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Is Massachusetts keeping pace with the AI technology race?

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A new report out today looks at the strengths of various U.S. cities when it comes to funding, research, job creation and usage of AI technologies, and finds two California cities in the lead: San Francisco and San Jose.

That’s not a huge surprise, as those two cities are generally considered the northern and southern poles of the Silicon Valley region, which is home to companies such as OpenAI, Google, Elon Musk’s X.ai, Anthropic and Stanford University.

But Boston is ranked among 27 other metropolitan areas as a “star hub,” according to the new report from the Brookings Institution, a nonpartisan think tank based in Washington, D.C.

Those cities all have “strong AI ecosystems, balancing top‑tier talent, research and enterprise uptake” of AI software, according to the report. However, the report notes that just as the state government in Massachusetts has created a new AI hub to foster more development in the industry, so have states such as New York, New Jersey and North Dakota.

Boston scores best in the new report on factors related to our universities and our ability to pull in federal funding. We’re #1 when it comes to our population of workers with doctorates in computer science, math or engineering; #1 when it comes to cranking out research papers related to AI and #2 when it comes to attracting federal research dollars related to Research and Development (The report, incidentally, relies on data about federal funding from 2024, before President Trump took office and initiated significant cuts.)

“Federal investment cuts could be very damaging, especially to less developed areas, but also to Greater Boston,” said Mark Muro, a senior fellow at the Brookings Institution who co-authored the report, “Mapping the AI Economy.” “We think maintaining federal research flows, especially in the face of what China is doing, is very important — for the nation, and for excellent local clusters like Boston’s. The same goes for immigrant talent and foreign students.”

I sought out reactions to the report from a handful of people active in the Boston AI scene.

“Being ranked second-tier feels a bit like getting a B+ on a test you studied hard for — good effort, but you know you’ve got an A in you,” wrote Jonathan Corbin via email.

Corbin is chief executive of Maven AGI, a Boston AI startup that serves corporate customers.

“Boston attracts and generates talent from schools like Harvard, MIT and Babson, but where it falls behind is in the investments to help people stay here and build” their companies, he wrote.

Rudina Seseri of Glasswing Ventures is a venture capitalist who frequently backs AI startups. She said, “I would like to see greater investment in compute infrastructure accessible to startups and researchers, faster commercialization pathways for AI research coming out of our universities and more targeted workforce development programs — especially those that broaden access beyond traditional tech pipelines.”

Seseri added that “we would also benefit enormously from incentives to attract AI investment dollars and make it easier for local startups to pilot and scale their technologies.”

“Boston doesn’t need to copy Silicon Valley, we need to be a louder version of ourselves,” said Judah Phillips, the Boston-based Chief AI and Product Officer of Market Holdings, a New Jersey company. Among the ideas he suggests: more capital and tax incentives to grow companies here, as well as integrating AI into K-12 education, and “more apprenticeships and credential programs to give more people access to real AI jobs.”

He would also like to see the state of Massachusetts invest in AI technology from startups.

Phillips is an organizer of Boston AI Week, taking place this fall, which includes a career fair he helped start in 2024. Their goal is to organize 100 different events over the course of the week.

Paul Baier, an AI analyst who is also involved with Boston AI Week, said that “Boston has plenty of assets.” But he said that after the passage of the Mass Leads economic development bill in 2024, which proposed creating an AI Hub, “momentum seems to have slowed. I hope with the appointments of a new Secretary of Economic Development and Executive Director of the AI Hub that communications and speed increase.”

A spokesperson for the AI Hub, part of the Massachusetts Technology Collaborative, a public economic development agency primarily funded by the state, said the hub plans to spend about $50 million over the next two fiscal years.

Among its initiatives: a professional development program for high school science and technology educators, and an “AI Models Innovation Challenge” to “encourage ethical, impactful and forward-looking advancements in applied AI,” according to spokesperson Jake Stern.

The Brookings Institution report touches on the impact AI will have on jobs — especially white-collar professions. Research from Brookings suggests that roughly one-third of all workers “could see at least 50% of their occupation’s tasks disrupted by generative AI in the coming years, with higher ‘exposure’ levels for higher-skill computer and office activities,” according to the report.

Some of that disruption could create new jobs and enhance productivity, the report says, but it could also lead to “chronic under- or unemployment.”

“For sure, the nation will want to invest more in ‘active labor market policies’ that help people shift into new jobs,” the report says. This could include funding for retraining workers, as well as developing more flexible benefits that are not tied to a single employer.

Other data released this week on venture capital funding for startups also finds that funding for AI companies in California — and in particular, money raised by ChatGPT-maker OpenAI, is helping to increase that state’s funding totals, while venture capital in Massachusetts declined in the second quarter of 2025, dropping to its lowest level in eight years.

That’s not a great sign, since funding is a key ingredient for growing future crops of successful companies.

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AI-Powered Drones Help To Track Wildfire Smoke

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Researchers at the University of Minnesota Twin Cities have developed aerial robots equipped with artificial intelligence (AI) to detect, track and analyze wildfire smoke plumes. This innovation could lead to more accurate computer models that will improve air quality predictions for a wide range of pollutants.

The research was recently published in Science of the Total Environment, a peer-reviewed scientific journal.

According to a 2024 Associated Press report, 43 wildfires resulted from 50,000 prescribed burns between 2012 and 2021, creating the need for better smoke management tools. 

Previous simulation tools have worked to model fire and smoke particle behavior, but there were still limitations in accurate collection of data, modeling and the current field tools to observe these smoke plumes. In this paper, the researchers addressed the challenges in accurately modeling how smoke particles behave and spread during wildfires and prescribed burns.

“A key step is understanding the composition of smoke particles and how they disperse,” said Jiarong Hong, a professor in the University of Minnesota’s Department of Mechanical Engineering and senior author on the paper. “Smaller particles can travel farther and stay suspended longer, impacting regions far from the original fire.”

Using a swarm of AI-guided aerial robots, they were able to capture multiple angles of the smoke plumes to create 3D reconstructions of smoke plumes and analyze flow patterns. Unlike traditional drones, these aerial robots can identify smoke and navigate into it to collect data. 

“This approach allows for high-resolution data collection across large areas—at a lower cost than satellite-based tools,” said Nikil Nrishnakumar, a graduate research assistant with the Minnesota Robotics Institute at the University of Minnesota and first author of the paper. “It provides critical data for improving simulations and informing hazard response.”

The cost-effective technology has potential beyond wildfires and could be adapted for sandstorms, volcanic eruptions, and other airborne hazards. The team’s next goal is to translate the research into practical tools for early fire detection and mitigation.

“Early identification is key,” Hong said. “The sooner you can see the fire, the faster you can respond.”

Previously, the team developed an autonomous drone system, equipped with onboard computer vision and sensors to detect and track wildfire smoke plumes in real time. Building on this, they will now be focusing on efficient plume tracking and particle characterization using Digital Inline Holography with coordinated multi-drone systems. They are also working on integrating a type of drone, called a fixed-wing VTOL (Vertical Takeoff and Landing), that can take off without a runway and can fly for more than an hour for extended-range surveillance missions. 

Reference: Krishnakumar N, Sharma S, Pal SK, Hong J. 3D characterization of smoke plume dispersion using multi-view drone swarm. Sci Total Environ. 2025;980:179466. doi: 10.1016/j.scitotenv.2025.179466

This article has been republished from the following materials. Note: material may have been edited for length and content. For further information, please contact the cited source. Our press release publishing policy can be accessed here.



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How Saia moves beyond AI hype to adopt technology

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When Saia considers new technology for its business, it considers several key parameters. The LTL carrier carefully evaluates the tech investment, makes sure it delivers productivity, and fits into its customer first strategy, EVP and Chief Information Officer Rohit Lal told Trucking Dive.

“While we are extremely nimble and forward-looking, we are also careful to look beyond the hype before implementing AI, or any other new technology,” Lal said in an email.

Data, technology and new digital capabilities have caused a frenzy with new products and services, but LTL carrier Saia notes there must be a clear purpose to guide those additions.

Despite the downcycle, the carrier has invested in technology, part of a sweeping $2 billion overhaul for the company over the last five years, according to its latest annual report.

“We believe the investments we have made and continue to make in our network, technology and our people during this down cycle position us well for the future,” EVP and CFO Matthew Batteh said on a July earnings call.

Net capital spending for a technology/other line item rose to $64 million in 2024, up from $36.7 million and $33.4 million in previous years, the annual report noted.

That spending matters for Saia’s extensive network of shipments, trucks and trailers, the company noted. 

For 2025 and 2026, tech investments are focused on “three key areas that directly impact our operational efficiency and customer experience: enhanced visibility across our network, improved predictive capabilities for capacity planning, and streamlined customer interfaces,” Lal said.

Other carriers have also implemented AI. XPO and ArcBest recently noted how they’ve used it to improve route planning, and Landstar System said in May it’s investing in the technology to help stop cargo theft.

But just because a technology is available doesn’t mean Saia will pursue it, officials said. Saia didn’t list any companies that it felt were doing the opposite of its strategy.

Regardless, Saia did say that every technology investment for the carrier is “carefully evaluated and must deliver tangible results, whether that’s improving productivity or meaningfully supporting our ‘Customer First’ strategy,” Lal said. 

“What’s most important isn’t the technology, but the focus on capabilities that will compound our key differentiators of speed, reliability, and excellent customer service,” he said.

 



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Can AI really bring down healthcare costs?

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The healthcare industry is at a critical inflection point. With rising costs and growing demand, the pressure on global healthcare systems is significant. AI tools hold immense potential to transform the healthcare system, but their impact will ultimately depend on how thoughtfully and effectively it is implemented.

AI’s potential to cut costs through prevention and efficiency

While developing AI in healthcare requires significant up-front capital investment, the long-term potential for savings is significant – particularly when it comes to prevention and early detection.



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