Cursor + SpaceXAI: the fastest iterating team wins
Move in the right direction, faster than anyone else.
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Cursor has officially been acquired by SpaceX.
AI coding could be the biggest addressable market of our lives. And, unless you think we’ve fully addressed it (which no one thinks; we are more like 1% of the way there), then the biggest question not just in venture but also in enterprise productivity is, “Which team do you bet on, to ultimately make the winning product?”
Do you bet on the team that is the smartest?
Do you bet on the team that has the most raw power, resources and compute?
Or do you bet on the team that iterates the fastest?
If you think it’s #1, that’s actually a pretty tough one, because every team is smart. Every week, some genius researcher moves from one lab to another and the “smart” leaderboard shifts. So if that’s your bet, good luck handicapping it; we think it’s pretty hard to say.
Maybe you think it’s #2, because you’re bitter-lesson pilled and little else matters for frontier models pulling away from the pack. (Although consider that only one gigawatt-scale data center is actually operational today, and who built it.)
But if you think it’s #3 - which historically is a pretty good bet - then the story of Cursor joining forces with SpaceXAI is one for the ages.
Pre-Cursor
Cursor’s story is different from the other AI storylines. They’re the only premium AI coding product that started as a product, trying to be useful; as opposed to starting in a lab, building foundation models. (Copilot is the other possible answer here, but they’re not in the same league.)
If we rewind just a few years, AI models weren’t very good at writing code. You could start interesting projects with them; but it took a lot of scaffolding to keep them from making everything a mess. Developers are always eager to explore the frontier of coding efficiency, but hardly anyone thought that you could just throw GPT-3.5 at your codebase and expect anything good out of it.
The dominant paradigm in the nascent field of “AI coding” was tab-complete. You’d have your IDE open, and you’d write code like normal; then the model would guess what came next. Although tab-complete wasn’t as revolutionary as what followed, it was a genuine time-saver for developers, and there was real demand for it.
It’s a bit funny that Michael Truell and Cursor defined the “tab-complete” era of coding to the degree they did, because that wasn’t his initial goal when he started Anysphere. He and his MIT cofounders, Sualeh Asif, Aman Sanger and Arvid Lunnemark, first tried making an AI email client which helped users draft and manage emails, and an AI-powered CAD tool for mechanical engineers. As Truell described it recently, they were a bit intimidated by the other AI coding projects; they felt like the space was already covered.
But, as he went on to explain: “We caught the bug. We couldn’t stay away from it. We started working on Cursor at the end of the year; fundamentally, because we wanted to build a tool that we really liked, and a tool that we wanted to use. And nothing out in the market really checked that box for us. … Our goal was to build a product that could be our development environment, that we could stand using, that didn’t make us 2x less productive.”
In early 2023, a few months after the launch of ChatGPT, Truell returned to Hacker News to introduce Cursor, “a code editor built for programming with AI”:
Truell’s various launch attempts on HN
His post got just 11 comments, and Cursor spent much of 2023 as a niche tool among early AI adopters. But the product was excellent, and the models were getting better at coding. Slowly but surely it built momentum among users. Word got around: Cursor is the best tool if you’re serious about using AI to code.
“They really want to build a great product.”
In 2024, things went vertical. Developers discovered the product and loved it, and they told their friends about it, and their friends loved it too. At one user meetup, a user from Japan explained that he’d flown in from Tokyo with a book he’d written in Japanese about how to use Cursor. It was honestly like the Beatles for software; we’d never seen anything like it.
Anysphere—which most people just called “Cursor” —became the fastest software company in history to reach $100 million in annual recurring revenue. From out of nowhere, four dropouts in their early 20s had one of the hottest startups in the world. They were some of the most special founders we’ve ever met. Among other things, they had great taste. If you stepped into their office—having taken off your shoes, of course—you’d encounter plants everywhere; twinkle lights; hygge furniture, picked up by the cofounders. For them, it wasn’t enough that AI coding was functional, or performant. It had to be beautiful.
And it really was. Cursor had an excellent tab-complete model, which it perfected after acquiring Supermaven in November 2024; a sidebar, where you could talk to the models and ask them to do things; and, crucially, the ability to switch between models with ease.
This mattered a lot. There are some enterprise AI applications where the end user doesn’t really care about the model being used, so long as the results are good. But not coding. Developers are notoriously choosy and opinionated about their coding setup, and they’re vocal about protecting their preferences and tastes. So it was very important that Cursor lets you use your model of choice, or toggle between them depending on the task. It put the developer in the sovereign seat, with Cursor as the clearinghouse connecting developers to tokens, with the right scaffolding to make the experience seamless.
Kate Deyneka, founder of Reelful and a longtime Cursor user, reiterates that this product-first instinct is what made Cursor different from the labs. “They are the only AI coding company right now that started as a product, not as a lab. They come from this developer perspective, because they’re developers themselves. They really want to build a great product.” That’s why the early IDE-first strategy mattered: Cursor didn’t ask developers to abandon their workflows overnight. It “wanted to meet people where they are, in VS Code,” Deyneka said, and that transition was essential because “fully trusting AI to do your coding is itself a process.” In her view, Cursor’s early product decisions did more than make developers more efficient; they taught developers to trust AI enough to delegate real coding work to it.
Critics called Cursor a mere “reseller” of the tokens produced by OpenAI, Anthropic, and the like. But developers at that point did not find the models were good enough for plug-and-play autonomy, nor differentiated enough to justify being locked into any one ecosystem. You needed someone to bridge the gap between what the models could do and what developers actually needed, and Cursor was the best product. For a moment, anyway.
The test
By 2025, the foundation models were getting better and better; and the model labs wanted more of the pie. That spring, Anthropic launched Claude Code: you gave it a task in plain English, and it would read your codebase, write code, run commands, and iterate on errors, all on its own. If the Cursor experience seemed like “copilot,” then Claude Code was close to “autopilot.” Still, the models weren’t quite good enough for autopilot just yet. If you were a serious developer, you still wanted to look at the code.
But in the final months of 2025—when a blockbuster new generation of frontier models came out, starting with Anthropic’s release of Opus 4.5—that autopilot seemed to take over. Suddenly lab revenue took off, with Claude Code and OpenAI’s Codex becoming two of the fastest growing products of all time. It looked like the frontier AI labs were running away with the prize.
This raised a difficult question for Cursor.
People had raised doubts about Cursor throughout the year, arguing that AI subscription services were structurally unsound and vulnerable—destined for a “short squeeze.” (We never agreed with that, by the way.) Now they were saying that the IDE—the form factor that made Cursor work—was obsolete. IDEs were essential for hand-writing code, and still necessary in the age of tab-complete. But in the age of agentic coding, when “coding” increasingly meant “telling your coding agents what to do,” it was no longer necessarily the center of the programming experience. If the agent will just do the work for us, and do it well enough that we don’t need to look at the code at all, why do we need the IDE at all?
That didn’t apply for everyone, of course; serious developers still want to look at the code and be familiar with their codebase. But the trajectory of progress was overwhelming. Tab-complete plus LLM chat isn’t so interesting when you have genuinely powerful agentic coding, and AI that can just do things for you. Toggling between models isn’t as compelling when one or two companies have a clear and massive lead in coding capabilities, and are willing to subsidize their owned channels. The value of Cursor’s scaffolding, model flexibility, and coding familiarity was coming under question from all sides, and by January 2026, the online discourse had written off Cursor as a serious contender.
The only way out is through
The critics underrated two things about Cursor, and those two things made all the difference.
The first was the reams of high-quality coding data it had from developers all over the world building on its platform. Cursor knew what good code looked like, and it knew what good coding practices looked like, at a moment where that data became very valuable. Their October 2025 launch of “Composer”, a new coding model developed entirely by Cursor, got shockingly little attention at the time for how fast, cheap, and ergonomically friendly it was.
As Jordan Topoleski, Cursor’s COO, told us: the company had long been “compute-starved; building increasingly capable coding models with a really small amount of compute.” The workaround was to lean into what Cursor uniquely had: a beloved product with massive developer distribution. The team’s thesis was that “it could combine open source base models with unique data from the distribution at scale to create a specialized coding model that could compete well above its weight.”
And the second was simply that Team Cursor was clear-eyed about the situation. On January 5, 2026—spurred by the huge improvements in frontier model capability at the end of 2025—Truell held a meeting for everyone at the company. Cursor had to build a coding model on par with the best in the world while continuing to offer its customers the incredibly valuable ability to diversify their model choices. This would require, Truell announced, that staff cancel all unnecessary meetings and be ready to work brutal hours with new teams on short notice.
It seemed utterly unreasonable that Cursor might pull this off in such a short amount of time. But iteration speed matters, and Cursor moves fast. In February 2026, Cursor announced Composer 1.5, a huge improvement over its predecessor and again a remarkably cheap model—but still behind the Anthropic/OpenAI frontier.
Composer 2 came five weeks later, built on top of Moonshot’s Kimi 2.5 base model, with a huge amount of additional training and reinforcement learning on top custom fit for coding. They were now beating Anthropic and OpenAI on the price-to-performance ratio. In eight more weeks came Composer 2.5: another huge jump in performance, from additional training and RL. Cursor’s homegrown models were starting to look competitive with the frontier labs—not just relative to price but in absolute terms. The evolution was underway.
As they built ever-more-capable models, the Cursor team transformed the product experience around them. In October, they announced “Cursor 2.0,” rearchitecting the Cursor app around interacting with multiple agents in parallel. In April came “Cursor 3.” Cursor no longer really looked like a classic IDE at all: it was “a unified workspace for building software with agents,” structured around the capabilities of the Composer models.
But even then, Cursor had another problem, which couldn’t be solved by determination or iteration. It needed more compute to stay in the fight.
OpenAI and Anthropic, by this point, were raising tens of billions of dollars every few months and secured enormous amounts of locked-in compute. They could train ever-bigger models on enormous clusters. You can be as clever as you like with your training recipes and your data, but at a certain point, you simply need the compute. Cursor needed to take the fight to the frontier labs, and that meant they needed scale. Somehow. But from where?
Rocket man
xAI, you might not have realized, was founded in the same month as Cursor. When Elon Musk started it in March 2023, it certainly got a lot more attention: Elon’s goal was to create a “maximally curious” AI that could “understand the true nature of the universe.” There was an AI race to win, and Elon wanted to win it.
Over the following two years, Elon successfully built massive data centers—the Colossus 1 and Colossus 2 sites straddling the Tennessee / Mississippi border—at unprecedented speeds. xAI had more compute than OpenAI or Anthropic! But it couldn’t close the gap on coding, and by 2025 it became clear that you couldn’t win the AI race without it. Elon admitted that the company wasn’t “built right the first time around.” But he refused to give up. “Whether it is the best remains to be seen,” he said in May 2026, “but I will never give up. Never.”
So Elon got in touch with Truell, who’d made his own coding models work shockingly well with so little compute.
In April 2026, SpaceX—which had absorbed xAI earlier that year—announced a potential deal with Cursor. It was a sort of call option on the company: SpaceX had the right to acquire the company for $60 billion, or, if the deal didn’t go through, it would pay a breakup fee of $1.5 billion in cash and $8.5 billion in compute. On June 12, SpaceX went public in the largest IPO in history; and four days later, Elon confirmed his intentions: SpaceX will buy Cursor in a $60 billion all-stock transaction. It is the largest acquisition of a venture-backed startup, ever.
Access to SpaceXAI data centers changes the scale of what Cursor can attempt. Jordan said the partnership gives Cursor “an order of magnitude more compute,” which will let the team “run significantly more experiments, and take advantage of significantly more data than before”. That matters especially for post-training: more compute means Cursor can iterate faster on the specialized recipes that turn strong base models into highly useful coding models, while keeping the model tightly coupled to the product. As Jordan put it, the company’s advantage is “not viewing the model and the product as separate things, but building towards the most useful model and most useful product experiences, marrying the two things together.”
Cursor and SpaceXAI are a deep cultural fit with one another. They both work with a pace and intensity that borders on the absurd. And they’re both defined by iteration. It doesn’t matter if you get it wrong the first time, or the second, or the third—even the tenth or the twentieth. Just keep on going. If you’re moving in the right direction faster than anyone else, you’ll probably win. That’s the entire story of Elon’s career, from SpaceX’s Falcon to Tesla’s Full Self-Driving. And it’s also the story of Team Cursor. Cursor reinvented itself as a company not once but twice: first, from email client to tab-complete IDE and token reseller; and again, from tab-complete IDE and token reseller to AI lab and token creator. And it did all of that in less than four years.
Cursor and the enterprise
By Jordan’s estimation, the Tab/autocomplete model gave developers a 5–10% productivity lift, and then agents boosted the productivity gains to around 35%. Now, Cursor is entering a third wave of multiple parallel and longer-running agents, specifically aimed at being useful in complex enterprise environments, with agentic sessions that run for hours and the average enterprise customer getting about 65% of their production code from AI.
He recalled meeting with the CIO of a Fortune 500 company who told him Cursor had increased the organization’s code output from about 150,000 lines per week to 800,000 lines per week. The upside was obvious, but the second-order problem was just as immediate. The increase had totally blown up the entire process of code review, creating a new bottleneck around keeping code high-quality as it moved toward production.
Cursor had to quickly make progress on what Jordan calls the “outer loop”: the process of reviewing, integrating, testing, and shipping code into production, both “to the right, and to the left of where code is generated across the software development lifecycle.” Hence, products like BugBot, Origin, SDKs, automations, and cloud agents: they’re responses to the new problems created when code generation becomes dramatically cheaper and faster, and where the scarce resource is no longer writing code, it’s vouching for it.
The numbers confirm the story. At the beginning of 2025, “80% plus” of Cursor’s revenue still came from the B2C side of the business, while B2B was more of a burgeoning piece of the organization. But that mix has flipped pretty quickly. At the moment of teaming up with SpaceX, almost 80% of revenue was from B2B. The shift reflects how quickly large customers expand once Cursor becomes embedded: early enterprise cohorts often grow usage by more than 10x in their first year, with accounts moving from “a million dollar line item to a $10 million line item to a $100 million line item” as the product’s impact spreads through the organization.
At one large entertainment company, a six-month, $1.2 million legacy code migration was completed during a three-day hackathon by seven or eight engineers. At NVIDIA, Cursor went from an initial POC to 100% of developers and 30,000+ weekly active users within their first few months of partnering together, making it the fastest, most broadly adopted tool the company had used across the organization. And at National Australia Bank, a team of 100–150 engineers completed a legacy .NET monolith migration to a Java backend and React frontend that was scheduled for nine months, in nine weeks; since then, Jordan told us that adoption has grown to 5,000–6,000 engineers, with the CIO pointing to Cursor’s multi-model setup as a way to keep costs under control at scale.
Grok Bot, which came out the other day, opened up the next frontier for SpaceXAI and Cursor to conquer together: the rest of the organization. Giving developer powers to everyone else in the company has been one of the Holy Grails of AI adoption, but the product just has to work to make any real progress. The immediate reaction on the timeline wasn’t just “it gets the job done,” but something much more special: “This is so fun!” That’s when you know a product has the special sauce: if people are having genuine fun using it, then not only have you helped them be more productive, you’ve probably made a real difference in their satisfaction and joy at work, too.
The future is here, and it’s awesome.
Long live Cursor
Michael has a quiet confidence about him, and when he tells you he’s going to build a generational company, you believe him. Even when he was sitting in our office in June 2024 telling us confidently why enterprises were going to buy Cursor instead of continuing to use VS Code, it was so hard to believe, but we just knew he was going to become one of the great stories of Silicon Valley.
In the world of venture, you’re always looking for stories like this. Unexpected arrivals and rapid ascensions by no-name kids who started the company in their garage: Jobs, Bezos, the Google guys. Falls from grace and improbable comebacks: Jobs again, Palmer Luckey, Travis Kalanick. And existential crises that either kill a company or make it immortal: Apple in the 1990s, Uber in the 2010s, Elon’s companies seemingly all the time.
What makes this story distinct is the speed at which it happened. Most of the other classic stories transpired over 10 or 20 or even 30 years; the entire arc of Cursor—meteoric rise, near-death narrative, strategic reinvention, $60 billion acquisition—took roughly four years.
Acquisitions can sometimes be socially celebrated as a sunset for a company, and that was how a lot of people reacted to the news. They passed around photos of Truell’s old Hacker News posts, or screenshots of messages they’d received from Truell in 2022 or 2023 asking if they wanted to join the team, or stories about how hard the Cursor team worked. But that’s a bad misreading of both Elon and Cursor. If that’s what they wanted, then Elon wouldn’t be Elon, and Cursor would soon be fading from memory.
Elon and SpaceXAI want to win the AI coding race and the optimal path to do it is alongside Michael and possibly the only team that can keep up with them. As they see it, the company with the best GPU-to-coding-ability ratio in the world is suddenly getting access to an enormous amount of additional compute: SpaceXAI’s gargantuan data centers, SpaceX’s broader infrastructure, and Elon’s uncanny ability to bring new capacity online fast. And with this week’s release of Grok 4.6, Cursor’s ability to produce Pareto frontier models, combined with SpaceXAI compute, is already proving to be a real competitive threat to the frontier.
AI coding barely existed as a market a few years ago, and we have barely any idea what it’s going to look like in a few more. In a market like that, you should probably bet on the people who move the fastest. There were plenty of companies that were very smart and had excellent initial products and simply did not reinvent themselves when the situation demanded it: companies that lost their early leads and faded from memory. The list of promising startups that couldn’t adapt is long and growing.
Cursor and Elon are the opposite. They didn’t get every call right; even the smartest and most capable people don’t get every call right. But they iterated relentlessly, and they really, really want to win.
A few days after the Cursor acquisition, an old story about Michael Truell came out from his days competing in the Neo accelerator, in the long-ago year 2019. His mentors recalled his maturity, his curiosity, and his “mischievous confidence.” At one group competition, one of them recalled, Truell gave his team a very unsubtle name: he called it “Winning Team.”
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"The scarce resource is no longer writing code, it's vouching for it." Iteration speed wins the model race but the real bottleneck has moved to the outer loop of review, testing, shipping.
Cursor gives xAI the mass adopted harness that it desperately need.
A mass adopted harness gives the critical feedback loop:
-Model is great.
-Companies use it through the integrated harness
-You get the data of usage to train the model
-The model gets better
-People use it more through the harness...
That's the flywheel of OAI and Anthropic because they have claude and codex.
That's the flywheel google is missing.