Tufts, was pretty certain his ...
Computer Science Program Ranks High For Quality And Affordability
CNU's programs in computer science, social work, and finance are ranked 3rd, 6th, and 9th best in Virginia, in Research.com’s 2027 rankings.
Owner of CT information technology consulting business sentenced for computer intrusion scheme
Federal officials said he remotely disabled a client’s computer network on 21 separate days in 2024, which resulted in the company being unable to conduct business on those days.
Google DeepMind opens its science AI models to 200 European researchers
Google DeepMind and Google Cloud will give up to 200 European researchers access to AlphaFold 3 and other science models, plus $3m in compute credits.
The ‘WarGames’ Problem: Computer Science Has Long Understood What it Takes to Keep AI Under Control
AI agents don’t go rogue. That’s something only humans do. Nevertheless, a New York Times article – representative
Nvidia vs. AMD: Which AI Business Has More Room for Margins to Fall?
What separates earnings resilience between NVIDIA Corporation (NASDAQ:NVDA) and Advanced Micro Devices, Inc. (NASDAQ:AMD)? Both sell AI compute, but a dollar of lost margin has a very different effect on their current profits. The stocks rank first and fifth in our 10 Best AI Infrastructure Stocks to Buy for the Long Term, respectively. See which […]
Intel SVP Confirms Razor Lake-AX Will Hit 700 TOPS & Launch In 2028, 3.88x The AI Compute Of Panther Lake
Intel has confirmed that its next-generation Razor Lake-AX SoCs will launch in 2028, bringing increased AI capabilities.
Quantum Meet Quantum: Hybrid Quantum Computer Lets Scientists Peak in on Foundational Quantum Effect
Oxford physicists used a hybrid quantum computer combining qubits and quantum oscillators to observe the Aharonov–Bohm effect.
Testosterone aids bone health in PWS with low behavior impact
Testosterone therapy may preserve bone health and improve body composition in males with PWS without worsening behavior, a study reports.
Argonne welcomes Chicagoland mayors to campus for meeting and tours of user facilities
Argonne hosted the Metropolitan Mayors Caucus for its quarterly meeting, where dozens of Chicagoland leaders learned about the lab’s work in super intelligence and resilience planning and toured the Advanced Photon Source and Aurora supercomputer.
Why Gartner AI Outlook Sent CoreWeave Stock, Neoclouds Reeling
CoreWeave stock plunged as shares in other AI-focused "neoclouds" also sold off after research firm Gartner predicted an industry shake-out.
Sundar Pichai: Google’s Frontier Imperative, Compute Scarcity Dilemmas, Future AI Products and Businesses
Alphabet CEO Sundar Pichai has little patience for doubts about Google’s willingness or capacity to compete at the frontier of AI. His company’s latest Gemini 4 Argon model — long-promised and recently delivered — beats OpenAI and Anthropic’s latest public models on coding, math, science, and knowledge-work benchmarks and has placed Google’s AI back among the world’s best. Amid chatter that Google was fine with good-enough lightweight ‘Flash’ models, Pichai pushed back hard against any suggestion that company is limiting its research ambitions. “We won’t do it any other way,” he told me in a Big Technology Podcast interview recorded this week at Google’s Mountain View headquarters. Pursuing cutting-edge research is costly for Google, requiring compute and chips that otherwise could be sold at a high markup by its cloud business. But Pichai said allocating resources to frontier AI development is “ground zero” for Google. “Our cloud business is at any given moment in time forgoing short-term revenues because we are making compute trade-offs to support frontier AI development,” Pichai told me. In our conversation, we discussed Argon (which Pichai says processed three times as many tokens as Gemini 3 in the 72 hours after launch), AI’s role in developing better models, the AI interface of the future, competition with OpenAI, Google’s partnerships with Anthropic and Apple, data centers in space, and the nature of his optimism. You can read the full conversation, lightly edited for length and clarity, below, or listen to it on Apple Podcasts, Spotify, YouTube, or your podcast app of choice. Alex Kantrowitz: Google is back at the frontier with Gemini 4 Argon, which beats OpenAI’s and Anthropic’s public models on benchmarks spanning coding, math, science, and knowledge work. The conventional wisdom was that Google had given up on the frontier to focus on fast, good-enough models for its products. That was wrong? Sundar Pichai: Very wrong. Look, we are so committed to being at the frontier. It’s in our DNA. We won’t do it any other way. We’ve been thinking about the AI frontier for well over a decade now. The frontier is very dynamic. It’s very competitive. It’s very exciting. I couldn’t be more pleased with how Argon has come together. We took our time with it. We made sure we tested it safely and deployed it carefully. We took a bit longer than our other models, given its capabilities. Across the board, be it for agentic work, knowledge work, coding, creative tasks, Argon is one of the really good frontier models. The first 72 hours since we launched, it’s almost up to 25 trillion tokens used, which is about three times what Gemini 3.0 had in the first 72 hours. And we are capacity constrained as we are serving it. Excited, but there are always areas….. You know, the frontier is very dynamic, and you have to keep evolving the models. There are areas which we can see we need to improve as well. We’ll be at work on all of that as well. Which areas? On any given model, Argon would be across the board better at everything if you just look even two months back. But the space is so evolving. Even on coding, for example, there are pockets where we know we need to do better, and there’s clear feedback. We’ll make it better. The argument for standard models is that they’re already good enough to build great products, with the frontier valuable only for certain tasks. Meta built Muse with good models rather than the best, and Google could put standard models into Maps, Gmail, and Search. Why isn’t that sufficient? It’s definitely not how I have thought about it at all. We are deeply committed to driving the state of the art and actually pushing where this technology can go, all the way from transformers. That’s a journey we’ve been on. You will see us continue to do that. When you’re at the frontier, you get an earlier look into the emerging capabilities, earlier than others, which helps you better prepare for the future. We are fortunate to have a wide set of opportunities across consumer and enterprise. Some of our cloud customers, for sure, want the model at the frontier. The frontier models will be better at developing the next set of models in an era of recursive self-improvement. I would argue it matters there. Internally, we have definitely seen a steep point of inflection by deploying Argon to our developers. It definitely tackles problems which our previous models could not do. Being at the frontier puts you at the innovation frontier and helps you drive that as well. There is room for models of all sizes and capabilities. That’s why we did Flash, Flash-Lite, etc. I agree with you that you can develop very good product experiences with not necessarily the most frontier model, because they tend to be slower and more expensive too. But we care about the Pareto frontier, and we’re committed to doing all of that. You said developing frontier models gives you a look at what’s coming. What do you see on the horizon? What makes this moment so exciting is you build something which you think is a culmination of months of work, and you put it out. You already see clear headroom ahead. We’ve all been working on these agentic, long-horizon capabilities, but clearly the frontier is going to have multiple of these agents working together to do things. That’s an example of an exciting paradigm underway. It’s also clear to me that users, both in consumer and enterprise, will have access to some multi-agent framework for them, working all the time doing things. It feels like a moment of infinite possibilities. Sergey Brin said one of Argon’s core innovations was relying more on the model to help develop the model. Yet incidents involving OpenAI’s bots breaking containment and Google’s bots accessing a site they shouldn’t have suggest these systems still aren’t perfectly aligned. Isn’t there reasonable cause for concern about accelerating toward recursive self-improvement? There are three parts to your question. Let’s first talk about RSI itself. You’re right, Sergey mentioned RSI being a critical contributor as we are developing Argon. But to be very clear, today, in a continuum, we are pretty far from the end state of RSI or something like that. The right way to describe it is you have RSI loops along with AI researchers in various aspects of model development. You almost have agents helping AI researchers in some automated way work together to build these models. It’s good to at least understand where we are today, and it’s super exciting. I expect that continuum to keep making progress as we go into next year. The second question is around safety. Security comes from defense in depth. You’re talking about alignment, obviously. We all are doing a lot of work to make sure the models are as aligned as possible. But it’s an active area of research, and there are clear gaps. That’s not the only thing we rely on. You have the work you do on alignment, but beyond that, we put a lot of effort into real-time monitoring as these models are working. Making sure you have good telemetry into what’s happening so that you can intervene in real time if you see unexpected behaviors. How you do that, the architecture with which you do that, how you make sure it’s all sandboxed and isolated in a way that it can continue doing its job during real-time monitoring — these are all areas of investment. Obviously, when we deploy these models, giving customers, enterprises, control, both with real-time monitoring, but their ability to decide where they deploy, what type of data which models ever have access to, etc. All that work is very, very important, and we are doing a lot of that. As an industry, we need to constantly up our game and take the time to do all this stuff, including dedicating the compute required to make sure you can safely deploy these models. The third part is: Is it all enough? This is why there are a lot of conversations in the industry, and I think it’s reasonable to be worried. The right way to approach something you worry about is early on thinking about what more we can do. As an industry, you’re definitely seeing increased levels of work on alignment, testing for safety, etc., as we deploy these models. The next step would be developing better standards so there are cross-industry standards of how we do these things, including the ability to evaluate using independent evaluators. If you look at areas like financial controls, we have accounting standards, we have independent auditors. Those are all the next natural steps for us as an industry to work toward, and regulation will end up playing a role as we go through all of that as well. Google pursues both language models and world models, while some other AI companies like OpenAI have focused more heavily on LLMs as the path to AGI. Why is world-model research important to you? As humans, you obviously perceive the world around you, and you’re taking all those inputs. Making sure these models are able to understand the laws of physics, simulate the world in high fidelity. All that, I think, are key enabling blocks for intelligence. We are working across all possible applications of this. Take an area like robotics. It’s not an accident: If you look at embodied reasoning benchmarks, Argon is state of the art. That all comes from our experience building world models. Inherently, I would expect there will be architectures which will converge all of this in the future. But for us, we view this as, today, this jagged frontier that is intelligence. We want to be at the frontier of all the possible approaches, and that will help us both drive better research, better insights, and develop a much better AGI model, is how we think about it. Some people argue Google should focus on selling its computing resources rather than using them for AI research. Cloud wants to sell compute at a high markup, while researchers need a very big computer to build the best models. How do you balance those demands? Ground zero for us is making sure we are investing in the compute that’s needed to be at the AI frontier. In 2015, I set the company to be AI-first. AI is that key horizontal ingredient across everything we do, all the way from Search to Cloud to Waymo. That is ground zero for us, making sure both for research and developing the models that we have enough compute needed. Beyond that, we have these independent businesses which each have their own long-range plans. They’re using those long-range plans to project forward, and we plan for compute for all of them. The cloud team, they have a business and they have a projection and they have demand signals. They have committed contracts. With that help, we can plan on a long-term compute trajectory for them. We’re fortunate to have this wide set of opportunities. There’s no company in the world which isn’t making trade-offs at any given moment in time. But the first principles we operate on is making sure we have enough compute to invest in the frontier, and that’s non-negotiable, and everything else works on top. Remember, we are building our own compute, our own data centers. There are long lead times. The compute we have today is a function of decisions I made in 2024. Even if you think back to a year from today, last year the agentic coding takeoff hadn’t happened. Today, obviously, when you sit and look at anything, everyone is short—not just us, other companies—and everyone wishes you had two times what you had planned for. That comes with the territory. At any given moment in time, we are planning for what we would need two to three years out in the future. You’re going to find some decisions you make may look suboptimal two years out, depending on where the state of the art is. I don’t spend too much time thinking about it. The way I think about compute allocation is making sure Google DeepMind, both for research and frontier model development, is cutting edge in terms of compute access. After that, we plan for all our businesses. It’s great we can provide our compute to other people too, like we have done. It allows us to scale that up, get real tests for it in the widest application set possible, test it in the real world, and make sure we are at the cutting edge of technology at every layer of our AI stack as well. Ben Horowitz relayed on a podcast that Andy Jassy had said compute was sold out until 2028. Are you seeing the same thing? If today a customer is coming out and asking us for compute in ’27, that is incredibly difficult to do. Even last year we went out and leased some short-term compute to accommodate some customers and help them out. But yes, we are all dealing with similar timelines. Every major technology shift seems to involve overbuilding, from railroads to fiber. Do you think that’s happening here? It depends on the time frames you’re looking at. This level of build, if I take a long enough time frame, it makes complete sense to me. We are at early stages of the most consequential technology ever, and this is not like fiber. It’s not dark. We are using it. We are capacity constrained on a daily basis. The applications, the underlying technology is improving rapidly. But running a company, the way I work through the moment is you look at your demand signals. You look at your committed revenue opportunities that you’re getting in the form of contracts. You balance that with the underlying technical capability, or internally you balance how we are using it internally, the model trajectories. Based on all that, you take a disciplined approach—a confident, aggressive, but disciplined approach—to plan out compute. But when you talk about the broader economy as a whole, you’re having people operate with various levels of risk. I focus on what I can control as a company and plan through it. If your question is, can there be an overbuild? Yes, because you can have some constrained components. Remember, what matters is a set of everything you need to build out that compute. Memory, electricity, electricians…. Land. Like 2,000 components or more. You could get constrained at some point. There could be things which cause bottlenecks, and then you could have overbuilds. There could be external shocks to the economy. There are macro risks. All that is possible at any given time. But am I confident that the compute we are planning for, we will have uses for it given enough time? Absolutely, yes. I was in the room when you said AI would be as profound as fire and electricity. If you believe that, shouldn’t you give researchers the biggest possible computer, even at the expense of today’s cloud profits? We’re all doing some form of that. Our cloud business is at any given moment in time forgoing short-term revenues because we are making compute trade-offs to support frontier AI development. What you’re seeing is true already. But you want to be thoughtful. Even our AI researchers have some constraints, which are also good. That’s what inspires creativity. That’s what helps you come up with a more efficient algorithmic breakthrough, maybe to use less memory. What we are committed to is to invest what’s needed to stay at the frontier. OpenAI President Greg Brockman told me a few months ago that the ideal AI product might have no interface at all—just as natural as conversation. What do you think the interface looks like in the age of AI? Computing historically has been humans adapting to computing. With AI, it’ll be the other way around. The technology will adapt seamlessly to how humans live and go about their day-to-day life. You want to be able to speak to that AI when you want to. It has context. It can be persistently running somewhere, give you continuity. You’re not dealing with a lot of complexity. It’s natural. All that makes sense to me. That doesn’t mean it won’t have any interface, however. Nowadays, even on my phone, Gemini voice models are so good. I can just press the microphone button. I can just ramble. That’s why we call it Rambler. I can ramble. It intelligently corrects with my personal context. Even my friends’ names, weird names, it gets it right, even if it first guesses it wrong. It’s amazing. I’m not going in, backspacing, editing once I finish saying it. It’s remarkable how good it’s gotten. But there are going to be moments you will need an interface. You may want to entertain yourself. You may want to watch a video. You may ask your AI to create something, and you want to watch it, consume it. The way I think about it is, you want it to be as seamless and as natural as possible, and that you are not doing the hard work adapting to whatever the interface is. The technology is adapting to how you’re working at any given moment in time. When I asked Sam Altman about competition from Google, he argued that OpenAI was building AI-native products and implied Google was bolting AI onto existing ones. Do you accept that framing, and can a “bolt-on” compete with an AI-native product? First of all, it’s a very dynamic moment. We are all pushing each other. OpenAI has pushed us as a company too. This is not just we are doing something to them. Far from it. Yes, Google went into its own version of a code red too. In the AI moment, all of us have to respond with varying levels of urgency at any given moment in time. We are constantly building new products as well. Gemini was built from the ground up as a product—the Gemini app I’m talking about. Or tomorrow, as all of us are adapting to agents, are you bolting agents onto ChatGPT, or are they building it natively? How would you think about that? You can always put these questions back. I don’t think of it that way. I think of it as you want to build world-class product experiences that are AI-native and they are winning. Some of it is adapting products, some of it is rethinking new experiences. They will all be part of how you approach this moment. How do you expect to make money from these AI interfaces? If an agent recommends a product and I buy it, should it take a cut of the sale, even if that’s disclosed? I’m going to put enterprise aside. Consumers are going to increasingly value this technology. Take software developers today. Developers are willing to pay for these coding models because it really helps make them more productive. Nobody would want to code the way they were coding three years ago, or even the same time last year. When you’re delivering value, there’s value to be had. I’ve never seen examples where consumer products really scale to billions of users, where people are using it intensely, and haven’t had monetization to go along with it. If you’re providing value to people, there is inherent value to be had. The nature of it may change. You’re asking a scenario in which agents are doing stuff for you. At the end of the day, you will have commercial needs. The agents need to fulfill those commercial needs. The providers on the other side are going to see value in that. There’s going to be some mechanism by which it gets fleshed out. That’s what an efficient system does. But what matters is that you’re building those world-class consumer experiences. I promised myself one product feature request: Can you make Gemini-powered search in Gmail as good as searching Gmail through ChatGPT? What’s going on here? One of the exciting things we announced recently is the unified Gemini agent for enterprise, and we’re bringing that to consumers. These are persistent agents which run 24/7 in the cloud. We’re spinning up a micro VM effectively for every query, and from the runtime, the harness, the model—it’s all a headless service. It’ll work everywhere, and it has your context with you. You can address this agent whether you’re in Workspace, in Search, in Gemini. Agents are going to drive this unification from a customer experience, which people are looking for. When you have a broad product portfolio and you’re moving forward fast, there are always areas where you’re moving forward faster than some other areas. That’s valid feedback. But we are putting a lot of time and effort into making sure, as we build out our agentic experience, that unification and that capability works well across all these services. People have asked where Google’s equivalent of Muse or Dots is. Do you anticipate creating a new product to compete with them? Our goal with Gemini is to be a personal, proactive, and powerful assistant. That’s what we are working toward. You saw us describe the early versions of it at Google I/O this year with Spark. Effectively, already, if you open the Gemini app as a Pro or an Ultra user, you see Chat and Task, and they start giving you that agentic experience. With Argon now, that capability is dramatically accelerated. What we announced recently in our enterprise event, we are bringing that to consumers as well. Soon in Gemini, you will be able to ask something, and it’s a continuum. You don’t need to think about whether you’re asking a chat or an agentic task. It’ll just automatically do the right thing, and it can work persistently in the background securely and get things done for you. Already, empirically, when I look at what’s coming into Gemini in our app, last year you would have tagged them and most of them would have been chats. But a steadily growing percentage is already agentic tasks in Gemini. Yes, we are going to bring personal agents to our consumers. It’ll work in the Gemini app. It’ll work in Google Search. It’ll work in Gmail. You can address it, call it, etc. We want to make sure we’re getting it right. It’ll be powered by Argon, and we are very excited to bring it to our users. Google has invested and committed tens of billions of dollars to Anthropic. What do you see in Dario Amodei that makes you want such a deep investment and partnership? Very early on, when we looked at the founding team of Anthropic, it was very clear to us this was a world-class team which was involved in some of the earliest GPT models. We were quickly able to assess that they would be at the frontier. We’ve always tried to identify and partner with companies which would be at the cutting edge of technology. We’ve done that with SpaceX. We’ve done that with Anthropic. That’s what we saw, and we had a chance to partner. Is your partnership with Apple to license Gemini for Siri expanding, contracting, or standing still? For both us and Apple, that partnership was about translating the capabilities of AI into better experiences for users. I thought Apple has done a phenomenal job of incorporating—they customize these models and build it into Siri—and it’s been great to see the reception. We are committed to doing more. You’re going to do more with them? There’s nothing specific to comment, but it’s a partnership. It’s working well. What have you learned so far about the feasibility of data centers in space? It’s a very exciting project. Projects like this remind me—I still recall that moment in the desert for the DARPA Grand Challenge, watching the Stanley car compete in it, which ended up being the basis for Waymo. With all these technologies, you’re at a moment in time where it looks a bit like sci-fi, but 10 to 20 years down the line, it’s actually working in a meaningful way. I feel the same in space. We’ve always believed in betting big on these technologies. Quantum has been a great example of it. We’re probably 10 years into the journey. Waymo, we are almost 20 years into the journey. Space, we are two to three years into the journey. It’s clear to me that it’s an approach which will make a lot of sense in the future. Being in sub-synchronous orbits with 24/7 access to the most abundant form of energy we have access to will make a lot of sense for this exponential increase in compute that we are going to need. Having said that, this is a challenging project. The team has disciplined milestones. It was a super exciting milestone to send the TPUs up to space. Sometime, as we are speaking, we are expecting a “hello world” moment from them. I look forward to seeing that happen, and we’ll continue investing here too. You previously said 25% of the code written within Google was written by AI. What’s the number today? That number in our world today is meaningless in the sense that every part of what we now touch is influenced by AI deeply. What we are tracking with Argon, for example, it’s been a point of inflection. People are using Argon to really go tackle complex projects, including projects which affect millions of lines of code. In the four weeks since we have launched Argon, we have seen a 30% increase in CLs, and more importantly, almost a similar increase—a one-third decrease in rollbacks. People are writing code more productively and better code with it. We are tracking it with newer metrics now. How are you dealing with opposition to data centers? If you can’t build in the U.S. the way you want to, where else do you go? We’re very committed to building our data centers very responsibly. When I look at our largest data centers in some of the communities where we have been the longest, I visit them, I talk to people. There are people who come and tell us their kids have gone to college because of our investments in these communities, and we’ve grown in those communities. To me, this is about doing it right. Engaging with the communities early, doing it transparently. We are committed to making sure we are bringing incremental power, and we cover 100% of the cost of our power needs for these projects. It’s understanding the impact on neighborhoods and making sure you’re working thoroughly to address them. Done rightly, you can continue making progress. But we are constantly taking feedback into account. I do worry not everyone operates the same way. People have been surprised by data centers showing up in their backyard. Those are all important. As an industry, we should continue to do better. But part of the reason, in a 20-year time frame, we are thinking about things like data centers in space is making sure we are able to put these data centers where maybe the trade-offs will be different. What unproven area of AI is Google working hardest to crack? Is it continual learning—getting models to keep updating after release—or something else? There are multiple parallel paths. You saw with Argon we have a million output tokens. With our Gemini models early on, we had a one-million context window on the input side. These are all examples of breakthroughs we are constantly working on. How do you think about synthetic data? How do you think about continual learning? How do you think about more efficient architectures? We do dedicate a meaningful percentage of our compute to these research bets too. It always goes hand in hand, including our world models. Those are all bets we are placing. Sometimes, as part of these bets, there are ideas that emerge which just make their way into the mainline models. Maybe we don’t fully talk about it, but we have multiple steps in these areas. Working in technology requires optimism that building something will improve the world, but those bets can leave real consequences when they fail. I recently returned to a town in El Salvador where enthusiasm for Bitcoin had given way to unfinished construction and something resembling a ghost town. How do you maintain the optimism you spoke about in your Stanford commencement address while accounting for those risks? I genuinely have a deep conviction that improving technology and improving access to technology makes people’s lives better. It’s been my personal experience in my life, out of which comes that deep conviction, optimism. But with that, I’ve always felt humanity’s journey is always about working hard to harness that technology to make it work well for society. The optimism is not an optimism that you just build whatever you want, whenever. You work hard to make sure you’re building technology in the service of humanity, to make the human condition better. That’s got to be the defining goal as we are approaching it. With that framework, I’m very, very optimistic. We are in very early stages of the kind of progress that we are going to see on many areas. We’re all talking about areas like healthcare. A lot of the progress is ahead. The ability to make things in a fundamentally cheaper way on many things where things have been getting expensive for a long, long time, discovering new materials, etc., which really bend the curve—it’s tough to step today and understand the impact of all that, but they’re going to be very profound. I’m very optimistic, but we have to be rightfully concerned about all the ways this technology can go wrong. Sundar, I know you don’t do many of these. Thank you. Thanks, Alex. Appreciate it.
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Computational Models as Liminal Models in Neuroscience, a Cognitive Science Speaker Series Presentation | Events
Speaker: Danielle Williams, Ph.D. Title: Computational Models as Liminal Models in Neuroscience Short Bio: Danielle is a philosopher of mind and science whose research examines the role of computation in our theories and explanations of the mind and brain. Her work brings together philosophy of mind, cognitive science, neuroscience, and artificial intelligence, with particular interests in computational theories of mind, computational modeling, and the relationship between different explanatory and modeling strategies in the mind sciences. She also investigates philosophical questions raised by contemporary AI, including the nature of opacity in computational models and its implications for explanation in cognitive science. Alongside this work, she is a historian of cognitive science and AI, examining how the development of computer technology has shaped our conceptions and models of the mind and brain. Abstract: Scientists rely on various types of models to make sense of their phenomena of interest. This has led philosophers to take up a range of semantic, ontological, and epistemological questions about how models represent, what kinds of things models are, what we learn from models, and how they explain. One type of model that has been of particular interest to philosophers is the computational model. Computational models are commonplace in the mind sciences in general and neuroscience in particular, with many asking how computational modeling can help us understand the nervous system and its contribution to cognition and behavior. These questions are particularly pressing in neuroscience, as computational models have become a dominant framework for making sense of cognition. Thus, old questions about model-based science have re-emerged within the context of neuroscience, with many philosophers looking to established frameworks to understand how computational models represent, explain, and contribute to theory development in neuroscience. These established frameworks, however, characterize computational models as a homogeneous type which cannot capture actual modeling practices in neuroscience. In this talk, I argue that computational models in neuroscience are best typed as a kind of liminal model, a type I introduce to capture how the status of a computational model is determined by the epistemic context in which it is deployed. Indeed, the category to which a computational model belongs shifts according to the role it plays within a given context. ASL-English interpreters have been requested. Light refreshments will be provided.