Screenshot from a Google announcement depicting its launch of a Nano Banana 2 feature in Google Earth. 

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Mayor Zohran Kwame Mamdani and Chief Technology Officer and Office of Technology & Innovation (OTI) Commissioner Lisa Gelobter announced the launch of "Public Interest Technology (PIT) Crew," a new initiative to deploy teams of technologists alongside City agencies to tackle public problems with in-house digital solutions at Coney Island in New York City on July 13, 2026. Credit: Katie Godowski/MediaPunch /IPX

Cities and States Can Replicate NYC’s Ambitious Public Interest Tech Crew

This image is a collage with a colourful Japanese vintage landscape showing a mountain, hills, flowers and other plants and a small stream. There are 3 large black data servers placed in the bottom half of the image, with a cloud of black smoke emitting from them, partly obscuring the scenery.

Deborah Lupton, "Servers in a Landscape" / 

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Telegram on App Store displayed on a phone screen and French flag displayed on a screen in the background are seen this illustration photo taken in Krakow, Poland on December 3, 2023. 

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 to Google Earth, giving users the ability to use its Nano Banana image generation capabilities to “transform” real world locations. After public outcry, on Friday the company 

 the new feature in order to work on “implementing stronger guardrails.”

The incident is just one more parable in a long line of product releases by AI firms with potentially dangerous consequences for information integrity. In this case, Google didn't just 

. It briefly changed one of the world's most trusted sources of evidence. For more than two decades, Google Earth has earned the trust of journalists, human rights investigators, open-source researchers, and even courts. Not because it is flawless, but because it provides a stable, time-stamped record of the physical world.

That credibility has made it part of the global verification infrastructure. By integrating generative image creation, Google threatened to fundamentally 

 that relationship. Google Earth was no longer just a record of reality, but also a generator of plausible realities, collapsing the boundary between one of the world’s most trusted reference systems and a generative AI system.

This episode revealed something much bigger than a single product mistake. It showed how easily generative AI can undermine the trust embedded in public information infrastructure when governance fails to keep pace and when companies prioritize commercial and consumer priorities over reality resiliency. And it is just another point of evidence as to why companies like Google must more actively engage with the civil society experts who could have seen this coming.

At WITNESS, we warned about exactly this in our 

2024 report on generative AI and conflict

. One of the key risks we identified was the ability to generate synthetic evidence of military operations, humanitarian crises, and damaged infrastructure tied to real places, making verification during conflict substantially harder.

We have already seen how this plays out. For example, during the recent Iran-Israel-US war, 

 purporting to show destroyed US military infrastructure spread widely online. The fabricated images were built from authentic Google Earth imagery of a US naval base in Bahrain, adding convincing synthetic destruction while preserving real geographic features. They accumulated hundreds of thousands of views before BBC Verify and other investigators established they were fake. None of this should have come as a surprise. We argued about this in our December 2025 Reuters Institute article on how AI is 

 exactly how these risks emerge in practice.

The greatest risk is not a single convincing fake. It is the normalization of ambient uncertainty: an information environment where authentic evidence, synthetic media, misleading forensic analyses, and faulty AI detection results circulate together until distinguishing reality becomes increasingly difficult.

 we saw exactly that. Genuine evidence was dismissed as AI. Fake evidence was used 

 were weaponized to undermine authentic reporting. The objective was no longer to convince people of a lie; it was to make reality itself feel unknowable.

Google’s rollback should be welcomed. But pausing one feature does not resolve the deeper issue that its brief deployment exposed. Google 

 the capability with examples like creating "a rendering of your future house nestled perfectly in the actual landscape." But products should not be evaluated solely by their intended use cases. They must also be judged by their foreseeable misuse. In this case, the potential misuse cases made it obvious that meaningful consultations with media and civil society organizations would have identified them before launch.

And companies like Google also have a responsibility when they recklessly expand the Overton window on what is acceptable or normal to manipulate. It’s true that it doesn’t require a Google tool to manipulate a satellite or geo-image—and that satellite and geospatial images were photoshopped before this (as 

 in its MH 17 investigations), and also that other tools could be used to achieve similar results. Satellite imagery has been a target of concern in deepfakes analysis and 

 since the earliest days. But there is an enormous difference between something being technically possible and being seamlessly integrated into one of the world’s most trusted geographic platforms. By making this capability native, frictionless, and globally accessible, Google dramatically lowered the barrier to producing location-based fabrication while normalizing manipulation on a hitherto relatively ground-truthed source.

The problem is compounded by a verification ecosystem that is already struggling to keep up with the increasing volume and variety of synthetic content. Today's AI detection systems were largely built to identify manipulated faces and biometric signals—not synthetic environments, landscapes, damaged infrastructure, smoke, satellite imagery, or disaster scenes. As

, the verification ecosystem is fundamentally mismatched to the kinds of deceptive content that increasingly matter in conflict, climate disasters, and humanitarian crises.

This is also a verification ecosystem where plausible deniability is routinely deployed to dismiss real content as artificially generated. The history of satellite imagery in human rights work is of its use to prove war crimes in 

; competing with falsified images from the ground that claimed to prove events didn’t happen or that 

 Now, the satellite imagery can be generated to reinforce the lie, and the ‘liar’s dividend’ deployed to dismiss what has been a hitherto trusted source.

Perhaps most striking is how casually this capability was introduced. Google's initial response emphasized safeguards largely focused on downstream mitigations such as SynthID watermarking and content restrictions. Those are useful measures, but they are not an adequate governance strategy. Products that become part of the world’s verification infrastructure require fundamentally different standards for deployment––this is a feature that reshapes one of the world's most trusted sources of visual evidence, and that places yet another assumption of ‘must verify everything you see on the internet’ upon us all.

Platform capabilities with this level of societal impact demand rigorous pre-deployment risk assessments, meaningful consultation with affected communities and civil society experts, and safeguards proportionate to the risks. Watermarking is an important mitigation, but it is not a substitute for responsible product design. In the real world, images are screenshotted, cropped, compressed, reposted, and stripped of provenance within minutes. They leave Google's ecosystem almost immediately, while the burden of sorting truth from fiction falls on journalists and investigators using verification tools that already struggle to analyze precisely this type of content.

That is exactly backwards. The burden should not fall on verification communities to absorb the consequences of increasingly powerful generative systems while tech firms profit. Companies introducing technologies that reshape the evidentiary landscape should have to demonstrate that they can be deployed safely before they become globally available, not ask the rest of the information ecosystem to adapt after the fact. Google’s decision to pause the feature suggests those concerns are now being taken seriously. The question is why they were not central to the product development process from the outset.

Google Earth will almost certainly remain an invaluable public resource. But by merging trusted geographic reference imagery with on-demand AI generation, Google risked changing more than a product. It threatened assumptions that underpin visual verification itself. If companies want to build tools that become part of the world's information infrastructure, they must stop treating them as consumer creativity products, and must be governed accordingly as systems that shape public trust, reporting, and accountability.

So what comes now? Google says it will work on better “guardrails.” But are guardrails enough when the fundamental accessibility, ease and promotion of the tool itself undermine our shared trust infrastructure in favor of novelty, humorous overlays, dicey fakes and easier real estate visualization?

New York City is working to answer a critical question facing cities and states: How do we put people first in a modern, digital era to fundamentally change how government operates—and what can people expect from it?

What’s most notable is the way the city is answering this question: by giving a new dedicated digital delivery team the internal support, empowerment, and structural authority to prioritize people's needs over bureaucratic processes.

—nicknamed “the PIT Crew.” The PIT Crew is a new government team made up of product managers, designers, engineers, user researchers and data experts to team up with City agencies to build digital solutions to thorny public problems. One of the Crew’s first projects will be to partner with the Department of Consumer and Worker Protection to build an online service where New Yorkers can easily file complaints against companies that place customers in hard-to-cancel, costly subscriptions.

For New Yorkers, that work is the point of government. It's also a test of whether government can reset what people expect from public services both in New York and around the country.

talking to more than 100 government leaders

 who have delivered for people across local, state, and federal levels. We ourselves have built, led, and supported successful delivery teams around the country, including leading IRS’ Direct File. What we’ve learned is that when teams can organize around outcomes, not bureaucracy, they can truly move at the speed that government’s biggest challenges require.

New York City is looking to do this at scale. What can your city learn from the NYC PIT Crew—not just the team, but the theory of transformation? The answer lies in five principles that should become the standard operating procedure for digital capacity initiatives everywhere.

1. Set a standard that changes everything

Most government digital projects aim for incremental improvements, setting a goal of “better than last time.” That’s insufficient. The IRS Direct File showed what’s possible when you aim higher: services so intuitive, accessible, and reliable that residents come to expect government to just work.

Rather than settling for mere “improvement,” New York City—and other cities and states—should adopt aggressive user experience standards and measure success against private-sector benchmarks, not against other government agencies.

2. Organize around people’s needs, not org charts

Government is siloed by design. People don't experience government by agency. They experience it in moments: applying for a job, starting a business, navigating a health crisis. When a new parent needs childcare support, they don't care whether it's "Housing's job" or "Labor's job." They care that it works.

The most effective delivery teams organize themselves around people’s journeys. They break down silos in funding, staffing, and operations. Every state and city that wants to improve outcomes should ask: 

Are we organized to solve problems for residents, or for ourselves?

3. Empower teams to own outcomes end-to-end

Accountability in government is notoriously diffuse. This is where most initiatives die—not on launch day, but six months later, when the process starves what shipped. Successful digital capacity initiatives change that. A single team owns a service, is transparent about performance, and has the authority to make real changes. This requires giving teams:

Clear responsibility for measurable outcomes

Authority to hire, procure, and design without permission layers

Budget control to avoid the classic government trap of funding being held hostage by competing departments

Local governments serious about scaling impact should pilot this model in one high-impact service and measure results after 90 days.

This is where most digital initiatives fail: they build something beautiful and then get strangled by traditional government processes. Budget cycles, hiring freezes, and procurement bureaucracy can kill momentum faster than any technical failure.

The answer is straightforward in theory, hard in practice: apply agile, outcomes-driven approaches to 

—hiring, procurement, budgeting and policy approval. New York City's commitment to this approach through its 

Commission on Government Efficiency (COGE)

 is encouraging. Other governments should move faster in this direction.

5. Build openly and aim for replicability

When government solves a problem well, it should share the solution. Open-source code, reusable service components, documented playbooks—one city's solution becomes every city's starting point. One city shouldn't have to reinvent the wheel when another has already built it.

Governments should compete on outcomes, not on proprietary approaches. The team that figures out how to streamline unemployment benefits should publish that playbook. The agency that builds accessible design systems should share them.

The launch of the New York City PIT Crew is encouraging because it signals that leadership understands what’s needed: in-house talent, organizational authority, and political cover. That’s rare. Most governments talk about “innovation” while structurally making it impossible.

But the launch of a team isn't success—delivery is. And delivery at scale requires the principles above.

The opportunity in front of governors, mayors, and city administrators is clear: Don't wait to see if New York City's model works. Begin building your own version now, before a disaster strikes and you need your own PIT crew to clean it up. Organize a team around the highest-impact resident experience problem in your area. Give them authority. Measure relentlessly. Share what you learn.

People in other states and cities shouldn't have to wait for their government to deliver what New Yorkers will soon expect as standard. That gap—between what's possible and what's normal—is where innovation lives.

It's now up to other governments around the country to close it.

Telegram on App Store displayed on a phone screen and French flag displayed on a screen in the background are seen this illustration photo taken in Krakow, Poland on December 3, 2023. (Photo by Jakub Porzycki/NurPhoto via AP)

A multilingual network of Telegram channels offering local news does not appear to be what it seems. It seems there is no newsroom behind any of it. Independent researchers working separately, including us at Democracy Reporting International, have reached the same conclusion about this cluster of growing Telegram channels: They belong to a coordinated operation spreading disinformation and propaganda aligned with the Kremlin that shares the same script, pushes biased and unverified content, translated and timed to hit everywhere at once.

Our research reveals how easily a network can significantly grow across many languages in a way that is difficult to detect, which is why we argue that European authorities should reconsider their light regulatory approach to Telegram.

During social media monitoring of the Hungarian elections earlier this year, DRI 

 seven of those previously uncovered channels, along with an additional twelve accounts that nobody had connected to this network. All 19 channels share the same signature of coordinated inauthentic behavior, and they never link or mention one another. Yet they all have similar names (translating roughly to "common sense"), and all but one share identical branding.

The "Common Sense network,” as we call it, shows two kinds of growth: vertical (rising subscriber counts within existing channels) and horizontal (new channels added over time). They tell different stories.

Subscriber numbers function as social proof. At its peak last winter, the network had over 220,000 subscribers; by early April it had dropped to 167,000, and it now sits under 100,000. We are confident the number will rise again, as it did last year, when each channel suddenly surpassed 10,000 subscribers almost overnight. The sudden spikes are consistent with purchased or otherwise inauthentic accounts. However, an unsuspecting German reader who does not spend time verifying a Telegram channel's legitimacy, and who encounters a channel "Die Stimme der Vernunft" which is posting regularly and has 40,000 subscribers, will likely conclude it is a serious channel. There is little to indicate that he or she has just walked into a network repeatedly linked to foreign information manipulation and interference (FIMI) operations.

Horizontal growth is a less ambiguous indicator. Nineteen channels across nineteen languages are themselves a significant footprint, and the more revealing insight is how the network came together. We reconstructed its horizontal expansion using data from the third-party service Telemetrio combined with systematic interval sampling of published messages via the Telegram API.

Step-line chart titled 'Network Growth Timeline (2022–2026): 19 channels across 19 languages,' showing the cumulative number of Telegram channels in the Common Sense network rising from 0 to 19 between November 2021 and May 2026. Growth occurs in discrete jumps: March 2022 adds 2 channels (PL, CS); June 2023 adds 1 (DE); August 2023 adds 6 (ES, HU, FR, IT, EL, NL); July 2024 adds 1 (EN); October 2024 adds 1 (SK); March 2025 adds 1 (RO); July 2025 adds 6 (SR, BG, HE, TR, FI, AR); and March 2026 adds 1 (SW). A shaded green region marks confirmed dates through mid-2025, and a shaded orange region marks partially estimated dates after August 2025. The largest single-day expansions—six new language channels each—occurred in August 2023 and July 2025.

Reconstruction of the Common Sense network's horizontal growth (N=19). The network may include additional channels that went undetected. Creation dates for confirmed channels are known exactly; for estimated channels, dates are approximated to a two-week window.

None of this growth, in either direction, is likely to be noticed by Telegram. A network that can double its subscriber count overnight, or add six new language audiences within days (as it did in August 2023 or July 2025) operates without triggering any visible intervention by the platform.

The obvious question is why such a network can continue expanding despite repeated public exposure.

The story with Telegram and EU regulation is also about numbers. Under the DSA, a platform becomes a Very-Large Online Platform (VLOP) if it has more than 45 million active monthly users. Telegram's 

 states they are still below that threshold. Its obligations are therefore largely limited to tackling illegal content: providing users with mechanisms to report it, acting on those reports, and protecting minors' privacy and safety. Very Large Online Platforms and Search Engines (VLOPSEs) obligations, such as identifying and mitigating systemic risks, do not apply.

The “common sense network” relies on purchased bots, fake engagement, and automated or semi-automated activity to artificially amplify certain content and create the false impression of broader public interest than it actually has. While the content is not illegal, such inauthentic “news channels” undermine the integrity of the public debate. If Telegram were designated a VLOP, it would have to assess and mitigate systemic risks associated with coordinated manipulation.

Nothing, however, prevents Telegram from moderating this type of behavior voluntarily. 

, another non-VLOP, already prohibits harassment, spam, disruptive behavior, impersonation, and other forms of coordinated abuse under its community rules. Telegram has instead chosen a far more hands-off approach, at times even resisting l

Even where Telegram’s obligations are clear, its moderation record falls short of comparable platforms. On July 23, the 

 over the past 180 days. A higher volume of moderation doesn't necessarily mean better protection for users. It can also reflect more user reporting or over-removal. Even so, the gap with its closest counterpart is significant: WhatsApp Channels, comparable in design and user base, recorded 

 over the same period -roughly sixteen times as many.

Nor can this gap be attributed to a lack of content requiring moderation. Civil society organizations have recently documented illegal content on Telegram, including 

 (NCII), and child sexual abuse material (CSAM). Last year, the Belgian Institute for Postal Services and Telecommunications (BIPT) –the Digital Services Coordinator responsible for overseeing Telegram’s compliance with the DSA– separately identified hundreds of 

Taken together, these factors point to a gap: a platform with considerable reach and influence in the European Union is operating under a significantly lighter regulatory burden than its 

A path forward for stronger Telegram accountability

The most direct path is VLOP designation. Pressure is building on the European Commission to examine Telegram's status. In March 2025, a 

 the Commission to clarify the methodology behind Telegram's EU user count, pointing to independent research that suggests the real figure exceeds 45 million. The 

 was noncommittal: it said only that it was monitoring Telegram's figures, following market developments, and remaining in contact with relevant authorities.

Telegram's reported figures do raise legitimate questions. In February 2024, the company 

 average monthly active recipients in the EU. Since then, it has offered only the claim of "less than 45 million," even as its global user base has 

. That the EU figure has stayed just under the threshold throughout this period merits closer examination, particularly since WhatsApp Channels, Telegram's closest equivalent in design and use, crossed the threshold and was 

Part of the problem is methodological. Article 24(2) of the DSA gives the Commission the power to adopt a delegated act detailing the methodology of how "active recipients" should be counted, but it has not yet exercised that power, relying instead on 

. According to this guidance, an “active recipient” is anyone exposed to content disseminated through a platform's online interface. Public Telegram channels can be viewed, searched, indexed, and shared by anyone, whether or not they subscribe. If exposure to content is the relevant standard, as the DSA suggests, there are reasonable grounds to question whether Telegram's reported figures fully capture the reach of its public channels. Greater transparency about how the methodology should be to calculate those figures would help answer that question.

In the meantime, the European Commission and the Belgian regulator have tools available to strengthen Telegram’s accountability. The Belgian regulator could use its supervisory powers to examine Telegram’s compliance with its existing obligations on illegal content and child safety and open enforcement action where necessary. Research from civil society organizations and independent investigators is a crucial source of evidence to push for these efforts.

also shows that content identified on Telegram circulates across VLOPs, including Facebook, X, Instagram, YouTube, and TikTok. While our analysis does not prove coordinated cross-posting, it suggests that disinformation networks operate across multiple platforms. The DSA already provides a systemic risk framework that encourages stronger cooperation between VLOPs, VLOSEs, and platforms such as Telegram to identify and disrupt campaigns that move across services.

Commission's DSA Guidelines on mitigating systemic risks to electoral processes

 can serve as a useful benchmark for platforms such as Telegram. Although they cannot create new legal obligations for non-VLOPs, they can establish clearer expectations for addressing coordinated manipulation and other threats to electoral integrity.

Raffi Krikorian is Chief Technology Officer at Mozilla.

In Washington, AI is increasingly being treated as something that needs to be controlled. The government believes that AI is, first and foremost, a national security asset, meaning that it must be sequestered to prevent enemies from gaining an advantage. On the other side of the world, in Beijing, the approach is moving in the opposite direction. China is reducing barriers, encouraging adoption, and using open-source AI as a way to spread Chinese-developed technology across global markets.

There is now a fundamental divide. The United States is betting that control is the path to preserve its lead. China, instead, is betting on diffusion. The country whose technology is adopted most widely may ultimately shape the future of AI. Questions over open source and open weights sit at the center of that contest.

Beginning on July 24, high-profile support for open source moved what is often a debate behind closed doors into the public sphere, where it belongs: Nvidia’s Jensen Huang’s 

 signed by 35 companies — including Palantir, Andreessen Horowitz and Microsoft — warning Washington not to over-restrict open source software. “Open models strengthen safety and cybersecurity, accelerate innovation and diffusion, and enable sovereignty,” Huang wrote, leading the likes of Elon Musk and Mark Zuckerberg to post their support. 

Mozilla signed it because, despite being a very different company from many of the signatories and not always seeing eye to eye, we believe in the spirit and substance of the letter, particularly that 'openness may be one of the most important paths to AI safety and security’. The letter was followed by the 

 of the Open Secure AI Alliance for AI Safety and Security, which aims to “build and share open tools that promote responsible use of and trust in AI.”

The battle is on. Here’s what’s behind it.

Open models now do about a third of the world's AI work, but they collect only about four percent of the money.

Those two numbers, taken from Mozilla's new “

, provide more context than the entire AI safety conversation. The report tells a story of a performance gap between open models — the ones whose weights anyone can download, run, and adapt — and the best proprietary systems. While the capabilities are still a jagged frontier, on average, the performance has narrowed sharply over the past year. Costs keep falling. Seventy-nine percent of developers now build with open models. And yet the money hasn't followed the usage.

This is where the battle lies. A third of the work, but only four percent of the money — that gap is the prize.

The battle started almost exactly three years ago, when Anthropic's Dario Amodei 

 that advanced open-source AI is on a "very dangerous path." On the surface, his argument is pretty simple: once a model's weights are public, no one can monitor abuse or revoke access. Once released, an open weights model can't be “unreleased.” But let’s ask ourselves what that revoke switch actually does. Revocation is not a feature of the model, it is in the API contract. That means it only applies to a lab's own customers — and the people in Amodei's threat model were never customers. The labs shipping frontier-class open weights — such as DeepSeek, Alibaba, Mistral, and Moonshot (with its just-released, 2.8T-parameter model Kimi) — mostly sit outside Washington's reach anyway. Two million open models already sit on Hugging Face; many run on a laptop. That means that in the real world, there's no single kill switch to throw.

This distance between what such a regulatory switch claims to control and what it actually does is what's missing from the debate over which models are "safer." It's also the key to the fight over who captures AI's value.

To the companies that built the proprietary models, value is about maintaining a privileged position and using everything at their disposal to protect it — policy, pricing, and technology. For everybody else, value means the ability and power to shape, audit, and improve the systems we all depend on. And increasingly, that power doesn't live in the model at all.

For instance, right now, two developers can take the identical open model and ship completely different products: a scam-call operation or a nurse-advice hotline. The model doesn’t know (or care) about the difference. What is making the actual decisions is the layer of software built around it — the agentic harness — that sits between users and the model, determining what the application can access, remember, and act on.

As models get cheaper, not to mention more interchangeable, that harness is where the power is actually going. And it's being quietly locked up by the big labs. Farmers know how this story goes. They bought their tractors outright, but the manufacturer kept the keys to the software, making the farmers owners on paper but renters in practice. It took years of lawsuits — and, 

, the Federal Trade Commission — to start prying that lock back open. A similar arrangement is now being built for the software that reads your email, books your travel, and remembers every detail of your life.

This isn't an accident of engineering; it's a business model. A closed wrapper makes money by making itself expensive to leave. An open one can't lock the door, so it survives only by staying worth using. Same underlying technology, opposite incentives. It's the reason the value captured by open models sits at four percent while their usage sits at a third. The real question for all the builders right now isn’t which model you’re using. Rather, it's whether you could leave for a different one.

The debate that matters isn't really which models get released or which get regulated; it's who controls the layer wrapped around them. That's being decided right now — mostly by developers who don't realize they're the ones responsible. For a glimpse of the future, we can look to the internet: it exists as it does today because, when the architecture was still up for grabs, developers chose HTML and HTTP over proprietary walled gardens like AOL. AI is at that same juncture now, and the fact that two million open models already exist suggests plenty of builders have shown up early. That window doesn't stay open on its own, and it doesn’t stay open forever. It stays open because people keep choosing it.

For developers, four habits matter most in ensuring an open future:

Build on open harnesses, not just open models.

 The orchestration layer above the weights is where capability is concentrating, and closed labs are already welding it shut. Keeping it open takes deliberate effort.

Store accumulated context in portable controllable formats, so it's retrievable if a vendor changes its terms rather than trapped inside one.

 Integrate an open model and keep it production-ready even while running primarily on a closed API, so switching is cheap if it becomes necessary.

Open models skew toward particular regions and providers; keeping this layer genuinely open means actively supporting a geographically distributed set of options, not defaulting to whichever model is cheapest this quarter.

None of this requires believing anyone is acting in bad faith. It's worth noticing, though, that the loudest safety arguments arrived right around the time models got cheap enough for the real competition to move up a layer. That's not evidence of a conspiracy — it's just where the incentives point, and it's why so much of the current debate is aimed at the wrong target.

More evidence is in our report, and most of it is good news: performance gaps closing, costs collapsing, millions of developers building. The question in front of developers isn't whether AI is dangerous — it's whether they'll hold the keys to the machines they're building. The question for governments is whether the keys they’re reaching for turn anything at all. For now, that door is still open. Let’s work together to keep it that way.

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