Gieß den Kiez app shows trees in critical need of watering and can be adopted by other heat-stressed cities around the world

Tech company executives are confident that AI will completely transform the economy and point to the changes they see in-house to prove that this change is coming fast. At Meta, Google, Microsoft, and others, leadership says that AI generates a growing share of the overall code, which makes it cheaper and faster to produce. The implication is that if this AI is good enough that tech companies are using it internally to improve efficiency and reduce headcount, it’s only a matter of time until every other industry is similarly transformed. Developers who are told to use AI whether they like it or not, however, tell a different story. On Reddit, Hacker News and other places where people in software development talk to each other, more and more people are becoming disillusioned with the promise of code generated by large language models. Developers talk not just about how the AI output is often flawed, but that using AI to get the job done is often a more time consuming, harder, and more frustrating experience because they have to go through the output and fix its mistakes. More concerning, developers who use AI at work report that they feel like they are de-skilling themselves and losing their ability to do their jobs as well as they used to. “We're being told to use [AI] agents for broad changes across our codebase. There's no way to evaluate whether that much code is well-written or secure—especially when hundreds of other programmers in the company are doing the same,” a UX designer at a midsized tech company told me. 404 granted all the developers we talked to for this story anonymity because they signed non-disclosure agreements or because they fear retribution from their employers. “We're building a rat's nest of tech debt that will be impossible to untangle when these models become prohibitively expensive (any minute now...).” > The actual quality of output doesn't matter as much as our willingness to participate. Tech company executives love to brag about how much of the code at their company is AI-generated. In April, Google said that three quarters of new code at the company was generated by AI. Last year, Microsoft CEO Satya Nadella said up to 30 percent of the company’s code was generated by AI. Microsoft’s CTO Kevin Scott said he expects 95 percent of all code at the company to be AI-generated by 2030. Meta’s Mark Zuckerberg said last year he expects AI to write most of the code improving AI within 12-18 months. Anthropic says 90 percent of the code written by most if its team is AI generated. Tech companies have also been bragging about their “tokenmaxxing,” or how much money they’re spending on AI tools instead of human employees. 💡 ****Are you a developer at Google, Microsoft, or another tech being pressured to use AI? I would love to hear from you. Using a non-work device, you can message me securely on Signal at ‪(609) 678-3204‬. Otherwise, send me an email at emanuel@404media.co.**** Predictably, the huge spike in productivity that these companies claim their own AI products have enabled hasn’t resulted in more or better products, shorter work weeks, or better consumer experiences. Mostly, AI implementation in tech companies has been used to justify multiple massive rounds of layoffs. To name just a few examples where tech companies said they reduced headcount because of AI use, more recently, Meta said it would cut 10 percent of its workforce (around 8,000 people), Microsoft said it would offer voluntary retirement to 7 percent of its American workforce (around 125,000 people). Snapchat said it would lay off 16 percent of its full-time staffers (about 1,000 people). The developers I talked to contradicted the narrative about AI’s utility in coding in many ways, but the most glaring issue with the narrative AI company executives are pitching is that the adoption of AI tools they see internally isn’t voluntary or organic. Developers say they are either explicitly ordered to use AI tools or heavily pressured to use them. “AI in some shape or form is all but explicitly mandated,” a software engineer at a FAANG company that brags publicly about its internal AI adoption told me. “Its usage is part of our performance review criteria and most (maybe all?) of us have been reorganized into AI focused ‘pods.’ We're absolutely flooded with AI tooling and it feels like the answer to every problem is ‘use AI first.’” “We've been told performance evaluations are tied to AI adoption,” the UX designer told me. “This has led to most of my teammates using it performatively, even if most of us implicitly know that the output is flawed. The actual quality of output doesn't matter as much as our willingness to participate.” Another software engineer at a financial technology company told me that he was never forced to use LLMs but that the companies where he worked changed in a way that encouraged their use. His previous employer didn’t demand developers use AI but it was encouraged and developers were given access to Cursor, one of the leading coding agents. “It started as a ‘who wants to try it’ and I volunteered. Later it was slowly, due to costs, that we stopped renewing our JetBrains IDE and forced everyone to move to Cursor (though the editor itself doesn't force you to use AI),” he said. JetBrains IDE is an integrated development environment used by software developers. “Adoption came mostly from inside the engineering team, with a single engineer manager trying to champion it and writing project based rules for Cursor to try to make the output better.” All the developers I talked to were excited to try using LLMs at work at first, or were at least curious about them. Their feelings about the tools, based on their personal experience, are now overwhelmingly negative. “There were almost no productivity gains using IDE-based AI tools. AI-generated code ended up with more bugs because I am working on distributed web apps, highly complex multi-system things, so giving the LLM context is very difficult,” a software developer at a small web design firm told me. “Another developer on a contract working with me at the moment generates massive amounts of code, leaving me with 1000+ lines of pull requests to review and it takes massive amounts of time to do this. This leads to me feeling more tired and burned out than I've ever felt in my entire life. The cognitive overhead of switching between prompting, coding, checking the LLM's output is a massive energy drain. It has not been a productivity booster at all, it feels like a speedrun towards severe mental exhaustion.” The developer in fintech I talked to also said that one major problem with LLMs is that it can generate more code than developers can properly vet or explain. “The sheer breadth of code makes it impossible to be critical enough and then you're either throwing it away or submitting it and feeling scared there might be really low quality stuff that if someone notices will make you embarrassed (and even more embarrassing to say: ‘oh i don't know what that is, the AI did that’),” he said. “Or worse, you ship it without someone noticing and that is really hit or miss.” “I have gotten stuck on bug fixes where, when I run out of Anthropic tokens in Claude Code, I couldn't work anymore. The current system I am working on started to become a monstrosity of complexity where I didn't even know what most of it does anymore, and when I had to fix a bug, it took longer than I would have taken in the past to debug,” the software developer at a small web design firm told me. The developers I talked to found AI useful for some tasks. Several developers said that it was good for experimentation, allowing them to quickly prototype an idea or to implement something in a domain they’re unfamiliar with. One developer said it was a good information interface. Specifically, he said, the AI helped him find where on the server a certain request is handled, summarize logs, or find documentation related to code changes. The problem all the developers I talked to agreed on is that the more they relied on AI to code, the more the skills they’ve honed for years deteriorated. This is by now a well studied phenomenon sometimes referred to as "cognitive debt” or "cognitive atrophy.” The idea is that people who use AI to automate certain parts of their job lose the ability to do those tasks well, therefore de-skilling themselves. “I had some issues where I forgot how to implement a Laravel API and it scared the shit out of me. I went to university for this, I've been a software engineer for many years now and it feels like I am back before I ever wrote a single line of code,” the software developer at a small web design firm told me. “It's making me dumber for sure,” the fintech software developer told me. “It's like when we got cellphones and stopped remembering phone numbers, but it's grown to me mentally outsourcing ‘thinking’ in general. I feel my critical thinking and ability to sit and reason about a problem or a design has degraded because the all-knowing-dalai-llama is just a question away from giving me his take. And supposedly I tell myself ill just use it for inspiration but it ends up being my only thought. It gives you the illusion of productivity and expertise but at the end of the day you are more divorced from the output you submit than before.” “When I was using it for code generation, I found myself having a lot of trouble building and maintaining a mental model of the code I was working with,” the software engineer at the FAANG told me. “Another aspect is that I joined late last year and [the company’s] codebase is massive. As a new hire, part of my job is to learn how to navigate the codebase and use the established conventions, but I think the AI push really hampered my ability to do that.” The developers I talked to agreed that LLMs will stick around and play a role in programming in the future in some fashion, but worried about how the industry will adapt to executives’ current obsession with the technology, especially when it comes to fostering future generations of developers. “Older programmers will be fine if there are any jobs left in a few years, but I worry for people early in their careers,” the UX designer told me. “We are hiring junior programmers who rely on AI to complete the simplest tasks. They don't have the knowledge or experience to know when AI output is error-laden or inefficient.” “I wish I had a crystal ball for this one, but my gut feeling is that this method of building software will be unsustainable either economically or in terms of tech debt,” the software engineer at the FAANG company said. “There's a pretty clear split on my team between people who love AI coding and those who just do it because it's what the company wants, and generally speaking I find that the people who are still [technically focused individual contributors] with their nose in code all the time are less likely to be big AI boosters. I think the tech and its outputs start to really break down the more you question them and those who are doing that day in and day out tend to have a worse opinion of the tech.” “I think there will be a ‘reckoning’ or ‘awakening’ from the industry notion that now everyone can code and that vibe coding is viable for a real production app and software companies are dead,” the developer in fintech said. “I think we will grow to find the patterns and industry best practices that will balance the negatives of LLM development (hallucination, unstructured code) with better techniques to verify the output's correctness at scale, and the hype and techno optimism of AI will get to a saner middle ground.”

Forty years after the reactor explosion, the wildlife around Chernobyl has recovered in strange and unexpected ways.

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🌘 ****Subscribe********to 404 Media to get**** _****The Abstract****_****, our newsletter about the most exciting and mind-boggling science news and studies of the week.**** Scientists have discovered all five nucleobases—the fundamental components of DNA and RNA—in pristine samples from the asteroid Ryugu, according to _a study published on Monday_ in _Nature Astronomy_. The finding strengthens the case that the ingredients for life are abundant in the solar system and may have found their way to Earth from space, according to _a study published on Monday_ in _Nature Astronomy_. Life as we know it runs on DNA and RNA, which are built from five chemical bases: adenine, guanine, cytosine, thymine, and uracil. A team has now identified this “complete set” of nucleobases in rocks snatched from the surface of Ryugu in 2019 by the Japanese spacecraft Hayabusa-2, which successfully returned them to Earth the following year. This discovery corroborates _the results from another mission_, NASA’s OSIRIS-REx, which returned samples of the asteroid Bennu that also contained all five nucleobases. Both asteroids belong to the same “carbonaceous” (C-type) family of primitive carbon-rich rocks, though the samples contain different ratios of the five nucleobases. Taken together, the findings shed light on the origin of life on Earth and raise new questions about the odds that it exists elsewhere. “These findings suggest that nucleobases may be widespread in carbonaceous asteroids and, by extension, in planetary systems,” said Toshiki Koga, a postdoctoral researcher at the Japan Agency for Marine-Earth Science and Technology (JAMSTEC), in an email to 404 Media. “This means that some of the key molecular ingredients for life could be commonly available,” he added. “However, this does not imply that life itself is widespread, but rather that the chemical starting materials for life may be more common than previously thought.” The emergence of life on Earth, also known as abiogenesis, remains one of the biggest mysteries in science. To untangle this enigma, scientists first need to figure out how our planet was initially enriched with the basic stuff of life—including water, amino acids, and the nucleobases that make up our genetic material. __The “Ryugu Story” illustration depicting the detection of all five canonical nucleobases in samples returned from asteroid Ryugu by the Hayabusa2 mission. Image: JAMSTEC__ One popular hypothesis suggests that asteroids bearing these biological building blocks pelted Earth as it formed more than four billion years ago. This idea has been supported by the presence of nucleobases in pieces of carbonaceous asteroids that have fallen down to Earth, such as the Murchison meteorite of Australia or the Orgueil meteorite of France. Meteorites, however, are not pristine as they become eroded by exposure to space and can also be contaminated by terrestrial material after landing on Earth. To get cleaner samples, scientists launched several spacecraft to grab samples directly from the source, beginning with Japan’s Hayabusa mission, which delivered several milligrams of dusty grains from asteroid Itokawa to Earth in 2010. Hayabusa-2 and OSIRIS-REx then obtained even larger samples from their targets, bringing back 5.4 grams from Ryugu and 121.6 grams from Bennu. Previous studies have already identified more than a dozen amino acids associated with life in both samples, as well as evidence that these asteroids were once altered by ice and water. Now, following the discovery of _all five nucleobases_ in the Bennu pebbles, Koga and his colleagues have found the complete set in Ryugu. The findings lend weight to the so-called “RNA world” model of abiogenesis. In this hypothesis, early life on Earth depended solely on RNA as a self-replicating molecule, laying the biological groundwork for later, more complicated systems that involved DNA and protein-based organisms. The extraterrestrial samples from Ryugu and Bennu provide evidence that at least some of the nucleobases that made up these early lifeforms came from outer space. The results were “broadly in line with our expectations, but still very exciting to confirm,” Koga said. “All five nucleobases had already been detected in the Murchison meteorite and in samples from the asteroid Bennu. Since Ryugu is also a carbonaceous asteroid, we expected that these molecules might be present, and it was very satisfying to confirm that the complete set is indeed present in the Ryugu samples.” But while both samples contained the royal flush of nucleobases, they differed in their relative abundances. For example, Bennu is much richer in pyrimidine nucleobases (cytosine, thymine and uracil) than Ryugu, though they both contain roughly similar levels of purine nucleobases (adenine and guanine). These idiosyncrasies point to a variety of formation processes that produced prebiotic materials on these celestial relics. “Our results suggest that nucleobases can form under a range of conditions in early Solar System materials, particularly within primitive asteroid parent bodies that experienced aqueous alteration,” Koga said. “The observed relationship between nucleobase composition and ammonia abundance indicates that local chemical environments, such as the availability of ammonia, may play an important role.” “At the same time, some precursor molecules may have formed earlier in interstellar environments, so nucleobase formation could involve multiple stages,” he continued. “Future studies, including analyses of different types of meteorites and laboratory experiments that simulate these conditions, will help to better constrain these formation pathways.” In other words, understanding how these molecules form in space could help answer the age-old mystery of whether life is a rare cosmic fluke—or a common process in the universe. The research also highlights the remarkable ingenuity behind these sample-return missions, which have delivered tiny time capsules from the birth of our solar system directly into our hands. “It is both exciting and humbling to work with these samples,” Koga said. “They are extremely limited and represent material that has remained largely unchanged since the early Solar System. At the same time, there is a strong sense of responsibility, because each tiny grain may contain important information about how organic molecules formed and evolved before the origin of life.” 🌘 ****Subscribe********to 404 Media to get**** _****The Abstract****_****, our newsletter about the most exciting and mind-boggling science news and studies of the week.****