In late 2022, when generative AI tools landed in students’ hands, classrooms changed almost overnight. Essays written by algorithms appeared in inboxes. Lesson plans suddenly felt outdated. And across the country, schools asked the same questions: How do we respond — and what comes next?
Some educators saw AI as a threat that enables cheating and undermines traditional teaching. Others viewed it as a transformative tool. But a growing number are charting a different path entirely: teaching students to work with AI critically and creatively while building essential literacy skills.
The challenge isn’t just about introducing new technology. It’s about reimagining what learning looks like when AI is part of the equation. How do teachers create assignments that can’t be easily outsourced to generative AI tools? How do elementary students learn to question AI-generated content? And how do educators integrate these tools without losing sight of creativity, critical thinking and human connection?
Recently, EdSurge spoke with three educators who are tackling these questions head-on: Liz Voci, an instructional technology specialist at an elementary school; Pam Amendola, a high school English teacher who reimagined her Macbeth unit to include AI; and Brandie Wright, who teaches fifth and sixth graders at a microschool, integrating AI into lessons on sustainability.
EdSurge: What led you to integrate AI into your teaching?
Amendola: When OpenAI’s ChatGPT burst onto the scene in November 2022, it upended education and sent teachers scrambling. Students were suddenly using AI to complete assignments. Many students thought, Why should I complete a worksheet when AI can do it for me? Why write a discussion post when AI can do it better and faster?
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Our education system was built for an industrial age, but we now live in a technological age where tasks are completed rapidly. Learning at school should be a time of discovery, but education remains stuck in the past. We are in a place I call the in between. In this place, I discovered a need to educate students on AI literacy alongside the themes and structure of the English language.
I reimagined my Macbeth unit to integrate AI with traditional learning methods. I taught Acts I-III using time-tested approaches, building knowledge of both Shakespeare and AI into each act. In Act IV, students recreated their assigned scenes using generative AI to make an original movie. For Act V, they used block-based programming to have robots act out their scenes. My assessment had nothing to do with writing an essay, so it was uncheatable. I encouraged students to work with me to design the lesson so I could determine the best way to help them learn.
Voci: Last fall, I was in a literacy meeting with administrators and teachers where I heard concerns about the new science of reading materials not engaging students’ interest. While the books were highly accessible, students had no interest in reading them. This was my lightbulb moment. If we could use AI tools to develop engaging and accessible reading passages for students, we could also teach foundational AI literacy skills at the same time.
This is where The Perfect Book Project was born. Students work with teachers to develop their own perfect reading book that is both engaging and accessible, learning literary skills alongside how to work with and evaluate AI-generated content. In its pilot, I worked directly with teachers as students conceptualized, drafted, edited and published their books. I spent hundreds of hours creating prompts with content guardrails, accessibility constraints and research-based foundational literacy knowledge to guide students and teachers through the process.
Wright: I’m doing quite a bit of work around the U.N. Sustainable Development Goals, teaching our explorers the impact of our actions not just on ourselves but also on others and the environment. I wanted to see them use AI to deepen their knowledge and serve as a thought partner as they develop solutions to issues like climate change.
I created a lesson called “Investigating Energy Efficiency and Sustainability in Our Spaces.” The explorers went on a sustainability scavenger hunt around campus to find examples of energy-efficient items and sustainable practices. They used AI tools to analyze their findings, interpret and evaluate AI responses for accuracy and potential bias, and reflect on how technology and human decisions work together to create sustainable solutions. The AI in this lesson wasn’t about the tools they used, but more about how AI is viewed in the context of what they are learning.
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What shifts in student learning did you observe?
Voci: One eye-opening moment was during my first lesson on hallucinations and bias with a third grade class. After introducing the concepts at a developmentally appropriate level, I had them reread their manuscripts through the lens of an AI hallucination and bias detective. It didn’t take long for the first student to find the first hallucination. There was incorrect scoring in a football game. AI counted a touchdown as one point. One student’s hand flew up; he was so excited to explain to me and the class how the model had incorrectly scored the game.
This discovery lit a fire under the rest of the class to begin looking more closely at every word of their text and not take it at face value. The class went on to find more hallucinations and discover some generalizations that did not represent their intentions.
Wright: I saw the explorers develop their critical thinking as they asked questions about how AI was used, how AI makes its decisions and whether this affects the environment. I truly appreciate that this age group holds onto their creativity and imagination. They don’t want AI to do the creating for them. They still want to draw their own pictures and tell their own stories.
Amendola: It was uncomfortable for my honors students to try something new. They were out of their element and craved the structure of the rubric. I had to let go of traditional grading structures first before I could help them embrace the ambiguity. Their willingness to explore and make mistakes was wonderful. The collaboration helped create a sense of class community that resulted in learning a new skill.
What’s your advice for educators hesitant to explore AI?
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Amendola: Don’t be afraid to try new things. Keep in mind that the greatest success first requires a change of mindset. Only then can you open the doors to what generative AI can do for your students.
Voci: Don’t let the fear, weight and speed of AI advancement paralyze you. Find small, intentional steps that are grounded in human-centered values to move forward with your own knowledge, and then find ways to connect your new knowledge to support student learning. In this age of AI, we need to give our fellow educators the same resources, scaffolding and grace.
Wright: Jump in!
Join the movement at https://generationai.org to participate in our ongoing exploration of how we can harness AI’s potential to create more engaging and transformative learning experiences for all students.
People without coding backgrounds are discovering that they can build their own custom apps using vibe coding — solutions like Lovable that turn plain-language descriptions into working code.
While these prompt-to-code tools can help create nice prototypes, launching them into full-scale production (as this reporter recently discovered) can be tricky without figuring out how to connect the application with external tech services, such as those that can send text messages via SMS, email, and process Stripe payments.
Ilan Zerbib, who spent five years as Shopify’s director of engineering for payments, is building a solution that could eliminate these back-end infrastructure headaches for nontechnical creators.
Last summer, Zerbib launched Sapiom, a startup developing the financial layer that allows AI agents to securely purchase and access software, APIs, data, and compute — essentially creating a payment system that lets AI automatically buy the services it needs.
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Every time an AI agent connects to an external tool like Twilio for SMS, it requires authentication and a micro-payment. Sapiom’s goal is to make this whole process seamless, letting the AI agent decide what to buy and when without human intervention.
“In the future, apps are going to consume services which require payments.Right now, there’s no easy way for agents to actually access all of that,” said Amit Kumar, a partner at Accel.
Kumar has met with dozens of startups in the AI payments space, but he believes Zerbib’s focus on the financial layer for enterprises, rather than consumers, is what’s truly needed to make AI agents work. That’s why Accel is leading Sapiom’s $15 million seed round, with participation from Okta Ventures, Gradient Ventures, Array Ventures, Menlo Ventures, Anthropic, and Coinbase Ventures.
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“If you really think about it, every API call is a payment. Every time you send a text message, it’s a payment. Every time you spin up a server for AWS, it’s a payment,” Kumar told TechCrunch.
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While it’s still early days for Sapiom, the startup hopes that its infrastructure solution will be adopted by vibe-coding companies and other companies creating AI agents that will eventually be tasked with doing many things on their own.
For example, anyone who has vibe-coded an app with SMS capabilities won’t have to manually sign up for Twilio, add a credit card, and copy an API key into their code. Instead, Sapiom handles all of that in the background, and the person building the micro-app will be charged for Twilio’s services as a pass-through fee by Lovable, Bolt, or another vibe-coding platform.
While Sapiom is currently focused on B2B solutions, its technology could eventually empower personal AI agents to handle consumer transactions. The expectation is that individuals will one day trust agents to make independent financial decisions, such as ordering an Uber or shopping on Amazon. While that future is exciting, Zerbib believes that AI won’t magically make people buy more things, which is why he’s focusing on creating financial layers for businesses instead.
Editor’s note: GeekWire publishes guest opinions to foster informed discussion and highlight a diversity of perspectives on issues shaping the tech and startup community. If you’re interested in submitting a guest column, email us at tips@geekwire.com. Submissions are reviewed by our editorial team for relevance and editorial standards.
Ben Golden.
I’m an attorney and advisor to many Pacific Northwest startups, investors, and social entrepreneurs, having spent the past two decades in the Washington innovation ecosystem — including as a higher education policy advocate and former co-chair of the WTIA Policy Committee. I love helping transform great ideas into job-creating companies in my community.
Which is why I’m unmoved by the panic surrounding the proposed “millionaires tax.” Every time Olympia proposes that our wealthiest contribute more, we’re told that this is the final straw for our brightest risk-takers, an existential threat to our state’s economy. But the real threat to the startup community is losing focus on building up our strengths as this catastrophizing becomes a self-fulfilling prophecy.
America is at a crossroads. In this defining moment, when our duties as citizens are gravely needed, a growing chorus of local startup luminaries are speaking up. Which issue galvanizes them? Civil liberties, or climate, or gilded age cronyism, or divestment from public interest research, or immigration, or the dignity of work amidst AI disruption, or freedom of speech…?
Disappointingly, much of the startup community’s advocacy efforts have instead been singularly focused on preventing a few very wealthy folks from changing their primary residence to Las Vegas or Jackson Hole or Palm Beach.
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My oh my, what an uninspired civic imagination in this moment of peril. We can do better.
So chill with the libertarian fever dream. Read the moment. And read the proposal’s fine print, including important small business tax cuts. And remember what’s made Seattle such a dynamic startup community in the first place.
The tax proposal is (probably) not going to take your money
This is a proposed tax on net income over $1 million in a single year. The first $1 million of income would be exempt. This point merits emphasis, as it’s often misunderstood: no one will pay a penny of tax on the first $0 to $999,999 of annual net income. There are additional carve outs and deductions to encourage charitable giving and avoid double taxation. The minimum threshold will be indexed upward with inflation. And the proposed tax would not begin collecting revenue until 2029, allowing plenty of time to work through rulemaking, legal challenges, and fine tuning.
If enacted as proposed, less than 0.5% of households would ever be impacted. Imagine 1,000 random Washingtonians in a room: you could count on one hand the number of people with enough luck, talent, and timing to ever pay this tax.
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What about founders and investors? Many will already benefit tremendously from federal tax advantages like QSBS, which can eliminate up to $10 million in federal capital gains taxes on a successful exit. (An unrelated proposal would apply the state’s capital gains tax on profits that are otherwise exempted from federal taxation; with only a handful of sponsors across both chambers, that proposal appears to have far less traction.)
Further, the same tax avoidance strategies they already deploy, such as staggered sales, deferred compensation, trust and estate planning, and real estate tax shelter investments, will continue to reduce taxes for founders and investors. The idea that a modest state tax on seven-figure net income is going to make entrepreneurship suddenly “not pencil out” is fuzzy math.
Fixing Washington’s regressive tax structure is good for business
Washington consistently ranks among the most regressive tax systems in the country. Relative to other states, lower- and middle-income families pay a disproportionate share of their income in state and local taxes due to our heavy reliance on sales, excise, and business taxes. Addressing this problem is essential to building a resilient state, which matters more than ever in this moment of increasingly reckless and unstable federal governance.
In announcing his initial support for this proposal, Gov. Bob Ferguson tied the tax explicitly to strengthening the Working Families Tax Credit, removing sales taxes on essential personal hygiene products, investing in K-12 education, and greatly reducing B&O taxes for early-stage businesses. In other words, this is a pro-entrepreneurship policy that argues that we’re all better off when we’re all better off.
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Of particular importance for startups, the proposal to provide B&O tax relief for small businesses would be a boon for early-stage companies in their earliest cash constrained years, i.e., when they need it most. The current draft legislation would provide a credit for B&O taxes on annual gross receipts less than $250,000, which would benefit thousands of local startups and small businesses every year. Meanwhile, Ferguson has called to go further by zeroing out B&O taxes up to $1 million on revenue.
In responding to the initial proposal, the governor said his ultimate support for the proposal is contingent on a much more aggressive small business tax break — “we need to have the largest tax break for small business owners in state history,” he said this week.
Rather than fear-monger, startup advocates should redirect their efforts toward supporting that effort for targeted savings for early-stage companies.
The Legislative Building in Olympia, Wash. (GeekWire Photo / Lisa Stiffler)
On the pro-millionaire advocates’ counterpoints
There are valid concerns about the proposal’s impact on the business climate and economic growth.
Some argue it “punishes success” by not maximizing exit proceeds. Yet this ignores how the proposal invests in conditions that allow startups to thrive in the first place as well as the urgency of addressing a broken tax system.
A frequent rebuttal to any tax proposal is that the state should cut spending instead. Absolutely, there must be accountability and responsible stewardship of our public resources. But this is not mutually exclusive; as in business, governments can manage their expenses and restructure revenue at the same time.
Critics warn that the income tax minimum threshold will expand in future years. Rep. Jeremie Dufault, R-Selah, calls it “kicking a budget snowball down a hillside. It’s small now, but it will grow as it rolls.” Maybe, but that’s not the proposal under consideration right now. In fact, the current proposal would raise the minimum threshold annually with inflation.
There are also legitimate legal hurdles to implementing the proposed policy. Fortunately, we have multiple branches of government. Jurisprudential ambiguity should not deter legislators from passing policies they deem in the best interest of the electorate.
Large tech companies are downsizing, particularly amongst software engineering teams. Our fizzling “prosperity bomb” is bad news for a local economy supported by so many coders, and those AI-disrupted jobs are not being replaced elsewhere. In this moment of disruption, creating policies that make it easier to be an entrepreneur and live comfortably in a community are more important than ever, regardless of whether a household brings in millions of dollars a year.
Many point to capital flight as the primary concern, though correlation and causation can be muddled on this point. A handful of large tech companies and wealthy individuals have moved operations out of Washington state, and there will likely be a few more (vocal) high net-worth households who will register their primary residence elsewhere to reduce their tax bill — and they may even shift the focus of their investments from local startups to their new neighbors. But the primary cause of capital flight risk is panic; most people do not move to escape tax increases. This tax on outsized annual incomes will not trigger economic ruin, but the outsized investor-class alarm could cause real harm.
Rather than catastrophize, the startup community ought to celebrate the opportunities that would be unlocked by relieving early-stage businesses of B&O taxes, modestly rebalancing our regressive tax structure, and making targeted investments to keep Washington affordable and thriving.
The bill is currently open for debate, and critical details remain to be finalized. The startup community should be in these negotiations, rather than adopting an out-of-touch absolutist approach that reduces their influence and credibility.
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Entrepreneurs will build here because we bet on ourselves
Entrepreneurs want to build something from nothing, test ideas, prove their doubters wrong, and ultimately solve problems. And sure, they want to make loads of money. Their ambition to build, ideate, prove, and solve will not be quashed by a tax that only kicks in after annual net income over $1 million.
Most creative, ambitious, and educated people are not primarily motivated by marginal tax rate optimization. They want to live in places with access to world-class universities, vibrant cultural and artistic ecosystems, reproductive health care, diverse neighbors, multimodal transportation, LGBTQ+ rights, respect for the natural environment, libraries that don’t ban books, and a basic sense that society has their back.
The best places in the country to launch a startup include the Bay Area, Boston, New York, and the greater Seattle area. With apologies to the fine folks in Sioux Falls, Houston, and Anchorage (the least taxed large U.S. cities), it turns out startups thrive in communities that invest in themselves and their people. We’ve done that in the Pacific Northwest and are set up for success. Millionaires tax or no tax, the next generation of great companies and scrappy entrepreneurs are primed to emerge from AI House, CoMotion, Foundations, 9Zero, and across our great state.
At the end of day, most of the loudest critics of this proposal — people I respect and work with daily — will almost certainly continue to live and work here in Washington state. So let’s cool it on the millionaires tax hysteria, recognize the criticality of the moment, and bet on ourselves.
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Disclaimer: Written in my personal capacity. I’m no startup Lorax — I do not speak for my clients.
Robotics specialist Unitree has been making waves with its humanoid robots, and a new video shows its impressive G1 bipedal bot dealing with incredibly cold conditions.
In a video showing the G1 trudging through deep snow, Unitree describes the feat as “the world‘s first autonomous walking challenge for humanoid robots in a -53.32°F (-47.4°C) extreme weather environment.”
The stunt took place in China’s Altay region, about 1,500 miles (2,400 km) northwest of Beijing, where Unitree’s snowbot trudged through deep snow to mark out the Olympic rings in celebration of Friday’s Winter Olympics opening in Italy.
It’s not clear how long the robot walked for, or how many times its battery needed to be swapped out, but during the course of its sub-zero slog it managed to create an image 100 meters wide and 186 meters long.
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Still, the fact that it managed to function at all in such frigid conditions is clearly impressive. Icy temperatures can freeze batteries, stiffen joints, or shut down electronics, but the G1, apparently assisted by its puffer jacket that possibly came with some internal heating, managed to stay alive in the challenging setting. The successful demonstration offers a glimpse at how the G1, or robots like it, could one day be deployed for tasks like search and rescue in polar environments, or even operate in faraway places like Mars where average temperatures reach around -76°F (-60°C).
China-based Unitree has emerged as one of the leading players in the increasingly competitive humanoid robotics sector. The G1 robot, which stands at 4 feet 4 inches (132 cm), also has a remarkable ability to regain control if it takes a tumble, and can apparently perform a number of household chores, too.
While many challenges lie ahead for robotics firms when it comes to readying humanoid robots for specific roles that can be performed consistently, reliably, and truly independently, this year is shaping up to be an exciting one in the sector.
If there’s one thing that Dyson knows, it’s how to make hugely powerful motors for cordless vacuum cleaners. The Dyson Gen5detect is the most powerful cordless vacuum cleaner that it has made yet. Putting this cleaner through our tests, we measured it at a massive 369AW on maximum power – the highest, by far, that we’ve ever seen from a cordless cleaner.
Otherwise, it runs at 30AW on its gentle power mode (good for dusting) and 75AW on medium. Well, kind of. As with previous Dyson vacuums, the Gen5detect has a piezo sensor for detecting dust, adjusting its power automatically based on how much dirt it has encountered. In automatic mode, the vacuum ups and downs its power on the fly, so that you get the best clean without having to worry about which power mode you’re in.
Cleverly, the LCD on the back shows the amount of dust being picked up, as well as the battery life remaining in minutes and seconds.
For hard floors, there’s an additional tool, the Fluffy Optic head, which uses a green laser to highlight dust. It works brilliantly, making it easy to see where you have and haven’t cleaned.
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On maximum power, we found that this cleaner could collect grains of rice from more than 3cm away. If you’ve got the hand tools attached, this means you can quickly collect dust or suck it out of hard-to-reach areas.
Moving on to our regular tests, we found that this cleaner picked up 98.25% of dust on carpet, which is the best result that we’ve ever seen from a cordless cleaner. Edge performance was the same: 95.3% of dust collected. Hard floor collection was at 100%.
Moving to the anti-tangle tests with human hair, the Dyson Gen5detect refused to get any hair caught up in its brushes.
The only slight issue we encountered was when using the vacuum cleaner on a rubber-backed mat. Here, the Dyson Gen5detect produces too much power and suctions itself to the ground, stopping the brush bar from moving. We had to manually dial down the power. Still, it demonstrates just how powerful this cordless cleaner is.
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Measuring battery life in auto mode, we found that the cleaner lasted 26m 13s. Given that you can clean thoroughly with a single pass, that’s more than enough time to tackle an entire home in one go.
Arguably, most people will find that the cheaper Dyson V15 Detect will suit their needs, but if you have the cash and want the absolute best, there’s no other cordless vacuum cleaner that comes close to this one for power.
[GizmoThrill] shows off a design for an absolutely gorgeous, high-fidelity replica of the main character’s helmet from the video game Satisfactory. But the best part is the technique used to create the visor: just design around a cheap set of full-face “sunglasses” to completely avoid having to mold your own custom faceplate.
One of the most challenging parts of any custom helmet build is how to make a high-quality visor or faceplate. Most folks heat up a sheet of plastic and form it carefully around a mold, but [GizmoThrill] approached the problem from the other direction. After spotting a full-face sun visor online, they decided to design the helmet around the readily-accessible visor instead of the other way around.
The first thing to do with the visor is cover it with painter’s tape and 3D scan it. Once that’s done, the 3D model of the visor allows the rest of the helmet to be designed around it. In the case of the Satisfactory helmet, the design of the visor is a perfect match for the game’s helmet, but one could easily be designing their own custom headgear with this technique.
The hexagon grid pattern? It’s actually a clear vinyl sticker and doesn’t obstruct vision at all. Another clever touch.
With the helmet 3D printed, [GizmoThrill] heads to the bandsaw to cut away any excess from the visor, and secure it in place. That’s all there is to it! Sure, you don’t have full control over the visor’s actual shape, but it sure beats the tons and tons of sanding involved otherwise.
There’s a video tour of the whole process that shows off a number of other design features we really like. For example, metal mesh in the cheek areas and in front of the mouth means a fan can circulate air easily, so the one doesn’t fog up the inside of the visor with one’s very first breath. The mesh itself is concealed with some greebles mounted on top. You can see all those details up close in the video, embedded just below.
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The helmet design is thanks to [Punished Props] and we’ve seen their work before. This trick for turning affordable and somewhat gimmicky sunglasses into something truly time-saving is definitely worth keeping in mind.
An Amazon delivery van parked in front of the company’s headquarters campus and The Spheres in Seattle. (GeekWire Photo / Kurt Schlosser)
Amazon posted record quarterly revenue and strong cloud growth in Q4 — but its stock sunk more than 10% in after-hours trading Thursday after the company missed Wall Street’s profit expectations and revealed plans to spend $200 billion on capital expenditures in the upcoming year.
“With such strong demand for our existing offerings and seminal opportunities like AI, chips, robotics, and low earth orbit satellites, we expect to invest about $200 billion in capital expenditures across Amazon in 2026, and anticipate strong long-term return on invested capital,” Amazon CEO Andy Jassy said in a statement.
That’s well ahead of analyst expectations, and up from the $125 billion that Amazon had estimated for capex in 2025.
Investors are closely watching how much tech companies are spending on infrastructure amid the AI boom. Google said this week that its capex could double this year to as high as $185 billion, and Meta said its spend could reach $135 billion in 2026, almost double from last year. Microsoft’s capital spend reached $37.5 billion in its most recent quarter, up 66% from a year ago.
Amazon’s total capital spending also includes the buildout of its e-commerce fulfillment network, which means it’s not directly comparable to Microsoft, Google and others.
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However, on the earnings call with analysts, Jassy said the projected capex is “predominately” in AWS. “Some of it is for our core workloads, which are non-AI workloads, because they’re growing at a faster rate than we anticipated,” he said. “But most of it is in AI, and we just have a lot of growth, a lot of demand.”
Jassy said AWS could be growing even faster if capacity were available, noting that Amazon added more data center capacity than any company globally in 2025 and still faces strong demand. “What we’re continuing to see is, as fast as we install this AI capacity, we are monetizing it,” he said. “So it’s just a very unusual opportunity.”
Some key takeaways from the fourth quarter report:
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Amazon reported $213.4 billion in Q4 revenue, up 14% year-over-year and topping estimates of $211 billion. It’s first time the company has eclipsed $200 billion in quarterly revenue.
The company just missed expectations with earnings per share of $1.95, up from $1.86 in the year-ago period. Net income was $21.2 billion, up from $20 billion last year.
Amazon Web Services, the company’s closely watched cloud computing unit, reported $35.6 billion in Q4 sales, up 24% year-over-year — the fastest growth rate in three years. That topped analyst estimates of 21%.
Amazon’s online store sales grew 10% to $83 billion during the holiday quarter, topping estimates of $82.1 billion. The company continues to face competition from Walmart, which is growing its e-commerce sales and just hit a $1 trillion market capitalization.
Amazon’s market cap is about $2.4 trillion. Its stock is down slightly in the past 12 months.
The company will host its call with analysts at 2 p.m. PT. We’ll be listening for any comments related to the company’s slashing of 16,000 corporate jobs announced last week. Update: Jassy did not comment on the layoffs.
Here are more details from Amazon’s fourth quarter earnings report:
Advertising: The company’s ad business brought in $21.3 billion in revenue in the quarter, up 23% from the year-ago period. Advertising, along with AWS, is a major profit engine.
Third-party seller services: Revenue from third-party seller services was up 11% to $52.8 billion.
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Shipping costs: Amazon spent $31.5 billion on shipping in Q4, up 10%.
Physical stores: The category, which includes Whole Foods and other Amazon grocery stores, posted revenue of $5.8 billion, up 5%. Amazon announced last week that it is closing all Amazon Go and Amazon Fresh locations, a total of 72 stores nationwide, concentrating its efforts instead on its Whole Foods Market locations and grocery delivery from Amazon.com.
Headcount: Amazon employs 1.58 million people, up 1% year-over-year, and down slightly from Q3. That figure does not include seasonal and contract workers, and does not reflect the latest job cuts, which took place after the end of the quarter.
Prime: Subscription services revenue, which includes Prime memberships, came in at $13.1 billion, up 14%.
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Guidance: The company forecasts Q1 sales between $173.5 billion and $178.5 billion. Operating income is expected to range between $16.5 billion and $21.5 billion, compared with $18.4 billion in the year-ago quarter.
HP and Redington have just announced the inauguration of a new Centre of Excellence (CoE) in Chennai, India, aimed at accelerating the adoption of digital printing technologies in the country. The facility is intended to support the evolving needs of India’s print and manufacturing ecosystem by offering hands-on access to advanced digital printing solutions.
The Centre of Excellence was inaugurated in the presence of customers, industry partners, and solution vendors, with senior leadership from both HP and Redington in attendance. According to the companies, the initiative reflects a continued focus on skill development, technology adoption, and innovation within the Indian digital printing industry.
Focus on Training, Demonstrations, and Consulting
Spread across 20,000 square feet, the Chennai-based CoE is designed as a comprehensive hub for technology demonstrations, professional training, process optimisation, and industry consulting. The facility will allow customers to experience HP’s digital printing solutions in real-world scenarios, while also running education programs for brands and print buyers to help them better understand and adopt digital printing workflows.
Pawan Chauhan, Country Business Manager for HP Industrial and Inkjet Business Solutions in India, “The launch of the Centre of Excellence reflects our long-term commitment to the Indian digital printing ecosystem and our focus on building skills for the future of work and driving innovation. It is also a moment to celebrate 21 years of trust and collaboration between HP and Redington.”
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From Redington’s perspective, the Centre of Excellence is positioned within a broader shift toward flexible, digital-first manufacturing platforms. V.S. Hariharan, Group CEO of Redington Limited, said the initiative will help deepen customer engagement, expand solution-led offerings, and accelerate the adoption of advanced digital printing technologies, including HP Indigo and HP’s industrial 3D printing solutions.
A developer gets a LinkedIn message from a recruiter. The role looks legitimate. The coding assessment requires installing a package. That package exfiltrates all cloud credentials from the developer’s machine — GitHub personal access tokens, AWS API keys, Azure service principals and more — are exfiltrated, and the adversary is inside the cloud environment within minutes.
Your email security never saw it. Your dependency scanner might have flagged the package. Nobody was watching what happened next.
The attack chain is quickly becoming known as the identity and access management (IAM) pivot, and it represents a fundamental gap in how enterprises monitor identity-based attacks. CrowdStrike Intelligence research published on January 29 documents how adversary groups operationalized this attack chain at an industrial scale. Threat actors are cloaking the delivery of trojanized Python and npm packages through recruitment fraud, then pivoting from stolen developer credentials to full cloud IAM compromise.
In one late-2024 case, attackers delivered malicious Python packages to a European FinTech company through recruitment-themed lures, pivoted to cloud IAM configurations and diverted cryptocurrency to adversary-controlled wallets.
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Entry to exit never touched the corporate email gateway, and there is no digital evidence to go on.
On a recent episode of CrowdStrike’s Adversary Universe podcast, Adam Meyers, the company’s SVP of intelligence and head of counter adversary operations, described the scale: More than $2 billion associated with cryptocurrency operations run by one adversary unit. Decentralized currency, Meyers explained, is ideal because it allows attackers to avoid sanctions and detection simultaneously. CrowdStrike’s field CTO of the Americas, Cristian Rodriguez, explained that revenue success has driven organizational specialization. What was once a single threat group has split into three distinct units targeting cryptocurrency, fintech and espionage objectives.
That case wasn’t isolated. The Cybersecurity and Infrastructure Security Agency (CISA) and security company JFrog have tracked overlapping campaigns across the npm ecosystem, with JFrog identifying 796 compromised packages in a self-replicating worm that spread through infected dependencies. The research further documents WhatsApp messaging as a primary initial compromise vector, with adversaries delivering malicious ZIP files containing trojanized applications through the platform. Corporate email security never intercepts this channel.
Most security stacks are optimized for an entry point that these attackers abandoned entirely.
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When dependency scanning isn’t enough
Adversaries are shifting entry vectors in real-time. Trojanized packages aren’t arriving through typosquatting as in the past — they’re hand-delivered via personal messaging channels and social platforms that corporate email gateways don’t touch. CrowdStrike documented adversaries tailoring employment-themed lures to specific industries and roles, and observed deployments of specialized malware at FinTech firms as recently as June 2025.
CISA documented this at scale in September, issuing an advisory on a widespread npm supply chain compromise targeting GitHub personal access tokens and AWS, GCP and Azure API keys. Malicious code was scanned for credentials during package installation and exfiltrated to external domains.
Dependency scanning catches the package. That’s the first control, and most organizations have it. Almost none have the second, which is runtime behavioral monitoring that detects credential exfiltration during the install process itself.
“When you strip this attack down to its essentials, what stands out isn’t a breakthrough technique,” Shane Barney, CISO at Keeper Security, said in an analysis of a recent cloud attack chain. “It’s how little resistance the environment offered once the attacker obtained legitimate access.”
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Adversaries are getting better at creating lethal, unmonitored pivots
Google Cloud’s Threat Horizons Report found that weak or absent credentials accounted for 47.1% of cloud incidents in the first half of 2025, with misconfigurations adding another 29.4%. Those numbers have held steady across consecutive reporting periods. This is a chronic condition, not an emerging threat. Attackers with valid credentials don’t need to exploit anything. They log in.
Research published earlier this month demonstrated exactly how fast this pivot executes. Sysdig documented an attack chain where compromised credentials reached cloud administrator privileges in eight minutes, traversing 19 IAM roles before enumerating Amazon Bedrock AI models and disabling model invocation logging.
Eight minutes. No malware. No exploit. Just a valid credential and the absence of IAM behavioral baselines.
Ram Varadarajan, CEO at Acalvio, put it bluntly: Breach speed has shifted from days to minutes, and defending against this class of attack demands technology that can reason and respond at the same speed as automated attackers.
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Identity threat detection and response (ITDR) addresses this gap by monitoring how identities behave inside cloud environments, not just whether they authenticate successfully. KuppingerCole’s 2025 Leadership Compass on ITDR found that the majority of identity breaches now originate from compromised non-human identities, yet enterprise ITDR adoption remains uneven.
Morgan Adamski, PwC’s deputy leader for cyber, data and tech risk, put the stakes in operational terms. Getting identity right, including AI agents, means controlling who can do what at machine speed. Firefighting alerts from everywhere won’t keep up with multicloud sprawl and identity-centric attacks.
Why AI gateways don’t stop this
AI gateways excel at validating authentication. They check whether the identity requesting access to a model endpoint or training pipeline holds the right token and has privileges for the timeframe defined by administrators and governance policies. They don’t check whether that identity is behaving consistently with its historical pattern or is randomly probing across infrastructure.
Consider a developer who normally queries a code-completion model twice a day, suddenly enumerating every Bedrock model in the account, disabling logging first. An AI gateway sees a valid token. ITDR sees an anomaly.
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A blog post from CrowdStrike underscores why this matters now. The adversary groups it tracks have evolved from opportunistic credential theft into cloud-conscious intrusion operators. They are pivoting from compromised developer workstations directly into cloud IAM configurations, the same configurations that govern AI infrastructure access. The shared tooling across distinct units and specialized malware for cloud environments indicate this isn’t experimental. It’s industrialized.
Google Cloud’s office of the CISO addressed this directly in their December 2025 cybersecurity forecast, noting that boards now ask about business resilience against machine-speed attacks. Managing both human and non-human identities is essential to mitigating risks from non-deterministic systems.
No air gap separates compute IAM from AI infrastructure. When a developer’s cloud identity is hijacked, the attacker can reach model weights, training data, inference endpoints and whatever tools those models connect to through protocols like model context protocol (MCP).
That MCP connection is no longer theoretical. OpenClaw, an open-source autonomous AI agent that crossed 180,000 GitHub stars in a single week, connects to email, messaging platforms, calendars and code execution environments through MCP and direct integrations. Developers are installing it on corporate machines without a security review.
The IAM implications are direct. In an analysis published February 4, CrowdStrike CTO Elia Zaitsev warned that “a successful prompt injection against an AI agent isn’t just a data leak vector. It’s a potential foothold for automated lateral movement, where the compromised agent continues executing attacker objectives across infrastructure.”
The agent’s legitimate access to APIs, databases and business systems becomes the adversary’s access. This attack chain doesn’t end at the model endpoint. If an agentic tool sits behind it, the blast radius extends to everything the agent can reach.
Where the control gaps are
This attack chain maps to three stages, each with a distinct control gap and a specific action.
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Entry: Trojanized packages delivered through WhatsApp, LinkedIn and other non-email channels bypass email security entirely. CrowdStrike documented employment-themed lures tailored to specific industries, with WhatsApp as a primary delivery mechanism. The gap: Dependency scanning catches the package, but not the runtime credential exfiltration. Suggested action: Deploy runtime behavioral monitoring on developer workstations that flags credential access patterns during package installation.
Pivot: Stolen credentials enable IAM role assumption invisible to perimeter-based security. In CrowdStrike’s documented European FinTech case, attackers moved from a compromised developer environment directly to cloud IAM configurations and associated resources. The gap: No behavioral baselines exist for cloud identity usage. Suggested action: Deploy ITDR that monitors identity behavior across cloud environments, flagging lateral movement patterns like the 19-role traversal documented in the Sysdig research.
Objective: AI infrastructure trusts the authenticated identity without evaluating behavioral consistency. The gap: AI gateways validate tokens but not usage patterns. Suggested action: Implement AI-specific access controls that correlate model access requests with identity behavioral profiles, and enforce logging that the accessing identity cannot disable.
Jason Soroko, senior fellow at Sectigo, identified the root cause: Look past the novelty of AI assistance, and the mundane error is what enabled it. Valid credentials are exposed in public S3 buckets. A stubborn refusal to master security fundamentals.
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What to validate in the next 30 days
Audit your IAM monitoring stack against this three-stage chain. If you have dependency scanning but no runtime behavioral monitoring, you can catch the malicious package but miss the credential theft. If you authenticate cloud identities but don’t baseline their behavior, you won’t see the lateral movement. If your AI gateway checks tokens but not usage patterns, a hijacked credential walks straight to your models.
The perimeter isn’t where this fight happens anymore. Identity is.
Amazon Web Services has accelerated its growth mode thanks in part to AI demand. (GeekWire File Photo / Todd Bishop)
Amazon Web Services revenue grew at its fastest pace in more than three years, up 24% to $35.6 billion in the fourth quarter, in a sign that demand for artificial intelligence and custom silicon is boosting corporate spending on the cloud.
The company disclosed revenue for its in-house data center chips for the first time, saying its Trainium and Graviton processors have a combined annual run rate of more than $10 billion.
But the revenue milestones are coming at a big cost. In the earnings release, Amazon CEO Andy Jassy signaled plans to spend a record $200 billion in capital expenditures across Amazon in 2026, citing “seminal opportunities like AI, chips, robotics, and low earth orbit satellites.”
Most of the capital spending is on AWS, Jassy said on the earnings conference call, seeking to assure investors that Amazon is “monetizing capacity as fast as we can install it.”
He pushed back on skepticism about the capital spending, saying “this isn’t some sort of quixotic top-line grab,” and compared the AI investment cycle to the early days of the company’s core cloud business. He called the current moment an “extraordinarily unusual opportunity to forever change the size of AWS and Amazon as a whole.”
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When asked about the shape of AI demand, Jassy offered a “barbell” analogy. On one end are the AI research labs spending “gobs and gobs of compute.” On the other are enterprises using AI for routine tasks like customer service and business process automation.
But the massive $200 billion bet is targeted at the “middle of the barbell,” core enterprise production workloads, which Jassy argues haven’t really arrived yet.
“The lion’s share of that demand is still yet to come,” Jassy said. He predicted this middle section “may end up being the largest and the most durable” part of the AI market.
Amazon’s plan adds to a wave of record-setting AI infrastructure spending from tech giants.
Google parent Alphabet said Wednesday it expects 2026 capital expenditures of $175 billion to $185 billion, roughly double its 2025 spending.
For the full year, Amazon generated $139.5 billion in cash from its operations in 2025, up 20%. But after accounting for the massive infrastructure buildout, the company was left with $11.2 billion in free cash flow, down from $38.2 billion a year earlier.
That means Amazon is making more money than ever, but plowing nearly all of it back into building out AI capacity, leaving little cash left over for shareholders.
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Amazon shares fell 10% after-hours following the earnings report. In addition to the outsized capex projection, the company’s profits of $1.95/share just missed Wall Street’s expectations.
Ransomware operators are hosting and delivering malicious payloads at scale by abusing virtual machines (VMs) provisioned by ISPsystem, a legitimate virtual infrastructure management provider.
Researchers at cybersecurity company Sophos observed the tactic while investigating recent ‘WantToCry’ ransomware incidents. They found the attackers used Windows VMs with identical hostnames, suggesting default templates generated by ISPsystem’s VMmanager.
Diving deeper, the researchers discovered that the same hostnames were present in the infrastructure of multiple ransomware operators, including LockBit, Qilin, Conti, BlackCat/ALPHV, and Ursnif, as well as various malware campaigns involving RedLine and Lummar info-stealers.
Location of devices using the same hostname Source: Sophos
ISPsystem is a legitimate software company that develops control panels for hosting providers, used for the management of virtual servers, OS maintenance, etc. VMmanager is the company’s virtualization management platform used to spin up Windows or Linux VMs for customers.
Sophos found that VMmanager’s default Windows templates reuse the same hostname and system identifiers every time they are deployed.
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Bulletproof hosting providers that knowingly support cybercrime operations and ignore takedown requests take advantage of this design weakness. They allow malicious actors to spin up VMs via VMmanager, used for command-and-control (C2) and payload-delivery infrastructure.
This essentially hides malicious systems among thousands of innocuous ones, complicates attribution, and makes quick takedowns unlikely.
The majority of the malicious VMs were hosted by a small cluster of providers with a bad reputation or sanctions, including Stark Industries Solutions Ltd., Zomro B.V., First Server Limited, Partner Hosting LTD, and JSC IOT.
Sophos has also discovered a provider with direct control of physical infrastructure named MasterRDP, which uses VMmanager for evasion and offers VPS and RDP services that do not comply with legal requests.
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According to Sophos, four of the most prevalent ISPsystem hotnames “account for over 95% of the total number of internet-facing ISPsystem virtual machines:”
WIN-LIVFRVQFMKO
WIN-LIVFRVQFMKO
WIN-344VU98D3RU
WIN-J9D866ESIJ2
All of them were present either in customer detection or telemetry data linked to cybercriminal activity.
The researchers note that while ISPsystem VMmanager is a legitimate platform for virtualization management, it is also attractive to cybercriminals due to “its low cost, low barrier to entry, and turnkey deployment capabilities.”
BleepingComputer has contacted ISPsystem to ask if they are aware of the large-scale abuse of VM templates and their plans to address the issue, but a statement wasn’t available by publishing time.
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