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Most programming languages were designed for humans who read error messages, interpret warnings, and manually trace through stack output to fix bugs. AI agents do none of those things well. They work better with structured data: predictable tokens, stable codes, and machine-parseable repair hints. That gap is what Vercel Labs is trying to close by releasing Zero, an experimental systems language that is faster, smaller, and easier for agents to use and repair. What is Zero Language Zero is a systems programming language that sits in the same design space as C or Rust. It compiles to native executables, gives you explicit memory control, and targets low-level environments. What separates Zero from existing systems languages is that its compiler output and toolchain were designed from day one to be consumed by AI agents, not just human engineers. The Agent-First Toolchain The core problem Zero addresses is how agents interact with compiler feedback. In a typical development loop involving a coding agent, the agent writes code, the compiler emits an error as unstructured text, and the agent has to parse that text to determine what went wrong and how to fix it. This is fragile — error message formats change, messages are written for human readers, and there’s no built-in concept of a ‘repair action.’ Zero’s CLI emits structured JSON diagnostics by default. When you run zero check –json, the output looks like: Copy CodeCopiedUse a different Browser { “ok”: false, “diagnostics”: [{ “code”: “NAM003”, “message”: “unknown identifier”, “line”: 3, “repair”: { “id”: “declare-missing-symbol” } }] } Each diagnostic carries a stable code (e.g., NAM003), a human-readable message, a line reference, and a repair object with a typed repair ID. Humans read the message. Agents read the code and repair. The same CLI command surfaces both — there is no separate mode or secondary tool to run. The toolchain is unified into one binary: zero check, zero run, zero build, zero graph, zero size, zero routes, zero skills, zero explain, zero fix, and zero doctor are all subcommands of the same CLI. This matters for agentic workflows because agents don’t need to reason about which tool to invoke for which task. Two subcommands are particularly relevant to the repair loop. zero explain <diagnostic-code> returns a detailed explanation of a given diagnostic code, so an agent can look up NAM003 without parsing prose documentation. zero fix –plan –json <file-or-package> emits a structured fix plan — a machine-readable description of what changes to make to resolve a diagnostic — rather than requiring the agent to infer the fix from the error message alone. zero skills serves a different purpose: it provides version-matched agent guidance directly through the CLI. Running zero skills get zero –full returns focused workflows covering Zero syntax, diagnostics, builds, packages, standard library use, testing, and agent edit loops — all matched to the installed compiler version. This is notable because it means agents working with Zero don’t need to scrape external documentation that may be out of sync with the compiler they’re actually running. Explicit Effects and Capability-Based I/O One of Zero’s core design decisions is that effects are explicit in function signatures. If a function writes to standard output, accesses the filesystem, or makes a network call, it must declare that through a capability object. The canonical entry point in Zero looks like this: Copy CodeCopiedUse a different Browser pub fun main(world: World) -> Void raises { check world.out.write(“hello from zeron”) } The world: World parameter is the capability object that grants access to the outside world. A function that doesn’t receive World (or a capability derived from it) cannot perform I/O — the compiler enforces this at compile time, not at runtime. There is no hidden global process object. The check keyword handles fallible operations. If world.out.write(…) can fail, check surfaces that failure along the call stack. The raises annotation on main signals that the function can propagate errors — making error paths visible in signatures rather than buried in runtime exceptions. Getting Started Installation requires one command: Copy CodeCopiedUse a different Browser curl -fsSL https://zerolang.ai/install.sh | bash export PATH=”$HOME/.zero/bin:$PATH” zero –version The installer downloads the latest binary from the GitHub release and places it in $HOME/.zero/bin/zero. Packages are defined with a zero.json manifest and source files under src/, initialized with zero new cli <name>. A VS Code extension for .0 file syntax highlighting ships in the repository under extensions/vscode/. Marktechpost’s Visual Explainer Vercel Labs 01 / 09  ·  Overview Zero The Programming Languagefor Agents An experimental systems language that gives AI agents structured diagnostics, typed repair metadata, and machine-readable docs — alongside sub-10 KiB native binaries. Systems Language Agent-Native v0.1.1 Apache-2.0 Experimental Released May 15, 2026 Author Chris Tate · Vercel Labs Repo vercel-labs/zero File Extension .0 Context 02 / 09  ·  Why Zero Exists The Agent Repair Loop Problem Most programming languages produce compiler output written for human readers — unstructured text that AI agents must parse to determine what failed and how to fix it. This creates a fragile loop. Agent writes code — compiler emits an error as unstructured text Agent parses text — error format can change between compiler versions No repair hint — there’s no built-in concept of a “repair action” Human steps in — the loop requires manual intervention to resolve errors Zero was designed from day zero so agents can read the code, interpret the diagnostics, and repair the program — without human translation. Core Feature 03 / 09  ·  JSON Diagnostics Structured Compiler Output Running zero check ——json emits machine-readable diagnostics instead of plain text. Every error includes a stable code, a human message, a line number, and a typed repair ID. $ zero check –json { “ok”: false, “diagnostics”: [{ “code”: “NAM003”, “message”: “unknown identifier”, “line”: 3, “repair”: { “id”: “declare-missing-symbol” } }] } code — stable identifier agents can match reliably (NAM003) message — human-readable description of the error repair — typed repair ID agents can act on without text parsing Core Feature 04 / 09

In the final week of the Musk v. Altman trial, lawyers traded blows over Elon Musk’s and OpenAI CEO Sam Altman’s credibility. Altman was grilled on his alleged history of lying and self-dealing involving companies that do business with OpenAI. But he fired back, painting Musk as a power-seeker who wanted to control the development of artificial general intelligence (AGI)—powerful AI that can compete with humans on most cognitive tasks.  As evidence of their commitment to AI safety, OpenAI brought out a golden trophy of a donkey’s ass that was gifted to an employee after he was called a “jackass” for standing up to Musk’s plans to race toward AGI.  Lawyers for both sides also presented their closing arguments, floating unflattering mugshot-style photos of Musk and Altman next to each other on a giant screen. Musk’s lawyer Steven Molo argued that Altman and OpenAI president Greg Brockman broke their promise to use money Musk donated to maintain OpenAI as a nonprofit that develops AI for the benefit of humanity. Instead, they created a for-profit subsidiary that made them extraordinarily wealthy. OpenAI’s lawyer Sarah Eddy argued that Altman and Brockman never promised to keep OpenAI a nonprofit. She added that even though it’s been restructured, OpenAI remains a nonprofit dedicated to developing AI safely. She claimed that Musk sued too late—and that his real motive is to sabotage a competitor to his own AI company, xAI, which he launched in 2023.  Musk is asking the court to unwind the 2025 restructuring that converted OpenAI’s for-profit subsidiary into a public benefit corporation and to remove Altman and Brockman from their roles. He is also seeking as much as $134 billion in damages from OpenAI and Microsoft, to be awarded to OpenAI’s nonprofit.  The jury will begin deliberating on Monday and deliver an advisory verdict as soon as next week. The jury verdict is not binding on the judge, who will decide the case. If the judge rules in Musk’s favor, it could upend OpenAI’s race toward an IPO at a valuation approaching $1 trillion. Meanwhile, xAI is expected to go public as a part of Musk’s rocket company SpaceX as early as June, at a target valuation of $1.75 trillion. Musk the power-seeker, Altman the liar. In the first week of the trial, Musk said he was suing to save OpenAI’s mission to build AI safely for the benefit of humanity. This week, Altman denied Musk was a paladin of AI safety and painted him as a power-seeker who wanted to control OpenAI.  Altman told the jury that in 2017, when Musk and other cofounders were discussing creating a for-profit arm, they asked Musk what would happen to his control over such an entity if he died. “Maybe the control of OpenAI should pass to my children,” Musk said, according to Altman. Musk’s lawyer shot back, grilling Altman on his alleged history of lying. He pointed out that OpenAI’s former executives Ilya Sutskever and Mira Murati, and former board members Helen Toner and Tasha McCauley, all testified that Altman had lied to them. In 2023, Altman was briefly fired as CEO over the alleged behavior. Molo also pressed Altman about his personal investments in startups that do business with OpenAI. Altman testified that he tried to steer OpenAI to buying power from the nuclear energy company Helion Energy, a third of which he owns. (Last Friday, the US House oversight committee launched an investigation into Altman’s potential conflicts of interest. Attorneys general from more than a half-dozen states called for the Securities and Exchange Commission to review them.) During his closing statement, Molo put Altman’s credibility on the stand again. “Imagine that you’re on a hike, and you come upon one of those wooden bridges that you see on a trail, and it’s over a gorge,” he said. “A woman standing by the entry to the bridge says, ‘Don’t worry—the bridge is built on Sam Altman’s version of the truth.’ Would you walk across that bridge?” Altman, who sat behind his lawyers, looked up uneasily every time his name was mentioned.  During her closing argument, Eddy fired back. Musk “never cared about the nonprofit structure,” she said. “What he cared about was winning.”  Musk, though, was absent. Despite the judge’s order that he remain available, he flew to China with President Trump. Did Altman promise to keep OpenAI a nonprofit? During her closing argument, Eddy argued that no testimony or evidence showed any conditions on Musk’s donations, or any promises made by Altman and Brockman to keep the company a nonprofit. “No commitments or promises were made. No restrictions were placed on Mr. Musk’s donations,” she said. Eddy added that it was evident Musk wasn’t truly committed to keeping OpenAI a nonprofit. She noted that in 2017, he tried to create a for-profit subsidiary and fought a bitter battle with Altman and Brockman to have control over it. “I was not opposed to there being a small for-profit that provides funding to the nonprofit,” Musk told the jury earlier in the trial, “as long as the tail didn’t wag the dog.”  Eddy then argued that Musk sued too late, filing in 2024 after the statutes of limitations on his claims ran out. In 2019, OpenAI created a for-profit subsidiary, under which employees and investors received a capped return on their investment.  But Musk testified that he discovered OpenAI had abandoned its nonprofit mission only in 2022, when Microsoft was preparing to invest $10 billion in OpenAI—a deal that closed in 2023. “I was disturbed to see OpenAI with a $20B valuation,” he texted Altman after reading the news. “This is a bait and switch.” Musk told the jury that the $20 billion valuation made him realize “the for-profit is the tail wagging the dog.”  “The 2023 deal was different,” Molo hammered home during his closing argument. Is OpenAI still a nonprofit committed to its mission? A central question raised in the last week of trial was whether OpenAI remains a nonprofit

The AI coding agent market looks almost unrecognizable compared to 2024 or even early 2025. What started as inline autocomplete has evolved into fully autonomous systems that read GitHub issues, navigate multi-file codebases, write fixes, execute tests, and open pull requests — without a human typing a single line of code. By early 2026, roughly 85% of developers reported regularly using some form of AI assistance for coding. The category has fractured into distinct archetypes: terminal agents, AI-native IDEs, cloud-hosted autonomous engineers, and open-source frameworks that let you swap in whatever model you prefer. The problem is that every tool claims to be the best, and the benchmarks used to justify those claims are not always measuring the same things — and in some cases are no longer credible measures at all. This article features the most important AI coding agents by the metrics that actually matter for production software development, while being honest about where those metrics have broken down. If you are an AI/ML engineer, software developer, or data scientist trying to decide where to invest your tooling budget in 2026, start here. How to Read These Benchmarks — Including Why the Most-Cited One Is Now Disputed Before the listing, an important calibration on the numbers — because one major benchmark shift happened mid-cycle and is not yet reflected in most tool comparison articles. SWE-bench Verified has been the industry’s standard coding benchmark since mid-2024. It presents agents with 500 real GitHub issues drawn from popular Python repositories and measures whether the agent can understand the problem, navigate the codebase, generate a fix, and verify that it passes tests — end-to-end, without human guidance. It was a credible proxy. In February 2026, that changed. On February 23, 2026, OpenAI’s Frontier Evals team published a detailed post explaining why it had stopped reporting SWE-bench Verified scores. Their auditors reviewed 138 of the hardest problems across 64 independent runs and found that 59.4% had fundamentally flawed or unsolvable test cases — tests that demanded exact function names not mentioned in the problem statement, or checked unrelated behavior pulled from upstream pull requests. More critically, they found evidence that every major frontier model — GPT-5.2, Claude Opus 4.5, and Gemini 3 Flash — could reproduce the gold-patch solutions verbatim from memory using only the task ID, confirming systematic training data contamination. OpenAI’s conclusion: “Improvements on SWE-bench Verified no longer reflect meaningful improvements in models’ real-world software development abilities.” OpenAI now recommends SWE-bench Pro as the replacement for frontier coding evaluation. This does not make SWE-bench Verified scores useless. Other major labs continue to report them, third-party evaluators continue to run them, and they remain useful for broad directional comparison. But any ranking that presents SWE-bench Verified scores as clean, objective measurements of real-world ability — without this caveat — is giving you an incomplete picture. All scores in this article are flagged accordingly. SWE-bench Pro is harder to interpret than Verified because published results vary significantly by split, scaffold, harness, and reporting source. The benchmark contains 1,865 total tasks divided into a 731-task public set, an 858-task held-out set, and a 276-task commercial/private set drawn from 18 proprietary startup codebases. When the original Scale AI paper measured frontier models using a unified SWE-Agent scaffold, top scores were below 25% — GPT-5 at 23.3% — reflecting a genuinely harder evaluation. However, current public leaderboard and vendor-reported runs now show substantially higher scores under newer models and optimized agent harnesses: OpenAI reports GPT-5.5 at 58.6% on SWE-bench Pro (Public), while Anthropic’s comparison table lists Claude Opus 4.7 at 64.3% and Gemini 3.1 Pro at 54.2%. These numbers should not be directly compared with the original sub-25% SWE-Agent results without noting the scaffold and split differences — the benchmark has not changed, but the evaluation conditions and model generations have. When you see a 60%+ SWE-bench Pro score alongside a sub-25% one, they are measuring the same benchmark under very different conditions, not two separate tests. Terminal-Bench 2.0 evaluates terminal-native workflows: shell scripting, file system operations, environment setup, and DevOps automation. As of April 23, 2026, GPT-5.5 leads at 82.7% on this benchmark — confirmed in OpenAI’s official release. Claude Opus 4.7 scores 69.4% (Anthropic/AWS-reported), and Gemini 3.1 Pro scores 68.5%. An important methodological caveat: different harnesses produce different numbers for the same model. Anthropic’s Opus 4.6 system card showed GPT-5.2-Codex scoring 57.5% on the independent Terminus-2 harness vs 64.7% on OpenAI’s own Codex CLI harness — a 7-point gap from harness alone. When comparing Terminal-Bench figures across sources, always check which execution environment was used. One final cross-benchmark caveat: agent scaffolding matters as much as the underlying model. In a February 2026 evaluation of 731 problems, three different agent frameworks running the same Opus 4.5 model scored 17 issues apart — a 2.3-point gap that changes relative rankings. A benchmark score labeled with a model name reflects the model and the specific scaffold wrapped around it, not the model in isolation. 10 AI Agents for Software Development A Note on Claude Mythos Preview The current leader on SWE-bench Verified among third-party trackers is Claude Mythos Preview at 93.9%, announced April 7, 2026 under Anthropic’s Project Glasswing. It is not generally available. Access is restricted to a limited set of platform partners; Anthropic has stated it does not plan broad release in the near term, in part due to elevated cybersecurity capability concerns. It sits outside the main comparison below because developers cannot access it through standard channels. Its existence does, however, signal that the practical capability ceiling sits substantially above what any publicly available tool currently delivers. #1. Claude Code (Anthropic) SWE-bench Verified (self-reported): 87.6% (Opus 4.7) / 80.8% (Opus 4.6) SWE-bench Pro (Anthropic internal variant): 64.3% (Opus 4.7, #1) / 53.4% (Opus 4.6) Terminal-Bench 2.0: 69.4% (Opus 4.7, Anthropic-reported) CursorBench: 70% (Opus 4.7, Cursor-reported) Claude Code subscription: $20–$200/month | Opus 4.7 API: $5/$25 per million tokens Claude Code is Anthropic’s terminal-native coding agent and the leader on code quality metrics across

Every year the World Health Organization publishes a global health statistics report. It features the numbers behind world health trends and, importantly, assesses whether we’re on track to reach ambitious goals set in 2015. It’s a bit like a health grade. The 2026 report was published on Wednesday. And the results aren’t looking brilliant. While we are seeing some improvements, they are uneven, and they’re far too slow. The targets themselves are part of the United Nations’ Sustainable Development Goals, a sprawling and ambitious plan focused on improving life around the world. The 17 goals were set to tackle poverty and climate change and to boost education, gender equality, health, and well-being, among many other quality of life issues. Those targets were meant to be met by 2030. Perhaps they were a little too ambitious. Here are the numbers and statistics that stood out to me on this year’s world health report card. 1.3 million new cases of HIV in 2024 Before the SDGs, there were the Millennium Development Goals. One MDG target was to halt and reverse the spread of HIV—and that target was exceeded by 2015. Back then, we were considered on track to “end the AIDS epidemic by 2030.” How depressing, then, to see that in 2024 there were an estimated 1.3 million new cases of HIV. That’s 40% lower than the figure from 2010. But it’s still 1.3 million additional people with HIV. The SDG target is to reduce HIV incidence by 90% by 2030—we’re not likely to meet it. 10.7 million new cases of TB The picture is even bleaker for tuberculosis, which ranks 10th on the WHO’s list of top global causes of death. The goal was to reduce cases by 80% between 2015 and 2030. So far, cases have only fallen by a measly 12%. And when you break the change down by region, the Americas saw an increase of 13% An 8.5% rise in malaria cases And then there’s malaria, the mosquito-borne disease with a 7% fatality rate. The European region has been free of malaria since 2015, but the disease is a significant concern in many countries in the Global South, particularly in Africa. The goal was to lower rates by 90% between 2015 and 2030. In 2024, there were an estimated 282 million cases of malaria globally—representing an 8.5% increase in incidence rates. Antimalarial drug resistance is a major challenge here—forms of the malaria virus that are resistant to drugs have been confirmed or suspected in eight countries in Africa, according to a separate WHO report. Mosquitoes that are resistant to commonly used insecticides are present in nine African countries. And climate change, which can alter mosquito habitats, may be making things worse. 42.8 million children are wasting We’re not meeting child health targets, either. Take malnutrition, for example. As of 2024, the global prevalence of wasting in children was 6.6%—that’s a staggering 42.8 million children who are literally wasting away because of a lack of adequate food. On the other end of the spectrum, 5.5% of children are now considered overweight. Both figures were meant to be below 5% by 2030, which now seems unlikely. Vaccination rates are dropping in the Americas Progress in improving childhood vaccination coverage has stalled. Globally, an estimated 76% of children are getting their second dose of a measles vaccine—a figure far below the the approximately 95% needed to prevent outbreaks. The Americas currently has lower rates of vaccine coverage for three of the four “core” vaccines than it did in 2015. This is partly due to a lack of investment, says Goodarz Danaei, an epidemiologist at the Harvard T.H. Chan School of Public Health. “But now we have a misinformation campaign going around vaccines that makes it worse,” he adds. The covid-19 pandemic didn’t exactly help, either. The impact on health services led to millions of children missing out on routine vaccinations. 22.1 million pandemic-related deaths And of course the pandemic affected progress toward health goals in more direct ways: 7 million people died of covid-19. The WHO report estimates that, for each of these, there were an additional two “excess” deaths related to the pandemic, due to disruptions in health care, for example. That puts the total figure at 22.1 million pandemic-related deaths. A woman dies every two minutes from “maternal causes” Maternal mortality rates fell by about 40% between 2020 and 2023. But today’s rate equates to 712 maternal deaths every single day. That’s one every two minutes. The WHO report notes that we’d have to reduce the mortality rate by almost 15% per year in order to meet the 2030 target. This seems incredibly unlikely, particularly given the recent decimation of US funding for global aid programs, which is expected to result in thousands of additional maternal deaths. Progress has also slowed in reducing the risk of death from noninfectious diseases like cancer, diabetes and cardiovascular disease. “Overall, neither the world nor any WHO region is currently on track to meet the 2030 SDG target,” the report states. 2.1 billion people struggle to afford health care Despite plans to make health care more affordable, a significant chunk of the population is being pushed into poverty by health-care costs. In 2022, 2.1 billion people faced financial hardship due to health spending—and 1.6 billion of them were living in or had been pushed into poverty. Across the board, there have been some important improvements in global health. But the achievements have not gone far enough. “The good news is that there is progress,” says Danaei. “But as always, the glass is half empty.” This article first appeared in The Checkup, MIT Technology Review’s weekly biotech newsletter. To receive it in your inbox every Thursday, and read articles like this first, sign up here.

In a dimly lit bedroom, a frightened young woman is thrown onto a bed by a tall, muscular man. He grabs her hand, and flame-like vines crawl across her body, fusing with her flesh. She levitates, then drops. A dragon-shaped tattoo appears across her chest. “Two months,” the man says. “Give me an heir, or I will eat you.” The scene is from Carrying the Dragon King’s Baby, one of the many hundreds of short dramas that appear on apps like DramaWave and ReelShort. There’s just something about this one that isn’t quite right. The lighting may be glossy and cinematic, but the show has an odd visual texture like something between a movie and a video game cutscene.  That’s because Carrying the Dragon King’s Baby is part of a new trend for making these shows entirely with AI: no actors, camera operators, cinematographers, or CGI specialists required. China’s short drama industry has boomed since its launch, in 2018. These ultrashort, melodramatic, and often smutty shows are designed for smartphone viewing, with episodes often running just one or two minutes long: Viewers can finish an entire series in as little as 30 minutes to an hour. The films are made for endless scrolling, packed with emotional confrontations and melodramatic plot twists. The trend’s growth is driven by apps that bombard TikTok, Instagram, and Facebook with cliffhanger-heavy ads designed to lure viewers into buying subscriptions. In 2024, China’s short drama market reached roughly $6.9 billion in revenue, surpassing the country’s annual box office earnings for the first time.  Since 2022, Chinese short drama companies have aggressively expanded overseas, translating existing hits and producing localized series featuring local actors. Globally, short drama apps have approached a billion cumulative downloads. The United States is the biggest market outside of China, providing around 50% of the revenue, according to research firm DataEye. Now the industry is reinventing itself. Chinese short drama companies—already masters of low-budget, algorithmically optimized entertainment—are embracing generative AI to produce content faster and cheaper than ever. An average of 470 AI-generated short dramas were released every day in January, according to DataEye. Short-drama companies like Kunlun Tech are ramping up AI productions, shrinking film crews, and reorganizing the labor pipeline from the ground up. For some studios, AI has moved from being a supporting tool to providing the backbone of production itself. Infinite stories, infinite tropes Short dramas are already famously low-budget. But AI has made them dramatically cheaper to mass-produce, helping to accelerate the entire process—and save money. Production timelines have collapsed. Conceptualization, script writing, casting, shooting, and editing used to take three to four months. With AI, the process can now take less than a month, says Tang Tang, vice president at short-drama platform FlexTV. Producing a short drama in North America once cost roughly $200,000, but AI can cut that cost by 80% to 90%, according to Tang. After expanding into the US market, Chinese short drama companies largely followed the same playbook they used in China: Buy traffic aggressively on TikTok, Facebook, and YouTube; offer a handful of free episodes; then charge viewers to unlock the rest inside the companies’ apps. Decisions about what to produce next are often driven less by creative instinct than by performance data. “We look at what themes, plotlines, and writers resonate with audiences, then quickly adjust,” says Tang. The industry operates at a relentless pace. “Everyone expects quick returns,” Tang says. “In China, if a series doesn’t break even within a month, the industry considers it a failure.”  As a result, screenwriters who spoke with MIT Technology Review said platforms often categorize projects using highly specific keywords that encompass everything from genre and setting to plot structure, such as “campus romance,” “gang rivalry,” “enemies to lovers,” or “rags to riches.” Recently, one of the most popular genres has been “reborn revenge,” a fantasy trope in which a wronged protagonist is miraculously reborn and given a chance to change their fate. “You kind of have to keep the emotional intensity extremely high throughout the show, using the same plot devices over and over again: sudden deaths, betrayals, physical violence, huge confrontations,” says Phoenix Zhu, a freelance short drama screenwriter based in Suzhou. “It’s common to sacrifice narrative logic for shock value, because otherwise people are more likely to scroll away.” Those simple tropes have made the format particularly compatible with AI-generated production. Earlier this year, FlexTV halted all traditionally shot productions and shifted entirely to AI-generated dramas. Kunlun Tech, the parent company of drama apps DramaWave and FreeReels, began producing AI-generated short dramas in 2025 and now offers more than 1,000 AI titles on its platforms. StoReels, another popular short drama company targeting a global audience, has said it aims to produce 100 AI-generated dramas per month. “People’s attention spans are getting shorter, and serialized drama naturally has to get shorter,” says Han “Daniel” Fang, the CEO of Kunlun Tech. Fang told MIT Technology Review that the company is not going to stop investing in traditionally shot short dramas with real actors. But the company is expanding AI-generated productions and gradually increasing their share on its platforms as a low-cost way to experiment with new genres, themes, and ideas. “We want to bring the amount of AI work to 20% of the platform,” Fang says. The format is also rapidly growing overseas. Research firm Omdia estimates that the global microdrama market reached $11 billion in 2025 and will grow to $14 billion by the end of 2026. The United States is expected to generate $1.5 billion in revenue in that market this year. “No one comes to short dramas expecting high art,” says investor Shangguan Hong, former partner of Legend Capital. “The short-drama industry already stands out from traditional TV and filmmaking by being real-time and data-driven. AI only furthers that logic. In a sense, short drama is perfectly compatible with AI.” Inside the content machine The industry’s AI revolution is already changing the type of roles required to make short

The Tesla Semi has officially arrived. The company recently released a photo of the first vehicle rolling off its new full-scale production line. This moment has been nearly a decade in the making: The company first announced the Tesla Semi in late 2017. And now we’ve got final battery specs, official prices, and big news about big orders. The Semi is a relatively affordable electric semitruck with pretty impressive performance. It also comes at a moment when Tesla has lost its grip on the global electric vehicle market. Let’s talk about what’s new with the Tesla Semi and why this could be a breakout moment for electric trucking. Medium- and heavy-duty vehicles, like buses and semitrucks, make up a small fraction of vehicles on the road but contribute an outsize fraction of pollution, including both carbon dioxide emissions and other pollutants like nitrogen oxides (NOx) and small particles. Globally, trucks and buses represent about 8% of total vehicles on the road, but they create 35% of carbon dioxide emissions from road transport. Tesla’s latest addition to its vehicle lineup, the Class 8 Semi, could be part of the solution to cleaning up this polluting sector. (I’ll note here that I briefly interned at Tesla in 2016. I don’t have any ties to or financial interest in the company today.)  In November 2017, Elon Musk took to the stage at a lavish event in LA to announce the Semi. At that event, Musk promised a truck that could go from zero to 60 miles per hour in five seconds, could achieve a range of 500 miles, and would come with thermonuclear-explosion-proof glass. (Remember the era before the Twitter takeover and DOGE, when this was what Musk was known for? A simpler time.) Soon after the unveiling, major corporations including Walmart put in early orders for Tesla Semis. Deliveries were expected in 2019. That deadline obviously didn’t work out. The date was pushed back several times, and Tesla did start delivering a small number of pilot trucks, beginning in 2022. But this year, things got more serious, with the company releasing its final production specifications in February and rolling its first Semi off its high-volume production line in late April.  And last week, WattEV announced an order of 370 Tesla Semis. WattEV offers electric freight operations, essentially providing trucks as a service to companies so they don’t have to purchase their own or supply their own charging infrastructure. The company will pay over $100 million for the new trucks, and the first 50 should be delivered this year, with the full fleet expected by the end of 2027. Those trucks will be supported by megawatt-charging systems located in Oakland, Fresno, Stockton, and Sacramento. With the factory up and running and a huge order on the books, it feels like the Tesla Semi has truly arrived. And some of Musk’s claims from 2017 ring true: The base model has a range of about 320 miles, and the long-range version about 480 miles (quite close to his 500-mile claim). Delivering this much range for this big truck means a whopping battery. The base model Tesla Semi battery pack has a usable capacity of 548 kilowatt-hours, according to a document filed with the California Air Resources Board (CARB). But the battery is even more massive in the long-range version, which boasts a whopping 822 kilowatt-hour battery. Compare these to the Tesla Model 3, which typically comes with a 64 kilowatt-hour pack. I reached out to Tesla to confirm the battery size and ask other questions for this article—the company didn’t respond. These trucks cost quite a bit more than they were expected to in 2017, though. At that time, the expected price was $150,000 for the base model and $180,000 for the long-range. Today, Tesla is pricing the trucks at $260,000 and $300,000, respectively, according to documentation filed with CARB. That’s considerably more expensive than the median diesel truck being sold today, which rang in at $172,500 for the 2025 model year, according to research from the International Council on Clean Transportation. But it’s much cheaper than similar battery-electric trucks available today, where the median is about $411,000. And in California, where companies can get vouchers that cover $120,000 towards the purchase price of an electric truck, the Tesla Semi is competitive right away, especially since electric trucks tend to be much cheaper to run and maintain than diesel ones. Over the years, it wasn’t always clear that the Tesla Semi would ever actually hit the roads. (At that same 2017 event, Musk announced a new Roadster sports car, and that’s nowhere to be seen.) So it’s encouraging to see the factory starting up, and a large order that looks like it could lend this project some commercial momentum. Tesla had a massive impact on the electric vehicle market, and if it can scale production and support charging infrastructure, it could help do the same for trucking. This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here.