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When people talk about UX, it’s usually about the things they can see and interact with, like wireframes and prototypes, smart interactions, and design tools like Figma, Miro, or Maze. Some of the outputs are even glamorized, like design systems, research reports, and pixel-perfect UI designs. But here’s the truth I’ve seen again and again in over two decades of working in UX: none of that moves the needle if there is no collaboration. Great UX doesn’t happen in isolation. It happens through conversations with engineers, product managers, customer-facing teams, and the customer support teams who manage support tickets. Amazing UX ideas come alive in messy Miro sessions, cross-functional workshops, and those online chats (e.g., Slack or Teams) where people align, adapt, and co-create. Some of the most impactful moments in my career weren’t when I was “designing” in the traditional sense. They have been gaining incredible insights when discussing problems with teammates who have varied experiences, brainstorming, and coming up with ideas that I never could have come up with on my own. As I always say, ten minds in a room will come up with ten times as many ideas as one mind. Often, many ideas are the most useful outcome. There have been times when a team has helped to reframe a problem in a workshop, taken vague and conflicting feedback, and clarified a path forward, or I’ve sat with a sales rep and heard the same user complaint show up in multiple conversations. This is when design becomes a team sport, and when your ability to capture the outcomes multiplies the UX impact. Why This Article Matters Now The reason collaboration feels so urgent now is that the way we work since COVID has changed, according to a study published by the US Department of Labor. Teams are more cross-functional, often remote, and increasingly complex. Silos are easier to fall into, due to distance or lack of face-to-face contact, and yet alignment has never been more important. We can’t afford to see collaboration as a “nice to have” anymore. It’s a core skill, especially in UX, where our work touches so many parts of an organisation. Let’s break down what collaboration in UX really means, and why it deserves way more attention than it gets. What Is Collaboration In UX, Really? Let’s start by clearing up a misconception. Collaboration is not the same as cooperation. Cooperation: “You do your thing, I’ll do mine, and we’ll check in later.” Collaboration: “Let’s figure this out together and co-own the outcome.” Collaboration, as defined in the book Communication Concepts, published by Deakin University, involves working with others to produce outputs and/or achieve shared goals. The outcome of collaboration is typically a tangible product or a measurable achievement, such as solving a problem or making a decision. Here’s an example from a recent project: Recently, I worked on a fraud alert platform for a fintech business. It was a six-month project, and we had zero access to users, as the product had not yet hit the market. Also, the users were highly specialised in the B2B finance space and were difficult to find. Additionally, the team members I needed to collaborate with were based in Malaysia and Melbourne, while I am located in Sydney. Instead of treating that as a dead end, we turned inward: collaborating with subject matter experts, professional services consultants, compliance specialists, and customer support team members who had deep knowledge of fraud patterns and customer pain points. Through bi-weekly workshops using a Miro board, iterative feedback loops, and sketching sessions, we worked on design solution options. I even asked them to present their own design version as part of the process. After months of iterating on the fraud investigation platform through these collaboration sessions, I ended up with two different design frameworks for the investigator’s dashboard. Instead of just presenting the “best one” and hoping for buy-in, I ran a voting exercise with PMs, engineers, SMEs, and customer support. Everyone had a voice. The winning design was created and validated with the input of the team, resulting in an outcome that solved many problems for the end user and was owned by the entire team. That’s collaboration! It is definitely one of the most satisfying projects of my career. On the other hand, I recently caught up with an old colleague who now serves as a product owner. Her story was a cautionary tale: the design team had gone ahead with a major redesign of an app without looping her in until late in the game. Not surprisingly, the new design missed several key product constraints and business goals. It had to be scrapped and redone, with her now at the table. That experience reinforced what we all know deep down: your best work rarely happens in isolation. As illustrated in my experience, true collaboration can span many roles. It’s not just between designers and PMs. It can also include QA testers who identify real-world issues, content strategists who ensure our language is clear and inclusive, sales representatives who interact with customers on a daily basis, marketers who understand the brand’s voice, and, of course, customer support agents who are often the first to hear when something goes wrong. The best outcomes arrive when we’re open to different perspectives and inputs. Why Collaboration Is So Overlooked? If collaboration is so powerful, why don’t we talk about it more? In my experience, one reason is the myth of the “lone UX hero”. Many of us entered the field inspired by stories of design geniuses revolutionising products on their own. Our portfolios often reflect that as well. We showcase our solo work, our processes, and our wins. Job descriptions often reinforce the idea of the solo UX designer, listing tool proficiency and deliverables more than soft skills and team dynamics. And then there’s the team culture within many organisations of “just get the work done”, which often leads to fewer meetings and tighter deadlines. As a result, a sense of collaboration is inefficient and wasted. I have also experienced working with some designers where perfectionism and territoriality creep in — “This is my design” — which kills the open, communal spirit that collaboration needs. When Collaboration Is The User Research In an ideal world, we’d always have direct access to users. But let’s be real. Sometimes that just doesn’t happen. Whether it’s due to budget constraints, time limitations, or layers of bureaucracy, talking to end users isn’t always possible. That’s where collaboration with team members becomes even more crucial. The next best thing to talking to users? Talking to the people who talk to users. Sales teams, customer success reps, tech support, and field engineers. They’re all user researchers in disguise! On another B2C project, the end users were having trouble completing the key task. My role was to redesign the onboarding experience for an online identity capture tool for end users. I was unable to schedule interviews with end users due to budget and time constraints, so I turned to the sales and tech support teams. I conducted multiple mini-workshops to identify the most common onboarding issues they had heard directly from our customers. This led to a huge “aha” moment: most users dropped off before the document capture process. They may have been struggling with a lack of instruction, not knowing the required time, or not understanding the steps involved in completing the onboarding process. That insight reframed my approach, and we ultimately redesigned the flow to prioritize orientation and clear instructions before proceeding to the setup steps. Below is an example of one of the screen designs, including some of the instructions we added. This kind of collaboration is user research. It’s not a substitute for talking to users directly, but it’s a powerful proxy when you have limited options. But What About Using AI? Glad you asked! Even AI tools, which are increasingly being used for idea generation, pattern recognition, or rapid prototyping, don’t replace collaboration; they just change the shape of it. AI can help you explore design patterns, draft user flows, or generate multiple variations of a layout in seconds. It’s fantastic for getting past creative blocks or pressure-testing your assumptions. But let’s be clear: these tools are accelerators, not oracles. As an innovation and strategy consultant Nathan Waterhouse points out, AI can point you in a direction, but it can’t tell you which direction is the right one in your specific context. That still requires human judgment, empathy, and an understanding of the messy realities of users and business goals. You still need people, especially those closest to your users, to validate, challenge, and evolve any AI-generated idea. For instance, you might use ChatGPT to brainstorm onboarding flows for a SaaS tool, but if you’re not involving customer support reps who regularly hear “I didn’t know where to start” or “I couldn’t even log in,” you’re just working with assumptions. The same applies to engineers who know what is technically feasible or PMs who understand where the business is headed. AI can generate ideas, but only collaboration turns those ideas into something usable, valuable, and real. Think of it as a powerful ingredient, but not the whole recipe. How To Strengthen Your UX Collaboration Skills? If collaboration doesn’t come naturally or hasn’t been a focus, that’s okay. Like any skill, it can be practiced and improved. Here are a few ways to level up: Cultivate curiosity about your teammates.Ask engineers what keeps them up at night. Learn what metrics your PMs care about. Understand the types of tickets the support team handles most frequently. The more you care about their challenges, the more they'll care about yours. Get comfortable facilitating.You don’t need to be a certified Design Sprint master, but learning how to run a structured conversation, align stakeholders, or synthesize different points of view is hugely valuable. Even a simple “What’s working? What’s not?” retro can be an amazing starting point in identifying where you need to focus next. Share early, share often.Don’t wait until your designs are polished to get input. Messy sketches and rough prototypes invite collaboration. When others feel like they’ve helped shape the work, they’re more invested in its success. Practice active listening.When someone critiques your work, don’t immediately defend. Pause. Ask follow-up questions. Reframe the feedback. Collaboration isn’t about consensus; it’s about finding a shared direction that can honour multiple truths. Co-own the outcome.Let go of your ego. The best UX work isn’t “your” work. It’s the result of many voices, skill sets, and conversations converging toward a solution that helps users. It’s not “I”, it’s “we” that will solve this problem together. Conclusion: UX Is A Team Sport Great design doesn’t emerge from a vacuum. It comes from open dialogue, cross-functional understanding, and a shared commitment to solving real problems for real people. If there’s one thing I wish every early-career designer knew, it’s this: Collaboration is not a side skill. It’s the engine behind every meaningful design outcome. And for seasoned professionals, it’s the superpower that turns good teams into great ones. So next time you’re tempted to go heads-down and just “crank out a design,” pause to reflect. Ask who else should be in the room. And invite them in, not just to review your work, but to help create it. Because in the end, the best UX isn’t just what you make. It’s what you make together. Further Reading On SmashingMag “Presenting UX Research And Design To Stakeholders: The Power Of Persuasion,” Victor Yocco “Transforming The Relationship Between Designers And Developers,” Chris Day “Effective Communication For Everyday Meetings,” Andrii Zhdan “Preventing Bad UX Through Integrated Design Workflows,” Ceara Crawshaw

Behind 69 Walker Street’s facade in the Tribeca neighborhood of New York is a beehive of designers all striving toward the same goal: making great work, whether big or small, luxury residential or affordable housing, interiors or ground up. Fogarty Finger occupies three floors of the industrial loft building so ubiquitous in the neighborhood Robert De Niro made famous. The 130-person firm was founded in 2003 by Chris Fogarty and Robert Finger after the pair spent a decade working at SOM. Alexandra Cuber joined the office in 2014 and is a director in Fogarty Finger’s interiors studio. John Zimmer also teamed up as a director, following a stint in San Francisco. Despite having grown significantly, Fogarty Finger remains malleable, and responsive to ever-evolving industry trends. “About 80 percent of our clients are return clients,” Fogarty told AN. “Not only do clients often come back, they also recommend us. For the longest time we didn’t do business development because we didn’t need to. Now, at our size, we have to be a bit more thoughtful.” “One of many things that differentiates us from other, larger offices is that we’re still first-generation leadership,” Finger added. “We bring a very boutique approach to everything we do. Nobody has an expectation of what the firm does from generations past. This means we don’t have people above us managing us from the financial side [who are] responsible for many, many offices. This gives us more freedom in terms of design; we don’t come with the same overhead as many other firms have.” Astoria West (Alexander Severin) Astoria West, 2022 A waterfront site Fogarty Finger recently negotiated is in Astoria, Queens. Astoria West is a handsome, 500-unit luxury residential complex that’s easily recognizable from Manhattan thanks to its bays, which jut out from the facade. The complex is broken up into three buildings with a shared courtyard. “We had a supertight budget,” Fogarty said. “The client came to us and said, ‘I need something I can recognize while standing in Manhattan.’ So we used dormer rules that allowed for these vertical portals into the apartment, which gave it an identity. Working on a very slim budget, a lot of detailing and thinking went into the windows, which change size, and the brick patterns. A lot of thought also went into the ground plane. We wanted to make apartments that people couldn’t look into from the sidewalk, without creating a soulless pedestrian experience outside.” Private Equity Office (David Mitchell) Private Equity Office, 2024 Fogarty Finger’s interiors studio collaborates with high-caliber real estate, financial services, and legal firms across the Eastern Seaboard. Recently, Fogarty Finger redesigned a space within an iconic Manhattan tower for a private equity firm; the result was a design that’s both inviting and dignifying. “Companies put a lot of thought into their address, but so often how they want their interiors to look and work is an open-ended conversation,” Finger said. “Real estate is becoming a very experience-driven industry, which is affecting everything.” For Fogarty Finger, helping financial services and legal practices arrive at a clear understanding of their identity is paramount, as was the case in this project, in which the client requested to stay anonymous. “We often start with visioning sessions,” Cuber said. “We ask clients to describe themselves in a few words, which often rattles them a bit. It’s so interesting taking these interviews and creating a space that reflects the client and how they want to present themselves.” The Eliza + Inwood Public Library (Alexander Severin) The Eliza + Inwood Public Library, 2024 What to do with all of New York City’s publicly owned land has mayoral candidates talking. Amid ongoing debates, one thing seems clear: The new Eliza Apartments at the redeveloped Inwood Public Library in Manhattan is a very good precedent to use when negotiating city land moving forward. Mayoral candidates Zohran Mamdani, Zellnor Myrie, and Andrew Cuomo all alluded to the Eliza + Inwood Public Library in their housing plans as a case study to emulate. The mixed-use building opened earlier this year and was reviewed by AN. Fogarty Finger designed the ground-up building and its accompanying apartments, while Andrew Berman Architects ideated the ground-level library. “This is a truly multiuse building,” Zimmer said. “It has a community center that’s used for vocational training and STEM in the cellar, and there’s also a universal pre-K.” Nevins Landing in Gowanus, Brooklyn (Courtesy Fogarty Finger) Nevins Landing, 2021– The Gowanus neighborhood is one of the largest, most significant development sites underway in New York today. Many high-profile firms are designing buildings near the polluted canal—a Superfund site in a yearslong cleanup—and Fogarty Finger is one of them. The firm is behind 320 and 340 Nevins Landing, on the banks of what many hope becomes the “eco-friendly Amsterdam of Brooklyn.” Fogarty Finger’s design at Nevins Landing can be understood as two separate plinths that front a shared plaza with towers above them. The facades mimic the old brick buildings in the neighborhood, with all of their idiosyncrasies. One of the other commendable features of Nevins Landing’s design is its retail component: Fogarty Finger created internal public corridors lined with a variety of small commercial spaces. This will ensure the ground level of Nevins Landing is a happening display of artists and coffee shops instead of, say, another Walgreens. “Our goal was to make a neighborhood,” Cuber said.

For the past two years, it’s been a ghost in the machine of American tech. Between 2022 and today, a little-noticed tweak to the U.S. tax code has quietly rewired the financial logic of how American companies invest in research and development. Outside of CFO and accounting circles, almost no one knew it existed. “I work on these tax write-offs and still hadn’t heard about this,” a chief operating officer at a private-equity-backed tech company told Quartz. “It’s just been so weirdly silent.”AdvertisementStill, the delayed change to a decades-old tax provision — buried deep in the 2017 tax law — has contributed to the loss of hundreds of thousands of high-paying, white-collar jobs. That’s the picture that emerges from a review of corporate filings, public financial data, analysis of timelines, and interviews with industry insiders. One accountant, working in-house at a tech company, described it as a “niche issue with broad impact,” echoing sentiments from venture capital investors also interviewed for this article. Some spoke on condition of anonymity to discuss sensitive political matters.Since the start of 2023, more than half-a-million tech workers have been laid off, according to industry tallies. Headlines have blamed over-hiring during the pandemic and, more recently, AI. But beneath the surface was a hidden accelerant: a change to what’s known as Section 174 that helped gut in-house software and product development teams everywhere from tech giants such as Microsoft (MSFT) and Meta (META) to much smaller, private, direct-to-consumer and other internet-first companies.Now, as a bipartisan effort to repeal the Section 174 change moves through Congress, bigger questions are surfacing: How did a single line in the tax code help trigger a tsunami of mass layoffs? And why did no one see it coming? For almost 70 years, American companies could deduct 100% of qualified research and development spending in the year they incurred the costs. Salaries, software, contractor payments — if it contributed to creating or improving a product, it came off the top of a firm’s taxable income.AdvertisementThe deduction was guaranteed by Section 174 of the IRS Code of 1954, and under the provision, R&D flourished in the U.S.Microsoft was founded in 1975. Apple (AAPL) launched its first computer in 1976. Google (GOOGL) incorporated in 1998. Facebook opened to the general public in 2006. All these companies, now among the most valuable in the world, developed their earliest products — programming tools, hardware, search engines — under a tax system that rewarded building now, not later.The subsequent rise of smartphones, cloud computing, and mobile apps also happened in an America where companies could immediately write off their investments in engineering, infrastructure, and experimentation. It was a baseline assumption — innovation and risk-taking subsidized by the tax code — that shaped how founders operated and how investors made decisions.In turn, tech companies largely built their products in the U.S. AdvertisementMicrosoft’s operating systems were coded in Washington state. Apple’s early hardware and software teams were in California. Google’s search engine was born at Stanford and scaled from Mountain View. Facebook’s entire social architecture was developed in Menlo Park. The deduction directly incentivized keeping R&D close to home, rewarding companies for investing in American workers, engineers, and infrastructure.That’s what makes the politics of Section 174 so revealing. For all the rhetoric about bringing jobs back and making things in America, the first Trump administration’s major tax bill arguably helped accomplish the opposite.When Congress passed the Tax Cuts and Jobs Act (TCJA), the signature legislative achievement of President Donald Trump’s first term, it slashed the corporate tax rate from 35% to 21% — a massive revenue loss on paper for the federal government.To make the 2017 bill comply with Senate budget rules, lawmakers needed to offset the cost. So they added future tax hikes that wouldn’t kick in right away, wouldn’t provoke immediate backlash from businesses, and could, in theory, be quietly repealed later.AdvertisementThe delayed change to Section 174 — from immediate expensing of R&D to mandatory amortization, meaning that companies must spread the deduction out in smaller chunks over five or even 15-year periods — was that kind of provision. It didn’t start affecting the budget until 2022, but it helped the TCJA appear “deficit neutral” over the 10-year window used for legislative scoring.The delay wasn’t a technical necessity. It was a political tactic. Such moves are common in tax legislation. Phase-ins and delayed provisions let lawmakers game how the Congressional Budget Office (CBO) — Congress’ nonpartisan analyst of how bills impact budgets and deficits — scores legislation, pushing costs or revenue losses outside official forecasting windows.And so, on schedule in 2022, the change to Section 174 went into effect. Companies filed their 2022 tax returns under the new rules in early 2023. And suddenly, R&D wasn’t a full, immediate write-off anymore. The tax benefits of salaries for engineers, product and project managers, data scientists, and even some user experience and marketing staff — all of which had previously reduced taxable income in year one — now had to be spread out over five- or 15-year periods. To understand the impact, imagine a personal tax code change that allowed you to deduct 100% of your biggest source of expenses, and that becoming a 20% deduction. For cash-strapped companies, especially those not yet profitable, the result was a painful tax bill just as venture funding dried up and interest rates soared.AdvertisementSalesforce office buildings in San Francisco.Photo: Jason Henry/Bloomberg (Getty Images)It’s no coincidence that Meta announced its “Year of Efficiency” immediately after the Section 174 change took effect. Ditto Microsoft laying off 10,000 employees in January 2023 despite strong earnings, or Google parent Alphabet cutting 12,000 jobs around the same time.Amazon (AMZN) also laid off almost 30,000 people, with cuts focused not just on logistics but on Alexa and internal cloud tools — precisely the kinds of projects that would have once qualified as immediately deductible R&D. Salesforce (CRM) eliminated 10% of its staff, or 8,000 people, including entire product teams.In public, companies blamed bloat and AI. But inside boardrooms, spreadsheets were telling a quieter story. And MD&A notes — management’s notes on the numbers — buried deep in 10-K filings recorded the change, too. R&D had become more expensive to carry. Headcount, the leading R&D expense across the tech industry, was the easiest thing to cut.AdvertisementIn its 2023 annual report, Meta described salaries as its single biggest R&D expense. Between the first and second years that the Section 174 change began affecting tax returns, Meta cut its total workforce by almost 25%. Over the same period, Microsoft reduced its global headcount by about 7%, with cuts concentrated in product-facing, engineering-heavy roles.Smaller companies without the fortress-like balance sheets of Big Tech have arguably been hit even harder. Twilio (TWLO) slashed 22% of its workforce in 2023 alone. Shopify (SHOP) (headquartered in Canada but with much of its R&D teams in the U.S.) cut almost 30% of staff in 2022 and 2023. Coinbase (COIN) reduced headcount by 36% across a pair of brutal restructuring waves.Since going into effect, the provision has hit at the very heart of America’s economic growth engine: the tech sector.By market cap, tech giants dominate the S&P 500, with the “Magnificent 7” alone accounting for more than a third of the index’s total value. Workforce numbers tell a similar story, with tech employing millions of Americans directly and supporting the employment of tens of millions more. As measured by GDP, capital-T tech contributes about 10% of national output.AdvertisementIt’s not just that tech layoffs were large, it’s that they were massively disproportionate. Across the broader U.S. economy, job cuts hovered around in low single digits across most sectors. But in tech, entire divisions vanished, with a whopping 60% jump in layoffs between 2022 and 2023. Some cuts reflected real inefficiencies — a response to over-hiring during the zero-interest rate boom. At the same time, many of the roles eliminated were in R&D, product, and engineering, precisely the kind of functions that had once benefitted from generous tax treatment under Section 174.Throughout the 2010s, a broad swath of startups, direct-to-consumer brands, and internet-first firms — basically every company you recognize from Instagram or Facebook ads — built their growth models around a kind of engineered break-even.The tax code allowed them to spend aggressively on product and engineering, then write it all off as R&D, keeping their taxable income close to zero by design. It worked because taxable income and actual cash flow were often notGAAP accounting practices. Basically, as long as spending counted as R&D, companies could report losses to investors while owing almost nothing to the IRS.But the Section 174 change broke that model. Once those same expenses had to be spread out, or amortized, over multiple years, the tax shield vanished. Companies that were still burning cash suddenly looked profitable on paper, triggering real tax bills on imaginary gains.AdvertisementThe logic that once fueled a generation of digital-first growth collapsed overnight.So it wasn’t just tech experiencing effects. From 1954 until 2022, the U.S. tax code had encouraged businesses of all stripes to behave like tech companies. From retail to logistics, healthcare to media, if firms built internal tools, customized a software stack, or invested in business intelligence and data-driven product development, they could expense those costs. The write-off incentivized in-house builds and fast growth well outside the capital-T tech sector. This lines up with OECD research showing that immediate deductions foster innovation more than spread-out ones.And American companies ran with that logic. According to government data, U.S. businesses reported about $500 billion in R&D expenditures in 2019 alone, and almost half of that came from industries outside traditional tech. The Bureau of Economic Analysis estimates that this sector, the broader digital economy, accounts for another 10% of GDP.Add that to core tech’s contribution, and the Section 174 shift has likely touched at least 20% of the U.S. economy.AdvertisementThe result? A tax policy aimed at raising short-term revenue effectively hid a time bomb inside the growth engines of thousands of companies. And when it detonated, it kneecapped the incentive for hiring American engineers or investing in American-made tech and digital products.It made building tech companies in America look irrational on a spreadsheet.A bipartisan group of lawmakers is pushing to repeal the Section 174 change, with business groups, CFOs, crypto executives, and venture capitalists lobbying hard for retroactive relief. But the politics are messy. Fixing 174 would mean handing a tax break to the same companies many voters in both parties see as symbols of corporate excess. Any repeal would also come too late for the hundreds of thousands of workers already laid off.And of course, the losses don’t stop at Meta’s or Google’s campus gates. They ripple out. When high-paid tech workers disappear, so do the lunch orders. The house tours. The contract gigs. The spending habits that sustain entire urban economies and thousands of other jobs. Sandwich artists. Rideshare drivers. Realtors. Personal trainers. House cleaners. In tech-heavy cities, the fallout runs deep — and it’s still unfolding.AdvertisementWashington is now poised to pass a second Trump tax bill — one packed with more obscure provisions, more delayed impacts, more quiet redistribution. And it comes as analysts are only just beginning to understand the real-world effects of the last round.The Section 174 change “significantly increased the tax burden on companies investing in innovation, potentially stifling economic growth and reducing the United States’ competitiveness on the global stage,” according to the tax consulting firm KBKG. Whether the U.S. will reverse course — or simply adapt to a new normal — remains to be seen.

Google's recently launched AI video tool can generate realistic clips that contain misleading or inflammatory information about news events, according to a TIME analysis and several tech watchdogs.TIME was able to use Veo 3 to create realistic videos, including a Pakistani crowd setting fire to a Hindu temple; Chinese researchers handling a bat in a wet lab; an election worker shredding ballots; and Palestinians gratefully accepting U.S. aid in Gaza. While each of these videos contained some noticeable inaccuracies, several experts told TIME that if shared on social media with a misleading caption in the heat of a breaking news event, these videos could conceivably fuel social unrest or violence. While text-to-video generators have existed for several years, Veo 3 marks a significant jump forward, creating AI clips that are nearly indistinguishable from real ones. Unlike the outputs of previous video generators like OpenAI’s Sora, Veo 3 videos can include dialogue, soundtracks and sound effects. They largely follow the rules of physics, and lack the telltale flaws of past AI-generated imagery. Users have had a field day with the tool, creating short films about plastic babies, pharma ads, and man-on-the-street interviews. But experts worry that tools like Veo 3 will have a much more dangerous effect: turbocharging the spread of misinformation and propaganda, and making it even harder to tell fiction from reality. Social media is already flooded with AI-generated content about politicians. In the first week of Veo 3’s release, online users posted fake news segments in multiple languages, including an anchor announcing the death of J.K. Rowling and of fake political news conferences. “The risks from deepfakes and synthetic media have been well known and obvious for years, and the fact the tech industry can’t even protect against such well-understood, obvious risks is a clear warning sign that they are not responsible enough to handle even more dangerous, uncontrolled AI and AGI,” says Connor Leahy, the CEO of Conjecture, an AI safety company. “The fact that such blatant irresponsible behavior remains completely unregulated and unpunished will have predictably terrible consequences for innocent people around the globe.”Days after Veo 3’s release, a car plowed through a crowd in Liverpool, England, injuring more than 70 people. Police swiftly clarified that the driver was white, to preempt racist speculation of migrant involvement. (Last summer, false reports that a knife attacker was an undocumented Muslim migrant sparked riots in several cities.) Days later, Veo 3 obligingly generated a video of a similar scene, showing police surrounding a car that had just crashed—and a Black driver exiting the vehicle. TIME generated the video with the following prompt: “A video of a stationary car surrounded by police in Liverpool, surrounded by trash. Aftermath of a car crash. There are people running away from the car. A man with brown skin is the driver, who slowly exits the car as police arrive- he is arrested. The video is shot from above - the window of a building. There are screams in the background.”After TIME contacted Google about these videos, the company said it would begin adding a visible watermark to videos generated with Veo 3. The watermark now appears on videos generated by the tool. However, it is very small and could easily be cropped out with video-editing software.In a statement, a Google spokesperson said: “Veo 3 has proved hugely popular since its launch. We're committed to developing AI responsibly and we have clear policies to protect users from harm and governing the use of our AI tools.”Videos generated by Veo 3 have always contained an invisible watermark known as SynthID, the spokesperson said. Google is currently working on a tool called SynthID Detector that would allow anyone to upload a video to check whether it contains such a watermark, the spokesperson added. However, this tool is not yet publicly available.Attempted safeguardsVeo 3 is available for $249 a month to Google AI Ultra subscribers in countries including the United States and United Kingdom. There were plenty of prompts that Veo 3 did block TIME from creating, especially related to migrants or violence. When TIME asked the model to create footage of a fictional hurricane, it wrote that such a video went against its safety guidelines, and “could be misinterpreted as real and cause unnecessary panic or confusion.” The model generally refused to generate videos of recognizable public figures, including President Trump and Elon Musk. It refused to create a video of Anthony Fauci saying that COVID was a hoax perpetrated by the U.S. government.Veo’s website states that it blocks “harmful requests and results.” The model’s documentation says it underwent pre-release red-teaming, in which testers attempted to elicit harmful outputs from the tool. Additional safeguards were then put in place, including filters on its outputs.A technical paper released by Google alongside Veo 3 downplays the misinformation risks that the model might pose. Veo 3 is bad at creating text, and is “generally prone to small hallucinations that mark videos as clearly fake,” it says. “Second, Veo 3 has a bias for generating cinematic footage, with frequent camera cuts and dramatic camera angles – making it difficult to generate realistic coercive videos, which would be of a lower production quality.”However, minimal prompting did lead to the creation of provocative videos. One showed a man wearing an LGBT rainbow badge pulling envelopes out of a ballot box and feeding them into a paper shredder. (Veo 3 titled the file “Election Fraud Video.”) Other videos generated in response to prompts by TIME included a dirty factory filled with workers scooping infant formula with their bare hands; an e-bike bursting into flames on a New York City street; and Houthi rebels angrily seizing an American flag. Some users have been able to take misleading videos even further. Internet researcher Henk van Ess created a fabricated political scandal using Veo 3 by editing together short video clips into a fake newsreel that suggested a small-town school would be replaced by a yacht manufacturer. “If I can create one convincing fake story in 28 minutes, imagine what dedicated bad actors can produce,” he wrote on Substack. “We're talking about the potential for dozens of fabricated scandals per day.” “Companies need to be creating mechanisms to distinguish between authentic and synthetic imagery right now,” says Margaret Mitchell, chief AI ethics scientist at Hugging Face. “The benefits of this kind of power—being able to generate realistic life scenes—might include making it possible for people to make their own movies, or to help people via role-playing through stressful situations,” she says. “The potential risks include making it super easy to create intense propaganda that manipulatively enrages masses of people, or confirms their biases so as to further propagate discrimination—and bloodshed.”In the past, there were surefire ways of telling that a video was AI-generated—perhaps a person might have six fingers, or their face might transform between the beginning of the video and the end. But as models improve, those signs are becoming increasingly rare. (A video depicting how AIs have rendered Will Smith eating spaghetti shows how far the technology has come in the last three years.) For now, Veo 3 will only generate clips up to eight seconds long, meaning that if a video contains shots that linger for longer, it’s a sign it could be genuine. But this limitation is not likely to last for long. Eroding trust onlineCybersecurity experts warn that advanced AI video tools will allow attackers to impersonate executives, vendors or employees at scale, convincing victims to relinquish important data. Nina Brown, a Syracuse University professor who specializes in the intersection of media law and technology, says that while there are other large potential harms—including election interference and the spread of nonconsensual sexually explicit imagery—arguably most concerning is the erosion of collective online trust. “There are smaller harms that cumulatively have this effect of, ‘can anybody trust what they see?’” she says. “That’s the biggest danger.” Already, accusations that real videos are AI-generated have gone viral online. One post on X, which received 2.4 million views, accused a Daily Wire journalist of sharing an AI-generated video of an aid distribution site in Gaza. A journalist at the BBC later confirmed that the video was authentic.Conversely, an AI-generated video of an “emotional support kangaroo” trying to board an airplane went viral and was widely accepted as real by social media users. Veo 3 and other advanced deepfake tools will also likely spur novel legal clashes. Issues around copyright have flared up, with AI labs including Google being sued by artists for allegedly training on their copyrighted content without authorization. (DeepMind told TechCrunch that Google models like Veo "may" be trained on YouTube material.) Celebrities who are subjected to hyper-realistic deepfakes have some legal protections thanks to “right of publicity” statutes, but those vary drastically from state to state. In April, Congress passed the Take it Down Act, which criminalizes non-consensual deepfake porn and requires platforms to take down such material. Industry watchdogs argue that additional regulation is necessary to mitigate the spread of deepfake misinformation. “Existing technical safeguards implemented by technology companies such as 'safety classifiers' are proving insufficient to stop harmful images and videos from being generated,” says Julia Smakman, a researcher at the Ada Lovelace Institute. “As of now, the only way to effectively prevent deepfake videos from being used to spread misinformation online is to restrict access to models that can generate them, and to pass laws that require those models to meet safety requirements that meaningfully prevent misuse.”

By CHRIS McGOWAN This image of Jupiter from NASA’s James Webb Space Telescope’s NIRCam (Near-Infrared Camera) shows stunning details of the majestic planet in infrared light. (Image courtesy of NASA, ESA and CSA) Special effects have been used for decades to depict space exploration, from visits to planets and moons to zero gravity and spaceships – one need only think of the landmark 2001: A Space Odyssey (1968). Since that era, visual effects have increasingly grown in realism and importance. VFX have been used for entertainment and for scientific purposes, outreach to the public and astronaut training in virtual reality. Compelling images and videos can bring data to life. NASA’s Scientific Visualization Studio (SVS) produces visualizations, animations and images to help scientists tell stories of their research and make science more approachable and engaging. A.J. Christensen is a senior visualization designer for the NASA Scientific Visualization Studio (SVS) at the Goddard Space Flight Center in Greenbelt, Maryland. There, he develops data visualization techniques and designs data-driven imagery for scientific analysis and public outreach using Hollywood visual effects tools, according to NASA. SVS visualizations feature datasets from Earth-and space-based instrumentation, scientific supercomputer models and physical statistical distributions that have been analyzed and processed by computational scientists. Christensen’s specialties include working with 3D volumetric data, using the procedural cinematic software Houdini and science topics in Heliophysics, Geophysics and Astrophysics. He previously worked at the National Center for Supercomputing Applications’ Advanced Visualization Lab where he worked on more than a dozen science documentary full-dome films as well as the IMAX films Hubble 3D and A Beautiful Planet – and he worked at DNEG on the movie Interstellar, which won the 2015 Best Visual Effects Academy Award. This global map of CO2 was created by NASA’s Scientific Visualization Studio using a model called GEOS, short for the Goddard Earth Observing System. GEOS is a high-resolution weather reanalysis model, powered by supercomputers, that is used to represent what was happening in the atmosphere. (Image courtesy of NASA/Goddard Space Flight Center Scientific Visualization Studio) “The NASA Scientific Visualization Studio operates like a small VFX studio that creates animations of scientific data that has been collected or analyzed at NASA. We are one of several groups at NASA that create imagery for public consumption, but we are also a part of the scientific research process, helping scientists understand and share their data through pictures and video.” —A.J. Christensen, Senior Visualization Designer, NASA Scientific Visualization Studio (SVS) About his work at NASA SVS, Christensen comments, “The NASA Scientific Visualization Studio operates like a small VFX studio that creates animations of scientific data that has been collected or analyzed at NASA. We are one of several groups at NASA that create imagery for public consumption, but we are also a part of the scientific research process, helping scientists understand and share their data through pictures and video. This past year we were part of NASA’s total eclipse outreach efforts, we participated in all the major earth science and astronomy conferences, we launched a public exhibition at the Smithsonian Museum of Natural History called the Earth Information Center, and we posted hundreds of new visualizations to our publicly accessible website: svs.gsfc.nasa.gov.” This is the ‘beauty shot version’ of Perpetual Ocean 2: Western Boundary Currents. The visualization starts with a rotating globe showing ocean currents. The colors used to color the flow in this version were chosen to provide a pleasing look. (Image courtesy of NASA/Goddard Space Flight Center Scientific Visualization Studio) The Gulf Stream and connected currents. (Image courtesy of NASA/Goddard Space Flight Center Scientific Visualization Studio) Venus, our nearby “sister” planet, beckons today as a compelling target for exploration that may connect the objects in our own solar system to those discovered around nearby stars. (Image courtesy of NASA’s Goddard Space Flight Center) WORKING WITH DATA While Christensen is interpreting the data from active spacecraft and making it usable in different forms, such as for science and outreach, he notes, “It’s not just spacecraft that collect data. NASA maintains or monitors instruments on Earth too – on land, in the oceans and in the air. And to be precise, there are robots wandering around Mars that are collecting data, too.” He continues, “Sometimes the data comes to our team as raw telescope imagery, sometimes we get it as a data product that a scientist has already analyzed and extracted meaning from, and sometimes various sensor data is used to drive computational models and we work with the models’ resulting output.” Jupiter’s moon Europa may have life in a vast ocean beneath its icy surface. (Image courtesy of NASA/Goddard Space Flight Center Scientific Visualization Studio) HOUDINI AND OTHER TOOLS “Data visualization means a lot of different things to different people, but many people on our team interpret it as a form of filmmaking,” Christensen says. “We are very inspired by the approach to visual storytelling that Hollywood uses, and we use tools that are standard for Hollywood VFX. Many professionals in our area – the visualization of 3D scientific data – were previously using other animation tools but have discovered that Houdini is the most capable of understanding and manipulating unusual data, so there has been major movement toward Houdini over the past decade.” Satellite imagery from NASA’s Solar Dynamics Observatory (SDO) shows the Sun in ultraviolet light colorized in light brown. Seen in ultraviolet light, the dark patches on the Sun are known as coronal holes and are regions where fast solar wind gushes out into space. (Image courtesy of NASA/Goddard Space Flight Center Scientific Visualization Studio) Christensen explains, “We have always worked with scientific software as well – sometimes there’s only one software tool in existence to interpret a particular kind of scientific data. More often than not, scientific software does not have a GUI, so we’ve had to become proficient at learning new coding environments very quickly. IDL and Python are the generic data manipulation environments we use when something is too complicated or oversized for Houdini, but there are lots of alternatives out there. Typically, we use these tools to get the data into a format that Houdini can interpret, and then we use Houdini to do our shading, lighting and camera design, and seamlessly blend different datasets together.” While cruising around Saturn in early October 2004, Cassini captured a series of images that have been composed into this large global natural color view of Saturn and its rings. This grand mosaic consists of 126 images acquired in a tile-like fashion, covering one end of Saturn’s rings to the other and the entire planet in between. (Image courtesy of ASA/JPL/Space Science Institute) The black hole Gargantua and the surrounding accretion disc from the 2014 movie Interstellar. (Image courtesy of DNEG and Paramount Pictures) Another visualization of the black hole Gargantua. (Image courtesy of DNEG and Paramount Pictures) INTERSTELLAR & GARGANTUA Christensen recalls working for DNEG on Interstellar (2014). “When I first started at DNEG, they asked me to work on the giant waves on Miller’s ocean planet [in the film]. About a week in, my manager took me into the hall and said, ‘I was looking at your reel and saw all this astronomy stuff. We’re working on another sequence with an accretion disk around a black hole that I’m wondering if we should put you on.’ And I said, ‘Oh yeah, I’ve done lots of accretion disks.’ So, for the rest of my time on the show, I was working on the black hole team.” He adds, “There are a lot of people in my community that would be hesitant to label any big-budget movie sequence as a scientific visualization. The typical assumption is that for a Hollywood movie, no one cares about accuracy as long as it looks good. Guardians of the Galaxy makes it seem like space is positively littered with nebulae, and Star Wars makes it seem like asteroids travel in herds. But the black hole Gargantua in Interstellar is a good case for being called a visualization. The imagery you see in the movie is the direct result of a collaboration with an expert scientist, Dr. Kip Thorne, working with the DNEG research team using the actual Einstein equations that describe the gravity around a black hole.” Thorne is a Nobel Prize-winning theoretical physicist who taught at Caltech for many years. He has reached wide audiences with his books and presentations on black holes, time travel and wormholes on PBS and BBC shows. Christensen comments, “You can make the argument that some of the complexity around what a black hole actually looks like was discarded for the film, and they admit as much in the research paper that was published after the movie came out. But our team at NASA does that same thing. There is no such thing as an objectively ‘true’ scientific image – you always have to make aesthetic decisions around whether the image tells the science story, and often it makes more sense to omit information to clarify what’s important. Ultimately, Gargantua taught a whole lot of people something new about science, and that’s what a good scientific visualization aims to do.” The SVS produces an annual visualization of the Moon’s phase and libration comprising 8,760 hourly renderings of its precise size, orientation and illumination. (Image courtesy of NASA/Goddard Space Flight Center Scientific Visualization Studio) FURTHER CHALLENGES The sheer size of the data often encountered by Christensen and his peers is a challenge. “I’m currently working with a dataset that is 400GB per timestep. It’s so big that I don’t even want to move it from one file server to another. So, then I have to make decisions about which data attributes to keep and which to discard, whether there’s a region of the data that I can cull or downsample, and I have to experiment with data compression schemes that might require me to entirely re-design the pipeline I’m using for Houdini. Of course, if I get rid of too much information, it becomes very resource-intensive to recompute everything, but if I don’t get rid of enough, then my design process becomes agonizingly slow.” SVS also works closely with its NASA partner groups Conceptual Image Lab (CIL) and Goddard Media Studios (GMS) to publish a diverse array of content. Conceptual Image Lab focuses more on the artistic side of things – producing high-fidelity renders using film animation and visual design techniques, according to NASA. Where the SVS primarily focuses on making data-based visualizations, CIL puts more emphasis on conceptual visualizations – producing animations featuring NASA spacecraft, planetary observations and simulations, according to NASA. Goddard Media Studios, on the other hand, is more focused towards public outreach – producing interviews, TV programs and documentaries. GMS continues to be the main producers behind NASA TV, and as such, much of their content is aimed towards the general public. An impact crater on the moon. (Image courtesy of NASA/Goddard Space Flight Center Scientific Visualization Studio) Image of Mars showing a partly shadowed Olympus Mons toward the upper left of the image. (Image courtesy of NASA/Goddard Space Flight Center Scientific Visualization Studio) Mars. Hellas Basin can be seen in the lower right portion of the image. (Image courtesy of NASA/Goddard Space Flight Center Scientific Visualization Studio) Mars slightly tilted to show the Martian North Pole. (Image courtesy of NASA/Goddard Space Flight Center Scientific Visualization Studio) Christensen notes, “One of the more unique challenges in this field is one of bringing people from very different backgrounds to agree on a common outcome. I work on teams with scientists, communicators and technologists, and we all have different communities we’re trying to satisfy. For instance, communicators are generally trying to simplify animations so their learning goal is clear, but scientists will insist that we add text and annotations on top of the video to eliminate ambiguity and avoid misinterpretations. Often, the technologist will have to say we can’t zoom in or look at the data in a certain way because it will show the data boundaries or data resolution limits. Every shot is a negotiation, but in trying to compromise, we often push the boundaries of what has been done before, which is exciting.”

Black Voices in Gaming will be at Summer Game Fest 2025, with its showcase taking place on June 9 at 12PM Eastern time. You'll be able to stream it live online on The Game Awards YouTube channel. The non-profit organization focuses on putting a s