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Emperor Musk’s AI Clothes – Will Lockett’s Newsletter

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Musk has been parading around in his AI clothes for a while now. With the amount he screams and shouts about AI, you’d think he invented it. Of course, like everything else Musk peddles, he had nothing to do with its invention or development, except for underpaying and overworking his engineers and being an awful, overpromising PR man. However, people aren’t just noticing that Musk’s clothes are non-existent — they are also starting to point and laugh at his skid marks and the “I Love the Nazi Man” tattoo down his back. Why? Because he just can’t seem to get his AI up and working. And there is no little blue pill to remedy this situation.

Take, for example, Tesla’s hilariously crap Robotaxi rollout. The media at large is only just cottoning on to it being a huge PR stunt.

I have gone on ad nauseam about why Tesla’s self-driving cars are completely inadequate, so if you want to know the details, read my previous article here. But the helicopter view is that, unlike other autonomous vehicles, Tesla’s system has zero redundancy or safety nets and requires a nearly 100% accurate AI — which categorically can’t exist — to be even remotely safe.

Tesla is painfully aware of this fatal flaw, with Tesla engineers whistleblowing their concerns about it to the media (read more here) and the DOJ opening an investigation (read more here). So I, along with countless other commentators, was pretty damn relieved to find out that Tesla’s Robotaxis had safety drivers. There was even mention of remote workers being able to take control of the car and drive it safely in the case of a critical disengagement.

But this kind of system isn’t impressive enough for Musk. Any Uber or Lyft driver with a Tesla who wastes their money on FSD can do the exact same thing. There is no social or investor kudos to be gained for Tesla or Musk here. And here is a hint: Musk doesn’t make money from Tesla sales. After all, his $50 billion pay packet (which is now less, thanks to Musk tanking Tesla’s valuation) was the equivalent of him getting $10,000 for every Tesla ever sold! Tesla makes substantially less profit from every car sold than that.

So, what do you do if you have bet your entire company’s valuation on autonomous technology that you simply can’t deliver on?

Fudge it.

Tesla put the safety driver in the passenger seat! Because, look, it’s a self-driving car — there is no one in the driver’s seat!

This is a dangerous move that offers no benefit other than optics.

Rather than being able to properly take over the car and drive it to safety, the only thing these safety drivers could do was press a button to bring the vehicle to a stop. Which, as anyone with a driving licence will tell you, is not always the safest option! Particularly when you consider that Robotaxis have been spotted driving into lanes of oncoming traffic.

Yet, this bafflingly shite decision wasn’t really reported on. Or at least it wasn’t until a video surfaced a few days ago that showed FSD failing and a safety driver being forced to exit the vehicle in the middle of traffic to take the driver’s seat and regain control. (watch it here).

This shows just how wildly dangerous Tesla’s Robotaxis are.

The safety driver had to take a serious risk to take control of the car. Not only that, but this incident suggests there are no remote operatives capable of taking over when things go wrong. That has been a core safety feature of all developing self-driving ride-hailing services, such as Waymo and Cruise, since day one and is routinely used to keep passengers safe. The fact that this is absent for Robotaxis, which Tesla already know have a far, far higher critical disengagement rate than any other self-driving ride-hailing service, could easily be seen as insanely negligent.

Musk is comfortable putting other people — not just the safety driver, but paying passengers and the public — in danger, all for a crappy PR stunt to cover up how bad his self-driving system actually is. And the media at large, as well as public consensus, are beginning to catch up to this horrifying fact.

However, Musk’s AI woes go far, far deeper than that.



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3 AI roadblocks—and how to overcome them

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Evidence of uneven AI adoption in the private sector grows by the day, with executives worried about falling behind more tech-savvy competitors. But the stakes are different, and considerably higher, in government. For local leaders, AI isn’t about winning a race. It’s about unlocking new problem-solving capacity to deliver better services and meet pressing resident needs.

Even so, city governments face real barriers to further adoption, including persistent concerns about accuracy and privacy, procurement hurdles, and too little space for civil-servant experimentation. The good news? Innovative leaders are already showing how to overcome these obstacles. And, in doing so, they’re reaping insights of use to others aiming to do the same.

Designing AI tools tailored to employees’ needs.

Boston Chief Innovation Officer Santiago Garces has no doubt that his city-hall colleagues want to push their efforts forward with AI, and he’s got the data to prove it.  His team recently conducted a survey of 600 Boston city employees and found that 78 percent of them want to further integrate the technology into their work. When asked what’s holding them back, security, accuracy, and intellectual property are among civil servants’ top concerns. 

Boston’s solution: Developing AI tools with more specific use cases, such as speeding up the procurement process, and employee concerns in mind from the start. 

Following through on a project they began last year, Garces and his team recently deployed a tool called Bitbot that can answer employees’ questions about procurement. Because it was trained on dozens of procurement documents, as well as state law, local ordinances, and city best practices, Garces argues the tool is best described not as a chatbot (though it resembles one) and more like the AI version of a handbook people know they can trust. And while the city’s randomized controlled trial of the tool’s impact is still wrapping up, Garces says the city has generally seen faster task completion and higher levels of accuracy from employees using it. At the same time, the tool is set up not to send information back to the major tech companies the way most public-facing AI tools do, which helps address employee concerns around privacy and security. 

While not every city has the resources to develop products like this on its own, Garces notes that working with university partners (he works closely with Northeastern University) can be very affordable. And this sort of approach could help civil servants everywhere be more comfortable in pushing AI use forward.

“They want the city-provided tool that they know that they can trust,” Garces explains.

Rapidly prototyping to de-risk big purchases.

When not developing bespoke AI solutions, cities turn to outside vendors. And they’re increasingly doing so with great success and impact, according to Mitchell Weiss, a Harvard Business School professor and senior advisor to the Bloomberg Harvard City Leadership Initiative. Still, adoption is uneven. “Some local leaders are wary [of making a sizeable], given broader concerns in the private sector and worries about the return on investment, ” he adds. Tight city budgets make the stakes of a misstep especially, and private-sector caution only reinforces city leaders’ hesitation.  

That’s why some cities are shaking up how they buy AI tools, both to speed that procurement process up and make sure that they stay laser-focused on boosting efficiency and effectiveness, rather than pursuing new tech for its own sake. Call it “try before you buy” for cities and AI.

Take San Antonio. Emily Royall, who until this past month worked as a senior manager for the emerging technology division in the city, helped run a rapid prototyping initiative that ensures potential AI contracts address tangible, department-level needs. The city spends up to $25,000 on three-to-six-month pilots before committing to longer-term vendor deals. The goal is to gauge impact and kick the tires first. 

Longer term, Royal and her new colleagues at the Procurement Excellence Network (she joined the team in September) believe one way cities will take their AI games to the next level is by banding together and conducting joint solicitations. And unlike traditional approaches to cooperative purchasing, cities are now determined to take a more muscular role in deciding for themselves what the most valuable AI use cases look like, and then calling on industry to develop the products that bring them to life while still meeting cities’ privacy concerns.

“This is about pooling purchasing power to deliver the outcomes that governments actually want to see from their implementation of the technology,” she says.

Leading teams toward bolder experimentation.

One of the cities leading that charge when it comes to local governments shaping the AI market is San Jose, Calif., which on Wednesday announced the first winners of its AI Incentive Program, offering grants to AI startups taking on everything from food waste to maternal health. But that’s not the only way the city is standing out. San Jose is also a model when it comes to creating a workplace where employees trust that leaders will have their backs as they constantly experiment in new ways with the technology.  

“Integrating AI into city hall isn’t just a question of expense,” explains Mai-Ling Garcia, digital practice director at the Bloomberg Center for Public Innovation at Johns Hopkins University. “It also requires that you have the political capital to spend to take risks.” 

And San Jose Mayor Matt Mahan is spending that political capital to great effect.

“He tells us it’s OK if you try something and it doesn’t work—you will not be penalized so long as there’s sufficient due diligence,” explains Stephen Caines, the city’s chief innovation officer.

But it’s not just what the mayor tells civil servants. And it’s not just the training San Jose provides through its data and AI upskilling programs, which are delivered in partnership with San Jose State University and which the mayor wants to train 1,000 more civil servants next year. It’s the larger political climate he’s cultivated to encourage AI experimentation. 

For example, the mayor presented a memo to the city council two years ago calling for the city to seize the moment and help shape (and stimulate) the emerging industry, and to integrate it across city operations. When local lawmakers voted for it, it helped clarify for everyone in city hall that pushing public-sector AI use forward wasn’t just allowed, but a key part of their job.

“I am often reminding policymakers and my colleagues that we spend probably a disproportionate amount of time focused on the technology itself or the latest hot startup versus what moves the needle the most, which is the people who will use these tools,” Mayor Mahan tells Bloomberg Cities. He adds that it isn’t just him, but city leaders across the organization who encourage experimentation with the technology. 

“How you choose to react to failure matters a tremendous amount for building culture,” Mayor Mahan, who is participating in the Bloomberg Harvard City Leadership Initiative, explains.

Among San Jose’s most concrete AI successes so far is a traffic-signal initiative that has already shown the potential to reduce resident commute times by 20 percent. And if the mayor and his team have anything to say about it, that’s just the start of not just pushing AI use forward in their city but encouraging other cities to experiment, too.

“The outdated vision of government is that we are merely consumers of technology,” Caines, the local innovation officer, explains. “The thesis that we’re putting forward is that government can be not only a lab where technology can be deployed, it can also be a valuable partner in co-creation, and we can actually serve as a market indicator by highlighting use cases that make a difference for residents.”

 



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AI in K-12 Education Market Size to Reach USD 5.24 Billion

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Austin, Sept. 04, 2025 (GLOBE NEWSWIRE) — The AI in K-12 Education Market size was valued at USD 0.37 billion in 2024 and is projected to reach USD 5.24 billion by 2032, expanding at a CAGR of 39.29% over 2025-2032.

Growth is driven by the increasing adoption of AI-powered learning tools, personalized education platforms, and intelligent tutoring systems that enhance student engagement and learning outcomes. Schools are leveraging AI for real-time performance tracking, adaptive assessments, and administrative automation. Additionally, rising digital literacy, government initiatives promoting EdTech, and the integration of AI in online and hybrid learning environments are accelerating market expansion across the K-12 education segment globally.


Download PDF Sample of AI in K-12 Education Market @ https://www.snsinsider.com/sample-request/8071 

In the U.S., the AI in K-12 Education Market was valued at USD 0.11 billion in 2024 and is projected to reach USD 1.51 billion by 2032, growing at a CAGR of 39.05%. Growth is driven by rising EdTech investments, adoption of AI tutors, personalized learning solutions, and classroom analytics that enhance student engagement, learning outcomes, and operational efficiency in schools.

Key Players:

  • IBM
  • Google (Google for Education)
  • Microsoft (Microsoft Education, Azure AI)
  • Amazon Web Services (AWS Education AI)
  • Carnegie Learning
  • Pearson
  • DreamBox Learning
  • Knewton
  • Century Tech
  • Nuance Communications
  • Blackboard
  • Knewton Alta (by Wiley)
  • Cognii
  • Querium
  • Squirrel AI
  • Jenzabar
  • Duolingo
  • Khan Academy
  • Altitude Learning
  • Osmo (by BYJU’S)

AI in K-12 Education Market Report Scope:

Report Attributes Details
Market Size in 2024 USD 0.37 Billion
Market Size by 2032 USD 5.24 Billion
CAGR CAGR of 39.29% From 2025 to 2032
Base Year 2024
Forecast Period 2025-2032
Historical Data 2021-2023
Report Scope & Coverage Market Size, Segments Analysis, Competitive  Landscape, Regional Analysis, DROC & SWOT Analysis, Forecast Outlook
Key Segments • By Component (Solution, Services)
• By Deployment (Cloud, On-premises)
• By Technology (Natural Language Processing (NLP), Machine Learning)
• By Application (Learning Platform & Virtual Facilitators, Intelligent Tutoring System (ITS), Smart Content, Fraud and Risk Management, Others — Administrative automation, Special education support tools)
Customization Scope Available upon request
Pricing Available upon request

If You Need Any Customization on AI in K-12 Education Market Report, Inquire Now @ https://www.snsinsider.com/enquiry/8071 

By Component, Solution Segment Leads AI in K–12 Education Market in 2024 with 71% Revenue Share

In 2024, the solution segment dominated the AI in K–12 Education Market, capturing 71% of revenue. Its leadership is fueled by extensive deployment of AI-enabled platforms, including intelligent tutoring systems, adaptive learning tools, and learning management systems (LMS). Continuous investment in digital curriculum and AI-based learning platforms ensures the solution segment remains the primary driver of market growth, supporting enhanced student engagement and personalized learning experiences.

By Deployment, Cloud Segment Leads AI in K–12 Education Market in 2024 with 69% Share

In 2024, the cloud segment dominated the AI in K–12 Education Market, capturing 69% of the share. Its leadership is driven by scalability, cost efficiency, and easy access across devices. The growing demand for centralized AI-integrated learning platforms and cloud-based learning management systems (LMS) further strengthens the cloud segment’s role in transforming digital education and enhancing personalized learning experiences.

By Technology, Machine Learning Segment Leads AI in K–12 Education Market in 2024

In 2024, the machine learning segment dominated the AI in K–12 Education Market with a significant revenue share. Its leadership is driven by extensive applications in adaptive learning, predictive analytics, and student performance tracking. The growing personalization of algorithms and automation of student progress monitoring reinforce machine learning’s central role in enhancing learning outcomes and educational efficiency.

By Application, Learning Platform & Virtual Facilitators Segment Leads AI in K–12 Education Market in 2024

In 2024, the learning platform & virtual facilitators segment dominated the AI in K–12 Education Market, capturing a major revenue share. Its growth is driven by widespread adoption of virtual assistants, learning management systems (LMS), and remote teaching platforms. The increasing use of hybrid learning environments and virtual classroom support tools further reinforces this segment’s position as the primary contributor to market expansion.

North America Leads AI in K–12 Education Market in 2024, Asia Pacific is Projected to Record the Fastest CAGR

In 2024, North America dominated the AI in K–12 Education Market, holding the highest revenue share. The region’s leadership is fueled by early adoption of EdTech solutions, strong funding support, high digital literacy, and widespread integration of AI-driven curricula across schools, establishing it as a benchmark in educational technology deployment.

The Asia Pacific region is expected to achieve the fastest growth in the AI in K–12 Education Market. Expansion is driven by government-led digital education programs, rising private sector investments, and increasing student populations. Countries including India, China, and Indonesia are adopting AI-powered, inclusive, and scalable education models, fueling rapid market adoption and digital transformation across the region.

Buy Full Research Report on AI in K-12 Education Market 2025-2032 @ https://www.snsinsider.com/checkout/8071 

Exclusive Sections of the Report (The USPs) – Check Section 5

  • USP 1 – AI Adoption & Readiness by Region and School Type

Helps clients identify regions and school segments with the highest potential for AI-based learning tools deployment.

  • USP 2 – Curriculum Enhancement & Personalized Learning Insights

Provides guidance on how AI can tailor learning paths, adaptive assessments, and student engagement strategies.

  • USP 3 – Teacher Productivity & Classroom Automation Analysis

Shows clients how AI solutions can automate grading, administrative tasks, and performance tracking to free teacher bandwidth.

  • USP 4 – Student Performance & Learning Outcome Analytics

Offers insights into improving student outcomes through predictive analytics, skill-gap detection, and learning trend analysis.

  • USP 5 – EdTech Integration & Platform Compatibility Assessment

Helps clients integrate AI tools with existing LMS, e-learning platforms, and digital classrooms for seamless adoption.

  • USP 6 – Regulatory & Data Privacy Compliance (FERPA, GDPR, COPPA)

Ensures AI tools meet data privacy and safety regulations for children, minimizing legal and reputational risks.

  • USP 7 – Future Trends & Innovation Roadmap

Prepares clients for emerging applications such as AI tutors, gamified learning, AR/VR classrooms, and real-time feedback systems.

About Us:

SNS Insider is one of the leading market research and consulting agencies that dominates the market research industry globally. Our company’s aim is to give clients the knowledge they require in order to function in changing circumstances. In order to give you current, accurate market data, consumer insights, and opinions so that you can make decisions with confidence, we employ a variety of techniques, including surveys, video talks, and focus groups around the world.


            



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A Boulder Creek tech startup is using drones and AI to detect the next big wildfire

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Quick Take

After the CZU fire devastated their community, a group of Boulder Creek residents, firefighters and engineers began meeting regularly to figure out how to better protect their homes and neighbors from future fires. Their solution, an AI-powered system of sensors, cameras and drones, joins a growing wave of technology aimed at taming California’s intensifying fire seasons.

What began as weekly meetings between Boulder Creek residents and local firefighters outside the Brookdale Lodge in the months following the CZU Lightning Complex Fire has evolved five years after the blaze into a pioneering wildfire detection system that could transform how communities respond to fire threats. 

Their creation: a network of artificial intelligence-powered sensors that scan for smoke every minute and alert both residents and emergency services to the early signs of a blaze, paired with autonomous drones that can deliver fire suppressant to remote locations.

The startup behind this technology, Ember Flash Aerospace, emerged from conversations between Lee Kohlman, a NASA Ames Research Center employee who was living in Boulder Creek when the CZU fire hit, and Joseph Norris, a former Coast Guard member with experience in software and driverless cars, who traveled to Silicon Valley frequently for work. Along with local firefighters and tech-savvy neighbors, Kohlman and Norris identified a critical gap in wildfire response: the need for faster detection in those crucial first minutes when a small fire can still be contained.

The company’s Vigilant Raptor is an autonomous drone that can quickly navigate into hard-to-reach areas to dispense fire suppressant. Credit: Ember Flash

Ember Flash joins an increasingly crowded field of new technology that is reshaping the fight against wildfires. Researchers, governments and the private sector are racing to detect blazes earlier and stop them before they spread using tools like sensors, satellites and artificial intelligence to identify the lightning strikes most likely to ignite fires, or detect the earliest whispers of smoke. Private companies are also experimenting with autonomous helicopters, smart sprinkler systems and mobile water delivery to help battle blazes as hotter, drier conditions fuel larger fires.

The growing frequency and intensity of wildfires prompted XPrize — a nonprofit-run competition program that offers millions of dollars in prize money to entrepreneurs to solve big problems through technology — to launch a new challenge focused on wildfires in 2023. 

“XPrize is a California-based organization and so many of our staff, sponsors, board members have experienced destructive wildfires so this was both a personal challenge for many as well as a global challenge,” said Andrea Santy, program director for the new XPrize Wildfire contest.  

Ember Flash was named a semifinalist in the XPrize Wildfire competition. The winners stand to make as much as $3.5 million when they’re announced in 2026. Finals testing is scheduled for July of next year. 

From the ashes of CZU

For Norris, Kohlman and their colleagues, pairing Silicon Valley technology with wildfire fighting seemed like a no-brainer.

Kohlman and his wife had been living in Boulder Creek for about a year when the fire broke out. The couple had moved from Virginia to take jobs at the Lucile Packard Children’s Hospital at Stanford University and NASA’s Ames Research Center. When lightning storms happened on Aug. 16, 2020, Kohlman didn’t think much of it at first; as a Midwest native, he and his wife were used to it, even if it was a little abnormal for California. It wasn’t until the next day that they heard about a potential fire, but he wasn’t too worried. Then his wife received a notification on her phone and called him from work, and he stepped outside to look. 

“I saw an orange glow to the northwest, just a couple of ridges over,” he recalled. “We had to quickly figure out what to do next.” 

A sign points the way to Boulder Creek amid the CZU Lightning Complex wildfire in 2020. Credit: Kevin Painchaud / Lookout Santa Cruz

He gathered the couple’s two dogs and two cats and packed a few bags. He and his wife decamped for a short-term rental on Foster City, where they’d end up staying for six weeks. For an agonizingly long time, they weren’t sure if they’d have a home to return to. Their house survived the blaze, but they came back to a forever-changed community. 

In those weeks and months after the fire, Kohlman spent a lot of time taking photographs of burnt buildings and trees to get a clearer picture of the fire’s extensive damage. He spoke with neighbors about their experiences with the CZU Lightning Complex, including those who’d lost their homes. He felt helpless and wanted to change that. So about six months after the fire, he put a call out on a Facebook page for Boulder Creek neighbors, looking for other people to discuss their experience and what had happened — everything from how they found out about the fire, to how they felt going through this experience, and what the rebuilding process would entail. 

A group of residents, including several local firefighters, met up and then kept meeting weekly, with topics ranging from community recovery to how wildfires are fought. Kohlman invited his friend and former neighbor, Joseph Norris, to attend. 

“We started having some little meetings,” said Norris. “Our first meeting was at the Brookdale Lodge at the picnic tables outside. We weren’t thinking about much more than what could be done, ‘How can we help others do more?’”

For Norris, who started his career in the Coast Guard, emergency response had long been an interest. He’d been a volunteer firefighter in his younger years before getting into software, working for companies focused on everything from software for nonprofit organizations to artificial intelligence and driverless cars. He and Kohlman befriended each other when they were both living in Virginia, and the pair had long talked about possible business ideas.

“There were a lot of people in Boulder Creek who had a lot of skills and wanted to do something [in the wake of the fire],” said Norris. “We felt like we were way too close to Silicon Valley to not be using a little technology for this.”

As the group discussed barriers to response, such as being located in rural areas where homes are farther apart, and current firefighting approaches, a key challenge emerged: the need to speed up wildfire detection. 

“Those first few minutes are really important, when [a fire is] small, just ignited,” said Norris. “If you can get to those quickly and put them out, you can stop a wildfire.”

‘New era in megafires’

Fighting fires in rural, often rugged terrain is complicated and becoming harder. Fires like the CZU Lightning Complex are fast-moving and can leap from one place to another, faster than a car can drive, wildfire experts say. 

California has been in a “new era of megafires” for about a decade, driven by factors including climate change, buildup of dry brush and dead leaves, and the fact that more people now live in wildfire-prone areas, said Chris Field, a professor at Stanford University whose research focuses on climate change and the impacts of wildfires.

Even for experienced firefighters, these blazes are incredibly challenging. 

“California has some of the most sophisticated and well-trained firefighters,” Field said. “It’s not that we don’t have the people [with skills] – it’s that these fires are getting more intense and harder to fight, which is where new technologies can come in.” 

Earlier notifications could also help residents make better preparations in the event of a fire, such as asking a neighbor to pick up your pets for you rather than driving back to Boulder Creek from your job in Santa Cruz, said Norris. 

Statewide, California’s evacuation alert systems came under scrutiny following fires throughout the summer of 2020. As the Associated Press reported, notifications were sometimes spotty or slow, and some residents said they never received them. Santa Cruz County leaders launched a new and improved alert and warning system, CruzAware, in 2023, driven in part by the lessons learned from the CZU Lightning Complex Fire. 

Even with upgraded warning systems, gaps remain; systems vary by region, phone alerts can be ignored as robocalls, and sometimes alerts still come too late for residents to return home to gather their belongings before evacuating. 

Ember Flash is combining cameras and AI technology to create special sensors that can detect wildfire smoke and issue alerts for residents and emergency services. Credit: Ember Flash Aerospace

To help solve these problems, Ember Flash developed Vigilant Detect, which essentially involves special sensors that combine advanced cameras and AI. The cameras can capture images every minute and the system then uses AI to analyze those images in real time for early signs of wildfire smoke. The idea is that these monitors could be installed in neighborhoods and Firewise communities, groups of neighbors who’ve come together to reduce wildfire risks in the community. Property owners and emergency service agencies could then receive notifications through an app when the system detects a wildfire. 

The company is currently doing pilot programs in a few sites in Santa Cruz County and other fire-prone areas, including residential neighborhoods, municipalities and local vineyards, Norris said. The Ember Flash team works on site to identify the best locations for the sensors and ensure everything runs smoothly. These tests help the company refine its detection algorithms, test the durability of the hardware and the product design in real-world conditions while also collecting feedback about the user experience. Ember Flash is looking for more property owners interested in joining its pilot program.

Norris said the company expects to be able to start selling their products to neighborhood associations and fire protection districts next year.

Vigilant Detect isn’t the only project the company’s working on. It’s also working with Dutch company Kitepower to mount Ember Flash’s AI-powered smoke-sensing devices on Kitepower’s high-tech kites that generate energy from wind. The idea there is that these kites could be flown into remote, windy places to detect wildfires while they’re still just wisps of smoke. 

Another effort is using drone technology for both gathering real-time data about existing fires and rapidly helping to put them out. The company’s Vigilant Raptor is an autonomous drone that can quickly navigate into hard-to-reach areas to dispense fire suppressant. These could be owned and used by private citizens, but Norris said the company envisions fire protection districts (the local government entities responsible for fire prevention and suppression in a specific geographic area) as the primary customers for these devices. 

“Our goal is to make them inexpensive, make them smart and make them fast,” Norris said of the company’s products. 

Eyes on the XPrize

The company’s Vigilant Raptor is currently a semifinalist in the XPrize Wildfire competition, in which teams around the world are competing in two different tracks to transform how wildfires are detected, managed and fought. 

The competition is divided into two tracks, space-based detection and intelligence and autonomous wildfire response, according to Santy. From more than 150 submissions, 49 teams were selected to advance and in 2025, semifinals testing began. 

Ember Flash was one of 15 semifinalists in the intelligence and autonomous wildfire response category and it now moves on to finals testing, which is slated for July 2026. The company will have to demonstrate to the contest’s judges that within 10 minutes, it can autonomously detect and suppress a high-risk fire in a 621-square-mile, environmentally challenging area. 

“The competition kind of kicked us into a slightly different route,” Norris said. “It kicked our suppression efforts into high gear.”

While the Kohlmans have since moved back to their native Ohio, Ember Flash Aerospace remains firmly rooted in Boulder Creek. Its local team members include Zach Ackemann, a former Felton firefighter who now runs the company’s business operations, and Steve Lindsey-Guerrero, a deputy fire chief in Palo Alto who serves as a technical expert.  In addition to the San Lorenzo Valley, the company also has an engineering team in Oklahoma. 

So far, Ember Flash has been funded by investments from its founding team and early employees, but company leaders said they’re also looking for investors and funding to help them scale.

For Kohlman and Norris, Ember Flash is now their full-time job, and both spend a lot of time in Santa Cruz County as testing, prototyping and development continue, with pilot programs underway. Involving community members to test out their products is a key part of the company’s efforts and harkens back to the conversations that inspired Ember Flash in the first place – giving residents a way to help out with efforts to improve wildfire notification and response. 

“We really want to empower them to be part of the solution,” Kohlman said of community members and non-firefighters. “People want to help. If a lot of people do a lot of small things, it can make a big difference.”

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