Veterinary care in the United States is in a quiet crisis. Vet bills have risen roughly 40% over the past five years, driven largely by private equity firms consolidating veterinary clinics into profit-maximizing chains. Now a startup called Snout has raised over $110 million to try to make basic pet care affordable again — by going around insurance entirely.

The Raise

Snout announced in late January 2026 that it secured $100 million in debt financing from Clear Haven Capital Management alongside a $10 million Series A led by Footwork, the venture firm co-founded by Mike Smith and Nikhil Basu Trivedi. Snout is Footwork’s first investment from its recently raised $225 million second fund. Additional participation came from Pear, Bread and Butter Ventures, Restive Ventures, and veterinary industry insiders.

How Snout Works

Founded in 2023 by Emily Dong — a decade-long pet industry veteran whose previous company Pawprint was acquired in 2020 — Snout partners directly with veterinary clinics to offer prevention-first wellness plans. Pet owners pay an average of $65 per month with no credit checks, no breed or age discrimination, and no reimbursement delays. It is not insurance. It is a membership model for routine care, paid in interest-free monthly installments.

“With inflation and private equity coming into the space, prices have gone up 40% over the last five years, and people can’t afford basic things,” Dong told Fortune. “It’s terrifying to go to the vet. You’re not going to get out of there for less than $300 to $500, even if nothing’s wrong.”

That quote says everything about where veterinary medicine has landed. The traditional cash-at-time-of-service model was already strained. When PE firms started rolling up independent clinics — squeezing margins and raising prices — basic preventive care became a luxury for millions of pet owners.

Why the Debt Facility Matters More Than the Equity

The $100 million debt facility is the real signal here. That is not venture optimism — that is institutional capital underwriting the risk model. Clear Haven is betting Snout’s repayment data holds up at scale. The playbook mirrors what worked in vertical fintech for dental and urgent care: identify a cash-heavy service industry with no financing infrastructure, build a membership layer, then prove the unit economics with institutional debt.

Laterstack Editorial Take

Laterstack exists to sharpen critical thinking by connecting tech, policy, and power to everyday life — across class, industry, and influence. Snout is a textbook case of a startup building a band-aid for a wound that private equity inflicted. The vet bill crisis is not a market failure — it is a market working exactly as designed after consolidation. Whether Snout can scale fast enough to matter before the next round of PE acquisitions absorbs even more clinics is the real question.

This pattern keeps repeating across healthcare: the same financialization that created the problem creates the opportunity for the solution. It happened with urgent care, dental chains, and pharmacy benefits. Now it is happening to your dog’s annual checkup.

What This Means for Everyday People

If you own a pet, vet costs are not coming back down. The consolidation is structural. Plans like Snout’s $65/month model could legitimately help families budget for routine care instead of skipping visits until something goes wrong. But the underlying economics — PE firms extracting returns from clinic margins — remain untouched. The financing makes it survivable. It does not make it fair.

For the startup ecosystem watching AI reshape every industry, Snout is a reminder that some of the biggest opportunities are not in frontier tech. They are in the structural damage left behind by financial engineering.

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The way people discover products is shifting underneath every brand on the internet. AI agents — not humans scrolling through search results — are increasingly making purchasing decisions, comparing options, and executing transactions. A startup called Limy just emerged from stealth with $10 million in seed funding to make sure brands do not disappear from that new equation.

The Raise

Limy announced its seed round on January 28, 2026, led by Flybridge with participation from a16z speedrun, Axiom, Clarim, Communitas, JRV, and AnD Ventures. The round was significantly oversubscribed. Founded in 2024 and headquartered in New York, Limy has a 17-person team with plans to scale to approximately 120 employees within a year.

What Limy Actually Does

CEO Aviv Shamny — alongside co-founders Ido Zabarsky (COO) and Ori Reichman (CTO) — built Limy to solve a problem that barely existed two years ago: how brands appear to AI shopping agents. Traditional SEO optimizes for Google’s search algorithm. Limy optimizes for the agentic web — the emerging layer where large language models act as intermediaries between consumers and products.

The platform lets brands track AI-driven traffic, analyze what prompts lead to their products being recommended, and optimize sentiment and visibility inside LLM outputs. “Every time bots arrive, we understand their intent and guide them to the most relevant content,” Shamny explained to Calcalist Tech.

Unlike tools focused on the consumer side of AI shopping, Limy focuses entirely on the agent side — understanding how AI bots evaluate, compare, and surface brands to end users. Both Shamny and Zabarsky are a16z speedrun scouts, and the founding team brings deep expertise in data science and how LLMs process and rank information.

The Traction Is Already Serious

Limy has approximately 250 large customers generating meaningful revenue on a subscription and usage model. Its client roster includes Fortune 100 names like AstraZeneca, Samsung, and KIA. Some customers are already attributing 10% of their revenue to the platform — a striking figure for a company that just came out of stealth.

For two decades, brands optimized for Google’s algorithm. SEO became a multi-billion-dollar industry because showing up in search results meant revenue. Now the interface is changing. Consumers increasingly get product recommendations from AI chatbots, not search results pages. Traditional SEO does not translate. Google rankings do not determine what ChatGPT recommends. The signals are different. The data pipelines are opaque.

Laterstack Editorial Take

Laterstack exists to sharpen critical thinking by connecting tech, policy, and power to everyday life — across class, industry, and influence. The “neutral AI recommendation” was always temporary. Limy is building the optimization layer that turns AI shopping into the next paid placement ecosystem — and the brands with the deepest pockets will dominate it first. This is the same pattern playing out in ChatGPT’s move toward advertising: the shift from search to AI agents does not eliminate manipulation. It moves it to a layer consumers cannot see.

The real question is not whether LLM-driven commerce is coming. It is who controls the optimization layer. Limy is positioning itself as the SEO of the AI era, and if that comparison holds, the companies that master this visibility game will dominate product discovery for the next decade.

What This Means for Everyday People

If you have ever asked ChatGPT or Perplexity for a product recommendation, the results were not neutral. They were shaped by how well a brand’s data was structured for AI consumption. Limy is building the infrastructure for brands to influence that process deliberately.

For small businesses, this is a warning. The brands with budgets to pay for LLM visibility platforms will be the ones AI agents surface first — just like the startups that moved fastest on cloud infrastructure captured outsized value. The agentic web has gatekeepers. They are just wearing different clothes.

For consumers, the takeaway is simpler: the AI is not neutral. The products it recommends have been optimized to be recommended. Treat AI shopping suggestions the way you already treat Google’s top results — with healthy skepticism.

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A former Google employee has filed a confidential SEC whistleblower complaint alleging the company helped an Israeli military contractor apply Gemini AI to drone surveillance video. The complaint, first reported by The Washington Post on February 1, claims Google breached its own ethics policies in the process.

The core allegation is straightforward. Google provided AI tools to an Israeli contractor who used them to analyze drone footage – the kind of application Google once publicly promised it would never support.

The Ethics Policy That Disappeared

In February 2025, Google quietly revised its public AI Principles, stripping out language that committed the company to avoiding AI technologies applied to weapons or surveillance violating “internationally accepted norms.” The updated version replaced those commitments with vaguer language about pursuing AI “responsibly” and in line with “widely accepted principles of international law.”

The timing was not subtle. The revision came weeks after Alphabet CEO Sundar Pichai attended President Donald Trump’s January 20, 2025 inauguration alongside Jeff Bezos and Mark Zuckerberg. Hours after taking office, Trump rescinded a Biden-era executive order that established guardrails for AI development.

Google was not alone. Throughout 2024, OpenAI, Anthropic, and Meta had already walked back their own AI usage policies to allow U.S. intelligence and defense agencies access to their systems.

Project Nimbus and the Broader Pattern

This whistleblower complaint lands on top of years of internal conflict over Project Nimbus, a $1.2 billion cloud computing contract with the Israeli government signed jointly by Google and Amazon. In April 2024, Google fired 28 employees who staged sit-in protests against the contract at offices in New York, Sunnyvale, and Seattle.

At least nine employees were arrested. No Tech for Apartheid, the activist group behind the protests, alleged the Israeli military was using Google Photos as part of its facial recognition efforts in Gaza.

The pattern is clear. Write ethics policies when public pressure demands them. Rewrite those policies when government contracts require it. Fire anyone who objects.

Commercial large language models are now reportedly used by the Israeli military for translating intercepted Palestinian communications, automatically adding individuals to target lists based on keywords. The line between “cloud services” and “military AI” has been functionally erased.

Laterstack exists to sharpen critical thinking by connecting tech, policy, and power to everyday life – across class, industry, and influence. Google did not accidentally end up powering drone surveillance. It removed the ethics language that would have prevented it, fired the employees who protested, then quietly rewrote the rules. The timeline is a sequence of deliberate decisions by executives who understood exactly what they were enabling. Lawmakers drafting AI governance frameworks, Pentagon officials evaluating tech partnerships, and the billionaires on Google’s board need to answer a direct question: when a company rewrites its ethics policy to match its contracts rather than the other way around, what is the policy actually for?

What This Means for Everyday People

Corporate AI ethics policies are marketing documents. They exist until they conflict with revenue. Every major AI company has now revised or abandoned its restrictions on military and surveillance use. The shift is not unique to Google – it is an industry-wide pivot toward defense revenue as the commercial AI market matures.

The whistleblower route through the SEC signals that internal dissent has been effectively crushed. When employees cannot protest internally without being fired, federal complaints become the last recourse.

Professor Elke Schwarz of Queen Mary University London put it bluntly: the “shifting mood among big tech firms towards military AI” is no longer a trend. It is the baseline.

Three of China’s biggest AI players are preparing to launch new models in February, timed to the Lunar New Year holiday. ByteDance, Alibaba, and DeepSeek are all reportedly ready to ship, turning what was once a quiet festival period into the opening round of 2026’s AI arms race.

The lineup is aggressive. ByteDance is releasing three products at once. Alibaba is going after the consumer market. DeepSeek is teasing a next-generation architecture that could reset benchmarks.

What Each Company Is Shipping

ByteDance plans to launch Doubao 2.0, the next version of its flagship large language model. Doubao already has 163 million monthly active users, making it China’s largest AI application by user count. Alongside it, ByteDance is releasing Seeddream 5.0 for image generation and Seeddance 2.0 for video generation. This is a full multimodal push.

Alibaba is launching Qwen 3.5, its next-generation model optimized for mathematical reasoning and code generation. Alibaba is also rolling out large-scale marketing campaigns for Qwen’s consumer-facing chatbot, directly targeting ByteDance’s Doubao in the consumer AI market.

DeepSeek has been quieter, but GitHub repository updates revealed a new architecture identifier called MODEL1, widely seen as the foundation for DeepSeek V4. Sources say the model could drop as early as mid-February. DeepSeek’s last major release sent shockwaves through global markets – the kind of disruption that rattled crypto alongside it.

The Lunar New Year Strategy

The timing is not accidental. Tencent announced it will distribute 1 billion yuan ($140 million) in cash through its Yuanbao AI chatbot during the holiday, copying the “red envelope” campaigns that made WeChat Pay dominant. ByteDance and Baidu are running similar AI promotions.

This is a user acquisition land grab. Chinese tech companies are using the holiday the way American companies use the Super Bowl – a cultural moment to capture mass attention and convert it into daily AI app usage.

The “Months Behind” Question

At Davos in January, Google DeepMind CEO Demis Hassabis said Chinese AI firms are now just “months” behind Western frontier models – down from the two-to-three year gap estimated as recently as 2024. Bloomberg reported the number at approximately six months.

But Hassabis drew a distinction between copying and inventing. “They’ve shown they can catch up and be very close to the frontier,” he said. “But can they actually innovate something new, like a new transformer, that gets beyond the frontier? I don’t think that’s been shown yet.”

Nvidia CEO Jensen Huang offered a blunter assessment at CES: “China is well ahead of us on energy. We are way ahead on chips. They’re right there on infrastructure. They’re right there on AI models.”

Laterstack exists to sharpen critical thinking by connecting tech, policy, and power to everyday life – across class, industry, and influence. The “months behind” framing from Hassabis is already outdated by the time it reaches Western audiences. Three major Chinese firms are releasing next-generation models simultaneously, backed by consumer platforms with hundreds of millions of users. American policymakers still debating export controls need to understand that the gap is not closing – it is being closed deliberately, at scale, with state coordination Western labs cannot match. The question is no longer whether China can compete in AI. It is whether the West’s lead was ever as large as its leaders claimed.

What This Means for Everyday People

The AI app war is now global. If you use ChatGPT, Gemini, or Claude, you are using products that are being benchmarked against Chinese competitors most Americans have never heard of. That competition drives faster releases, lower prices, and more aggressive data collection on all sides.

For investors, the February launches could move markets. DeepSeek’s last major release triggered a $1 trillion sell-off in U.S. tech stocks. If DeepSeek V4 matches or beats frontier Western models again, expect similar volatility.

Apple is betting its AI future on Google. The company confirmed that a completely overhauled Siri, powered by Google’s Gemini AI, will arrive in iOS 26.4 – with beta testing starting in late February and public release expected by March or early April 2026.

The deal is roughly $1 billion per year. Apple gets access to Google’s advanced 1.2 trillion parameter Gemini model and Google Cloud TPU infrastructure. In return, Google gets something arguably more valuable: its AI running on over a billion Apple devices.

What the New Siri Actually Does

The iOS 26.4 release introduces “World Knowledge Answers” – Siri will provide web-based summaries with citations, similar to what ChatGPT and Perplexity already offer. Deeper integration across Mail, Photos, Music, and TV is planned, with voice commands for searching and editing photos by spoken description and generating emails based on calendar activity.

This is not a minor update. Apple is admitting that after years of trying to build competitive AI in-house, it could not catch up. Siri has been the worst major voice assistant for the better part of a decade. The Gemini integration is a concession dressed as a partnership.

Apple says the Gemini model will run on its Private Cloud Compute servers, keeping user data isolated from Google’s infrastructure. That privacy framing is doing heavy lifting – Apple is routing its most personal user interactions through a competitor’s AI while insisting no data crosses the line.

The $2 Billion Q.ai Acquisition

On January 29, Apple acquired Israeli startup Q.ai for approximately $2 billion – the company’s second-largest acquisition ever, behind only the $3 billion Beats deal in 2014.

Q.ai develops AI that analyzes facial expressions and micro-movements to interpret silent communication. Patents show the technology being used in headphones or glasses, using “facial skin micro movements” to communicate without talking. Q.ai’s CEO, Aviad Maizels, previously founded PrimeSense, the company whose technology became Apple’s Face ID.

The acquisition signals Apple’s longer-term play: a future where Siri understands you without you saying a word. Combined with the Gemini language capabilities, Apple is assembling a voice assistant that processes both spoken and unspoken inputs.

What Apple Shelved to Get Here

To focus resources on the Siri overhaul, Apple shelved its AI-powered Safari features and planned health AI tools. That is a telling priority call. Apple decided that fixing Siri was more urgent than expanding Apple Intelligence into new domains.

Johny Srouji, Apple’s SVP of hardware technologies, called Q.ai “a remarkable company pioneering new and creative ways to use imaging and machine learning.” Bloomberg’s Mark Gurman reported that Apple is also developing an internal chatbot-style assistant codenamed “Campos” that will eventually replace the current Siri interface entirely, with capabilities “competitive with Gemini 3.”

Laterstack exists to sharpen critical thinking by connecting tech, policy, and power to everyday life – across class, industry, and influence. Apple paying Google $1 billion a year for AI is the clearest admission yet that the company lost the AI race it claimed to be winning. Tim Cook spent years telling investors that Apple Intelligence would be a differentiator. Instead, the company that already pays Google an estimated $20 billion annually for default search is now paying for AI too. The dependency is deepening, not shrinking. Regulators currently scrutinizing the Google-Apple search deal should be asking whether adding an AI layer to that relationship makes the antitrust problem better or worse. For consumers, the practical question is simpler: if Apple cannot build its own AI, why is anyone paying a premium for the ecosystem?

What This Means for Everyday People

If you have an iPhone 15 Pro or newer, you will get the new Siri in spring 2026. Older devices are left out. That is Apple’s standard upgrade pressure, but now it is tied to AI capability rather than just camera improvements.

The privacy question is real. Apple’s promise that Gemini runs on Private Cloud Compute servers sounds reassuring, but it means your Siri queries are being processed by Google-designed AI on Apple-managed hardware. Whether that distinction matters depends on how much you trust both companies simultaneously – a question that keeps getting harder to answer.

Microsoft posted $81.3 billion in revenue and $4.14 in non-GAAP diluted EPS for Q2 FY2026 on January 28. Both numbers beat Wall Street expectations — analysts had called for $80.27 billion and $3.97 respectively. The stock plunged roughly 10% the next day, erasing $357 billion in market value. The second-largest single-day loss in U.S. stock market history.

The message from investors was blunt: beating earnings does not matter if you cannot prove the AI money machine actually works.

Azure Growth Hits a Wall of Expectations

Azure revenue growth slowed to 39%, down from 40% the prior quarter and below the institutional “whisper numbers” that expected AI tailwinds to accelerate growth. CFO Amy Hood guided Q3 Azure growth to 37%-38%, signaling further deceleration.

Capital expenditures surged 66% to $37.5 billion — well above the $34.31 billion analysts expected, and putting Microsoft on a $148 billion annual run rate for AI infrastructure spending. Hood confirmed two-thirds went to short-lived assets like CPUs and GPUs — hardware that depreciates fast and requires constant replacement.

Hood also revealed something telling: if Microsoft had allocated all GPUs that came online in Q1 and Q2 exclusively to Azure customers, “the KPI would have been over 40.” Translation — the slowdown is partly a strategic choice. Microsoft is reserving compute capacity for Copilot and its partnership with OpenAI rather than selling it to enterprise customers.

Meta Showed Receipts. Microsoft Showed a Bill.

The contrast was brutal. On the same reporting day, Meta Platforms posted massive AI spending and its stock jumped 8%. The difference: Meta demonstrated that AI spending was directly fueling record advertising revenue. Tangible receipts. Microsoft is still asking Wall Street to trust the process.

Barclays analyst Raimo Lenschow noted the company “will not really accelerate Azure further from here, due to the law of large numbers and extra capacity being used for its own, higher-margin, first-party offerings.” Wedbush’s Dan Ives called 2026 “the inflection year for AI and MSFT.” Bernstein analyst Mark Moerdler suggested management “made a cognizant decision to focus on what is best for the company long term rather than driving the stock up this quarter.”

Laterstack Editorial Take

Laterstack exists to sharpen critical thinking by connecting tech, policy, and power to everyday life — across class, industry, and influence. Microsoft lost $357 billion in a single day — not because the business is failing, but because Wall Street is starting to ask the question Silicon Valley does not want to answer: where is the return? The market is not punishing AI investment. It is punishing AI investment without proof of monetization. That distinction matters for every company in the AI infrastructure race, and for every lawmaker weighing subsidies and tax incentives for data center buildouts. The “spend now, monetize later” era has an expiration date, and the clock just got louder.

What This Means for Everyday People

If you work at a company paying for Microsoft 365 or Azure, watch closely. Microsoft is prioritizing internal AI development over cloud capacity for paying customers — which could mean slower feature rollouts, capacity constraints, or price increases as the company recoups its investment. The startup ecosystem feels it too — when the biggest cloud provider signals that AI infrastructure costs are accelerating faster than revenue, smaller companies building on that infrastructure absorb the pressure first.

For anyone holding Microsoft stock in a 401(k) or index fund, you watched real money evaporate not because the company failed, but because it spent aggressively on a future that has not materialized yet. The $357 billion wipeout is a stress test for the entire “spend now, monetize later” thesis driving Big Tech AI investment.

The Bottom Line

Microsoft’s numbers were fine. The problem is that “fine” does not justify $37.5 billion quarters. Wall Street is not punishing the results. It is punishing the gap between what Microsoft is spending and what it can prove. Until Copilot and Azure AI show enterprise adoption at scale, every earnings call is a referendum on whether the biggest AI bet in corporate history will pay off.

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A driverless Waymo vehicle struck a child near Grant Elementary School in Santa Monica, California on January 23, 2026. The collision happened on Pearl Street within two blocks of the school during morning drop-off hours. Other children, a crossing guard, and several double-parked vehicles were present. No human was in the vehicle.

Both the National Highway Traffic Safety Administration (NHTSA) and the National Transportation Safety Board (NTSB) have opened investigations. The child sustained minor injuries.

What Waymo Says Happened

The child ran across the street from behind a double-parked SUV toward the school. Waymo’s 5th Generation Automated Driving System detected the child “as soon as they began to emerge from behind the stopped vehicle,” according to the company. The vehicle braked hard, reducing speed from approximately 17 mph to under 6 mph before contact.

Waymo claimed a “fully attentive human driver” in the same scenario would have likely hit the child at 14 mph — more than double the contact speed. The vehicle stopped, pulled to the side, and remained until law enforcement cleared the scene.

NHTSA’s Office of Defects Investigations announced it would assess “whether the Waymo AV exercised appropriate caution given, among other things, its proximity to the elementary school during drop-off hours, and the presence of young pedestrians and other potential vulnerable road users.”

Austin: The Recall That Failed

The Santa Monica incident alone would warrant scrutiny. But the Austin situation reveals a deeper pattern. Waymo vehicles in Austin racked up 24 documented violations for illegally passing stopped school buses — buses with flashing lights and deployed stop signs while children were boarding or exiting. Bus-mounted cameras captured every one.

In December 2025, Waymo issued a voluntary recall of 3,067 vehicles and pushed a software update to fix the problem. It did not work. At least four more violations occurred after the recall, with the most recent on January 14. The NTSB opened its own probe on the same day as the Santa Monica collision.

Austin ISD demanded that Waymo cease all operations during school hours on school days. Waymo refused. The district said it would pursue “any and all” legal recourse. Waymo’s chief safety officer Mauricio Pena responded that the company “safely navigates thousands of school bus encounters weekly across the United States” — a statement that simultaneously acknowledges the scale of the problem and dismisses it.

Laterstack Editorial Take

Laterstack exists to sharpen critical thinking by connecting tech, policy, and power to everyday life — across class, industry, and influence. A child was hit near a school. A software recall failed and violations continued. A school district asked a tech company to stop operating near children and the company said no. The pattern is not ambiguous — it is a company scaling at commercial speed while safety validation moves at bureaucratic speed. City lawmakers, NHTSA officials, and the investors pushing Waymo toward 1 million rides per week need to confront what is actually being traded: public safety in exchange for scale metrics. The question is not whether autonomous vehicles are safer than human drivers on average — it is who bears the cost when they are not, and whether the people making deployment decisions have any accountability to the communities absorbing the risk.

What This Means for Everyday People

If your children walk, bike, or take the bus to school in a city where Waymo operates — currently San Francisco, Phoenix, Los Angeles, and Austin — this is immediate and personal. The software update your children’s safety depends on already failed once. And with Waymo scaling from 450,000 to 1 million rides per week, the same infrastructure pressures driving Big Tech’s aggressive expansion are pushing autonomous vehicles into more neighborhoods, faster.

The question is not whether autonomous vehicles will eventually be safer than humans. The question is who bears the risk while the technology figures it out. Right now, the answer is children walking to school. The implications for emerging technologies broadly are the same — deployment speed consistently outpaces the regulatory frameworks meant to protect the public.

Federal Scrutiny Is Just Beginning

The NTSB’s preliminary report on the Austin violations is expected within 30 days, with a final report and recommendations in 12 to 24 months. NHTSA’s investigation into the Santa Monica collision could trigger additional recalls or operational restrictions. For a company that has operated largely on goodwill and favorable comparisons to human drivers, the next year will determine whether regulators treat autonomous vehicles as a transportation technology — or a public safety experiment being run without consent.

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The biggest bottleneck in quantum computing is not the qubits themselves. It is everything required to keep them alive — the cryogenic cooling systems, the drive wires, the room-sized energy infrastructure that makes scaling past a few thousand qubits an engineering nightmare.

A team led by CSIRO, the University of Queensland, and the Okinawa Institute of Science and Technology (OIST) just published a theoretical framework that attacks this problem directly. Their paper, “Powering Quantum Computation with Quantum Batteries,” appeared in Physical Review X on January 29, 2026.

Quantum Batteries Recycle Energy Instead of Wasting It

The core idea: embed quantum batteries inside the quantum computer itself. Unlike classical power sources that dump energy into the system from the outside, quantum batteries maintain quantum coherence with the qubits they power. Energy gets recycled rather than dissipated as heat.

Dr. James Quach, CSIRO’s quantum batteries research lead, explained that the computers use significantly less energy because internal quantum batteries recycle energy within the system. The team’s modeling shows this approach achieves near-zero energy dissipation for certain computations.

The practical result: fewer wires, less heat, and four times more qubits packed into the same physical space.

The Math Behind the Multiplier

Lead author Yaniv Kurman, a CERC postdoctoral fellow at CSIRO, and co-authors Kieran Hymas, Arkady Fedorov, and William J. Munro demonstrated that initializing a bosonic quantum battery in a Fock state can supply the energy for arbitrary unitary gates regardless of circuit depth. Allowing quantum battery-qubit entanglement during computation lowers the initial energy requirements below previously established energy-fidelity bounds.

Translation: the batteries do not just power the machine — they make the computations themselves more efficient.

Speed Gets a Boost Too

The modeling revealed an unexpected benefit. The architecture enables quantum superextensivity — a phenomenon where adding more qubits actually makes each qubit operate faster. More scale means more speed. That flips the conventional scaling problem on its head.

Laterstack Editorial Take

Laterstack exists to sharpen critical thinking by connecting tech, policy, and power to everyday life — across class, industry, and influence. The quantum computing race is fundamentally an infrastructure race disguised as a science race. The companies and nations that solve the energy and cooling bottleneck first will control the hardware layer that everything else — cryptography, AI training, drug discovery — depends on. CSIRO publishing this openly in Physical Review X rather than locking it behind corporate R&D is a deliberate strategic move. Watch who licenses it.

What This Means for Everyday People

Quantum computers will not show up in your living room. But they will reshape the systems you depend on — from how your financial assets are secured to how new medicines get developed. The energy problem is the gate. If quantum batteries work in practice the way they work in theory, that gate opens wider and faster than current timelines project. Every government and major tech company tracking quantum supremacy is watching this paper closely.

The work is theoretical. Experimental validation is next. But the team says the approach is feasible within existing quantum hardware platforms — which means this is not a decade-out concept. It is an engineering challenge with a clear path forward.

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OpenAI announced it will start serving ads inside the free version of ChatGPT and its $8 per month ChatGPT Go over the coming weeks. The move is long expected, but it raises questions about how a company known for AI innovation balances revenue with user trust.

The company reached $13 billion in revenue last year and expects to triple that this year, according to an anonymous source. Most of that revenue is being spent on cloud services and data centers to support AI infrastructure. OpenAI plans to spend $115 billion between 2025 and 2029, a figure that dwarfs the budgets of most tech companies.

Ads in ChatGPT will not change the answers it provides, OpenAI says, nor will advertisers influence the responses. Still, the method of ad delivery is unlike anything seen on the web. Chatbots generate text instead of web pages, which makes standard display ads impossible. Instead, OpenAI will tailor ads based on the questions users ask and prior queries, with an option to disable personalization.

This approach exposes the tension between AI monetization and the trust users place in the service. ChatGPT is used for everything from coding to personal advice. If users start to perceive any subtle influence from advertising, the credibility of the platform could erode.

It also highlights the scale of AI’s infrastructure demands. OpenAI will use Cerebras chips that consume hundreds of megawatts of electricity, equivalent to powering tens of thousands of households. OpenAI is not alone; companies like Microsoft and Google are also investing heavily in global AI compute, with significant cost and environmental considerations.

This is a moment where technology, business, and ethics intersect. Every ad served is a decision about how much users pay with attention and how much companies pay for compute. AI growth has costs that go beyond money, and users are only beginning to notice the trade-offs.

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Four years ago, two corporate developers from New Jersey were tinkering with Ethereum rigs in their basements. They spent tens of thousands of dollars trying to mine cryptocurrency, only to realize that the payoff would never match the cost. Mining was ending with a network upgrade called The Merge and the work had become tedious.

Instead of quitting, they pivoted. They converted the same computers into AI servers, long before ChatGPT or DALL-E 2 were on anyone’s radar. The software they were forced to use for GPUs was clunky and broken. Frustrated developers make for observant entrepreneurs. The problem they saw became their business.

Runpod was born to give developers a fast, flexible platform for hosting AI apps. Users could configure hardware easily, use APIs, or even deploy serverless options. It was designed for people who wanted to get actual work done without fighting the tech.

Launching in early 2022, the founders posted on a few AI subreddits offering free access in exchange for feedback. The response was immediate. Beta users converted to paying customers. Within nine months, the company had hit $1 million in revenue and the founders quit their day jobs.

Growth remained unconventional. They did not take early venture capital, instead forming revenue-share deals with data centers to expand capacity. They had to anticipate demand because if capacity ran out, users moved on. Reddit and Discord amplified adoption, but the founders were still learning how to navigate business. Their first VC call came months later after a Dell Technologies Capital partner discovered them on Reddit.

By 2024, the timing aligned. AI was exploding. Runpod’s platform had attracted 100,000 developers and secured a $20 million seed round. The company has since grown to 500,000 developers across 31 global regions, with users ranging from independent creators to Fortune 500 enterprises. The customer list reads like a who’s who of tech: Replit, OpenAI, Cursor, Wix, and Zillow.

Competition is stiff. Amazon, Microsoft, Google, and other AI-focused cloud providers dominate the space. Yet Runpod’s focus is not the cloud itself but the developers who use it. In their view, software development is changing. Programmers are becoming AI operators and creators. Runpod wants to be the platform they grow up on.

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