Tech

Unintended Consequences of Video Surveillance

Published

2 months ago

June 10, 2026

A man raises his phone as police move into a crowd. The video is shaky, loud, immediate. Within minutes, it is online. Within hours, it is everywhere. This is how accountability works now. Something happens, someone records it, and that footage can show what really happened, sometimes contradicting official accounts. It can empower citizens and create consequences for officials.

But the footage’s life cycle does not end there.

In recent months, civil liberties groups have warned that adding facial recognition to consumer smart glasses could turn everyday recording into something more troubling: real-time facial identification. It reflects a broader shift already underway, where images and videos captured for one purpose can later be searched, matched, and used for another.

An ouroboros is an ancient Egyptian symbol, a snake or dragon eating its own tail. As I began to see patterns in my broader research on surveillance corporatism and governance lag, I began using the term “surveillance ouroboros” to describe this recursive pattern of observations intended to hold power accountable becoming new input for the same surveillance infrastructure.

Facial recognition changes accountability

During the George Floyd protests in 2020, people filmed police in real time. Phones were pointed at officers, not at each other. The goal was simple: to show what the state was doing. That footage spread quickly and became part of a much larger pool of public data.

At the same time, reporting from outlets including The New York Times and BuzzFeed News showed that law enforcement agencies were using facial recognition tools, including systems built by Clearview AI. Those systems were built from billions of images scraped from across the internet, including publicly available photos and videos.

The basic approach is now routine: People record the state, or anything else—as in the January 6 attack on the U.S. Capitol—and the state compiles that footage and data into a searchable environment, which may later be used to identify some of the same people who made the footage.

Facial-recognition systems used by law enforcement are increasingly outpacing the legal safeguards.

A 2024 Government Accountability Office review found that federal law enforcement agencies continued to expand their use of facial-recognition systems for criminal investigations despite ongoing concerns around training, privacy protections, civil-liberties safeguards, and oversight. Earlier GAO findings showed that agencies had conducted roughly 60,000 facial-recognition searches before formal training requirements were put in place for personnel using the systems.

The American Civil Liberties Union and other groups have warned that these tools could be used to identify people from images shared online, including protest-related footage. Concerns about facial recognition led some U.S. states and cities, including San Francisco and Boston, to restrict or ban government use of the technology, while federal agencies have continued to face scrutiny over how such systems are tested, deployed, and audited. A 2024 analysis published in Internet Policy Review warned that facial-recognition systems used by law enforcement are increasingly outpacing the legal safeguards meant to govern them, creating growing tensions around data protection, oversight, and proportional use.

The spy network that built itself

Surveillance used to require infrastructure. Cameras had to be installed and data had to be collected deliberately. That is no longer the case. People carry cameras everywhere. They record constantly and upload in real time. Events are documented from multiple angles without planning or coordination. The cumulative result is a continuous stream of usable data: faces, locations, timestamps, and interactions. The Internet of Things also waits all around us, gathering information and releasing it when people least expect it, as Andrew Guthrie Ferguson describes in a recent excerpt of his book Your Data Will Be Used Against You.

Similar dynamics are emerging globally. A recent analysis in the International Journal of Law and Information Technology examined how facial-recognition systems in China and Japan are expanding faster than the legal frameworks governing them. Reporting by The Guardian described the limited legal protections around the rapid deployment of AI-assisted surveillance infrastructure across parts of Africa.

There used to be a clear distinction between surveillance and accountability. Surveillance meant the powerful watching the people; authorities tended not to share their imagery except under duress or a court order and usually after a long delay. Accountability meant the people watching the powerful, and often publishing imagery immediately to head off or counteract official mischief. That distinction no longer holds. The same footage can serve both roles. A recording meant to expose misconduct can later be used to identify someone else entirely.

Surveillance ouroboros is not a future risk. It is already here.

This dynamic persists because people still need to record. In many places, it is one of the only tools available when formal accountability breaks down. When oversight institutions weaken or fail, public documentation becomes a substitute. In that environment, people turn to visibility. But that visibility comes with a cost. The more people that document, the more data that exists. The more data that exists, the easier it is to search, match, and store. Every video feeds the ouroboros. People are not feeding the system because they trust it. They are feeding it because the alternative is silence.

Most of the people in these videos are not the focus. They are in the background, passing by or standing nearby. But that distinction does not matter once the footage enters a system. Today’s facial recognition can identify even a face that passed through the corner of a frame. Someone who did nothing can still become part of a dataset without ever knowing it. As recognition systems improve, older footage becomes more useful, and invasive.

No single decision created this outcome. It emerged gradually through more cameras, better recognition, larger datasets, and easier integration. Each step made sense on its own. Together, they changed what recording means.

Public recording is still necessary. Without it, many forms of abuse would remain hidden. But recording is no longer just exposure. It is also contribution. If you published imagery or video last year, you may already have contributed to a system you have never seen, but the ouroboros has.

Surveillance ouroboros is not a future risk. It is already here. Every time someone presses publish, they are doing two things at once. They are exposing power, and they are helping build the system that the powerful will later use to track the less powerful.

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Tech

AI Shopping Traffic Raises Retail Customer Data Concerns

Published

2 minutes ago

August 10, 2026

qasimgillani

Retailers have spent years fighting to appear at the top of search results; now they have another gatekeeper to win over as shoppers increasingly ask AI assistants what to buy.

The shift is forcing retailers to rethink how their products are discovered online, as AI systems increasingly interpret shopping requests, compare products, and recommend purchases.

According to Reuters, many retail companies are responding by optimizing their websites and product information so AI systems can more easily understand and surface their products.

But visibility inside an AI response is only half the battle. Retailers also want shoppers to return from AI recommendations to their own sites. These retailers argue that keeping the transaction in-house gives them greater control over the purchase experience and the customer information generated by it.

E-commerce retailers also argue that they already have what it takes to handle the sales process efficiently. In a statement cited by Reuters, The Knot’s CEO, Raina Moskowitz, said they “have three decades of data” that can be used to deliver the best experience to customers.

That creates a new challenge for e-commerce: retailers must make their products easy for AI systems to discover and recommend without letting those systems become permanent middle layers between the brand and its customers.

Even Amazon Web Services (AWS) has backed that idea up, advising retail companies to ensure their platforms are the best place to buy while still leveraging AI tools for brand discovery and referrals.

AI companies that have tried to handle the entire chain end-to-end have hit customer adoption hurdles. A prominent example was OpenAI’s Instant Checkout. In March, the company discontinued the feature after five months of unsuccessful adoption, saying March, the company discontinued the feature after five months of unsuccessful adoption.
AI shopping is gaining traction
The numbers are making it harder to dismiss AI shopping as trivial.

According to Reuters, Juniper Research expects AI agents to drive $8 billion in transaction value in 2026, while Adobe Analytics found that 41% of US consumers used generative AI for online shopping in June.

The conversion data points in the same direction. Adobe also found that shoppers from AI referrals generated 41% higher revenue per visit, underscoring the high intent these shoppers bring while giving retailers a clear financial reason to compete for AI-driven discovery.
Not just an e-commerce story
What is happening in retail is part of a much bigger change: AI is fundamentally reshaping the path between a person’s question and the website that wants their attention.

ChatGPT has already become a serious alternative to traditional search for discovery, while Google is rebuilding Search around AI rather than simply defending the old list of links.

That shift changes the value of traffic itself. Reddit, for example, is increasingly important to AI search because its discussions are frequently surfaced as sources, even as the platform worries that AI-generated answers may satisfy users without sending them through to the original site.

Beyond retailers, many brands are increasingly adding AI search optimization to their dominant SEO playbook, trying to make their brands, products, and content visible inside AI-generated answers as well as traditional search results.

The winners may not always be the biggest brands, but the ones that can consistently get in front of customers wherever they choose to search — while keeping enough of their moat intact to turn that visibility into a lasting advantage.
Also read: Meta’s new Facebook Marketplace Seller app uses AI to create listings while keeping inventory, buyer messages, and performance data connected to Marketplace.

Tech

AI Shopping Traffic Raises Retail Customer Data Concerns

Published

9 minutes ago

August 10, 2026

qasimgillani

Retailers have spent years fighting to appear at the top of search results; now they have another gatekeeper to win over as shoppers increasingly ask AI assistants what to buy.

The shift is forcing retailers to rethink how their products are discovered online, as AI systems increasingly interpret shopping requests, compare products, and recommend purchases.

According to Reuters, many retail companies are responding by optimizing their websites and product information so AI systems can more easily understand and surface their products.

ChatGPT has already become a serious alternative to traditional search for discovery, while Google is rebuilding Search around AI rather than simply defending the old list of links.

Tech

Discovered Materials is playing AI whack-a-mole to hunt cooler chips

Published

2 hours ago

August 10, 2026

qasimgillani

Chips running AI workloads are too hot: That’s one reason why data centers consume so much electricity and require cooling systems. And, inevitably, entrepreneurs are turning to AI to solve the problem it created.

Discovered Materials is the latest, with plans to use swarms of AI agents to find new materials that can be used to build more efficient integrated circuits. The startup said it recently closed a $9 million seed round from Lightspeed India Partners after emerging from from Y Combinator, with investment from Peak XV Partners and angel investors Paul Graham, Gokul Rajaram, and Thariq Shihipar.

Founders Advaith Sridhar and Akash Ramdas teamed up to launch the company, drawing on Ramdas’ experience earning a doctorate in materials science from Stanford, and Sridhar’s work on agents at Persona AI and Luma Labs.

The two have created a software pipeline that uses Anthropic models in a custom harness to generate material leads, and then turns to foundational physics models they’ve trained to run simulations that verify if the candidate materials are actually of interest.

“[Ramdas] was doing maybe 20 guesses a day during his PhD,” Sridhar told TechCrunch. “We’re able to do thousands of guesses a day now by having these agents run 24/7 on the cloud, exploring research directions that he gives them.”

Discovered Materials released examples of hundreds of new materials today, as well as their “Material Discovery Bench” today, which is designed to track how frontier models take on this challenge.

Companies like MatNex, SandboxAQ, and CuspAI have all launched similar efforts, but Discovered Materials is betting that a laser-focus on the thermal problems of semiconductor materials is the path to success. The startup says it has already discovered several materials that match the properties of existing materials used by major chipmakers, but can’t share more details about them.

One challenge is the engineering trade-space: If they find a material that might reduce heat generation or improve dissipation, it might be too difficult to actually manufacture a chip out of it, or its electrical properties are compromised.

“It’s a bit of playing whack-a-mole with atomic structures,” Hemant Mohapatra, the Lightspeed partner who led this round, told TechCrunch. “A material is only useful in the real world if all of them converge at once, which is what makes this a really interesting search problem.”

Mohapatra expects that the business of predicting novel substances will be commoditized as models continue to improve. The difference with Discovered Materials is Ramdas’ deep experience in the field, and the ability to run a lab that can rapidly experiment and validate the candidates — something he says the two founders have already done with several new materials.

When they find valuable candidates, Sridhar says the company will attempt to patent the use of the materials in GPUs, or the process by which chips can be made out of the substance, licensing them out to chipmakers. He hopes that they will have new materials worth patenting in the next year.

However, for all the excitement, we still haven’t seen any drugs or materials discovered by AI actually make a commercial impact. The closest is perhaps Insilico Medicine’s Renterosib, the first drug discovered with generative AI to make it into a Phase II clinical trial. On the materials side, promising candidates have been found, like MatNex’s rare-earth free permanent magnets or new semiconductor materials worked out by Panasonic and Citrine Informatics. But these haven’t been commercially deployed at scale yet.

These techniques may be coming into their own now as AI continues to improve, but it’s one reason why Mohapatra says that he doesn’t believe finding more candidates is the hold-up for AI materials science; instead, “filtering them correctly and synthesizing them is the bottleneck.”

While Sridhar believes that Discovered Materials’ unique data and expertise will help the startup compete with deep-pocketed frontier labs, he acknowledged that the reality is that “a lot of this will involve actually going into wet labs and like making things as well. And this is the process that cannot be sped up.”

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