Cursor is closing in on one of the largest venture checks of the year, with talks underway for a funding round that would bring in at least $2 billion and value the four-year-old company at roughly $50 billion before the new money, according to people familiar with the discussions quoted by TechCrunch.

Returning backers Thrive Capital and Andreessen Horowitz are expected to lead the deal, while Battery Ventures is in line to join as a new investor. Nvidia, already a partner, is also poised to write another check. The round is already oversubscribed, though terms could still shift before closing.

If completed, the financing would nearly double Cursor’s post-money valuation from just six months ago, when it raised $2.3 billion at $29.3 billion. That kind of jump in such a short window underscores the ferocious investor appetite for tools that promise to reshape how software gets written.

Despite stiff competition from heavyweights, including Anthropic’s Claude Code and OpenAI’s updated Codex, Cursor’s revenue has kept climbing at a startling pace. The company is projecting an annualized revenue run rate above $6 billion by the end of 2026, implying it expects to more than triple its current trajectory in the next ten months.

As recently as February, its annualized revenue had already crossed $2 billion, having doubled in just three months, with roughly 60 percent coming from enterprise customers.

Cursor, originally known as Anysphere, was founded in 2022 by four MIT students: Michael Truell, Sualeh Asif, Arvid Lunnemark, and Aman Sanger. What started as an ambitious student project has exploded into one of the fastest-growing AI companies on record, now used daily by a significant portion of Fortune 500 companies and generating hundreds of millions of lines of code for enterprises.

A key turning point came last November with the launch of Cursor’s proprietary Composer model. Before that, like many AI coding tools reliant on third-party large language models, Cursor was running at negative gross margins — the cost of inference simply outstripped what it could charge. Composer, combined with smarter routing to more affordable models such as China’s Kimi, has flipped the economics.

The introduction of a proprietary Composer model last November, along with the ability to call on less expensive models like China’s Kimi, has helped the company achieve slight gross margin profitability, the people said.

The company has now reached a slight overall gross margin profitability. On enterprise deals, the margins are solidly positive, though it still loses money on many individual developer subscriptions.

That shift is strategic as much as financial. By depending less on outside model providers, especially Anthropic, whose Claude Code has become Cursor’s most direct rival, the startup is working to protect itself from being commoditized or displaced by the very companies supplying its underlying intelligence.

The rapid move toward in-house capabilities also points to a broader reality in the AI coding space: raw model performance is becoming more democratized, so sustainable advantage increasingly hinges on tight product integration, superior user experience, enterprise-grade reliability, and defensible data moats.

Cursor appears to be winning on the product front, with developers praising its speed, context awareness, and ability to handle large codebases in ways that feel almost collaborative.

The fundraising comes amid intense competition, but also explosive demand. AI-assisted coding is moving quickly from experimental sidekick to standard developer workflow. Companies are racing to adopt these tools not just to boost individual productivity but to accelerate entire engineering organizations. Cursor’s ability to command premium pricing from large enterprises, even while still refining its margins on the consumer side, shows it is carving out a meaningful position in that transition.

For investors, the $50 billion pre-money mark represents a bold wager that Cursor can maintain its momentum as the field matures. The participation of Nvidia adds another layer of validation, given the chipmaker’s central role in powering the AI infrastructure that makes tools like Cursor possible.

With the round still in flux and terms not finalized, the exact size and final valuation could shift. But the early interest already signals that Cursor has graduated from promising startup to one of the defining companies in the AI developer tooling boom. Founded by MIT students barely four years ago, it now sits at the center of a transformation that could reshape how software is built for decades to come — if it can keep executing at the pace it has set so far.

Netflix is moving to reshape how audiences find and consume content, introducing a TikTok-style vertical video feed inside its app while embedding artificial intelligence more deeply across recommendations, content creation, and advertising.

The short-form feed, set to launch this month after a year of testing, marks a notable shift in the company’s interface design. It allows users to scroll through clips drawn from films, series, and video podcasts, effectively turning discovery into a continuous, algorithm-driven experience. While the format mirrors mechanics popularized by TikTok, the strategic intent is distinct. Netflix is not attempting to become a social platform; it is trying to compress the time between opening the app and choosing what to watch.

That problem has become more acute as Netflix’s catalogue expands. With hundreds of millions of users and a growing mix of long-form and episodic content, the platform faces what executives have long described as a “decision bottleneck.” The vertical feed is designed to address this by surfacing high-signal content snippets that can trigger immediate viewing decisions, reducing reliance on static thumbnails and traditional browsing rows.

Co-CEO Gregory Peters framed the initiative as an evolution of Netflix’s recommendation engine, which has been central to its strategy for two decades.

“We still see tremendous room to make it better by leveraging newer technologies,” he said, pointing to advances in AI model architectures that allow faster iteration and more precise personalization.

In effect, the feed becomes both a product feature and a data engine, continuously refining user preferences based on micro-interactions such as scroll speed, replays, and skips.

This is where the integration with ChatGPT-powered search becomes more consequential. Introduced last year, the conversational search tool allows users to describe what they want in natural language. Combined with a vertical feed, Netflix is building a multi-layered discovery system, one that blends passive consumption with active querying. The convergence suggests a broader ambition to move beyond recommendation as a static output toward a more adaptive, real-time system.

Beyond the interface, the company is advancing AI deeper into the production pipeline. Co-CEO Ted Sarandos positioned generative AI as an augmentation tool for creators rather than a replacement.

“It takes a great artist to make great art,” he said, emphasizing that AI’s role is to improve tools and workflows.

Practically, this could reshape cost structures in areas such as visual effects, localization, dubbing, and pre-production design, where automation can compress timelines and reduce overhead.

Netflix’s acquisition of Interpositive, an AI-focused company co-founded by Ben Affleck, provides a clearer signal of intent. Unlike general-purpose generative AI tools, Interpositive’s technology is tailored specifically for filmmaking, suggesting Netflix is investing in proprietary systems that could differentiate its production capabilities. Early interest from creators indicates that adoption may be driven not just by cost savings, but by expanded creative possibilities.

The commercial implications extend to advertising. Netflix expects to generate about $3 billion in ad revenue this year, and AI is central to that target. More advanced recommendation systems can be repurposed for ad targeting, enabling dynamic ad insertion, personalized formats, and improved measurement. This effectively turns the platform’s core strength, user data and engagement patterns, into a monetization engine beyond subscriptions.

Financial results provide context for the scale of these bets. First-quarter revenue rose 16.2% year-on-year to $12.25 billion, while profit surged 83% to $5.28 billion. The margin expansion indicates that Netflix is beginning to benefit from operating leverage, where incremental revenue growth outpaces cost increases. The company ended 2025 with 325 million paying subscribers, maintaining its position as the largest global streaming platform.

Recent price increases in the United States are expected to further lift revenue, though they also introduce the risk of higher churn in a market where competition remains intense. Rivals continue to invest heavily in both content and technology, making differentiation in user experience increasingly important.

Netflix’s pivot toward short-form discovery comes at a time when viewing habits are shifting, particularly among younger audiences who are accustomed to algorithmic feeds and shorter content cycles. By integrating a vertical feed into a long-form platform, Netflix is attempting to bridge two consumption models, capturing the engagement dynamics of short video without abandoning its core identity.

However, the strategy carries execution risks. A feed-driven interface could fragment attention, encouraging sampling over sustained viewing. It also raises questions about content positioning, particularly for high-budget productions that rely on immersive storytelling rather than quick-hit engagement. Balancing these dynamics will be critical.

Governance changes add another layer to the transition. Co-founder Reed Hastings is set to leave the board this summer, closing a chapter in the company’s leadership evolution. The shift places greater operational responsibility on the current executive structure as Netflix navigates this next phase.

Together, the introduction of a vertical video feed and the expansion of AI across the business point to a company recalibrating its model around speed, personalization, and efficiency.

The U.S. government is moving to place one of the most powerful new cybersecurity tools yet developed into the hands of federal agencies, even as officials acknowledge that the same technology could introduce a new class of risk to already fragile digital systems.

The new tool is Mythos, a frontier model developed by Anthropic, which is being tested under a restricted program known as Project Glasswing. The initiative allows select organizations to access an early version of the system for defensive purposes, reflecting a broader shift in how governments are beginning to operationalize advanced artificial intelligence in national security.

What distinguishes Mythos is not simply its ability to write code or analyze systems, but the speed and scale at which it can uncover weaknesses. According to people familiar with its early use, the model has already identified thousands of vulnerabilities across widely used software, from operating systems to web infrastructure. In conventional cybersecurity workflows, such discoveries would take months, sometimes years, to surface through manual audits or fragmented testing processes.

That compression of time is precisely what makes the tool both valuable and potentially destabilizing.

In a communication to senior officials, the White House Office of Management and Budget indicated that it is working with industry partners and intelligence agencies to establish safeguards before allowing broader access to a modified version of the system. The message, sent by federal chief information officer Gregory Barbaccia, stopped short of confirming when or how agencies would begin using the model, but made clear that preparations are underway.

“We’re working closely with model providers, other industry partners, and the intelligence community to ensure the appropriate guardrails and safeguards are in place before potentially releasing a modified version of the model to agencies,” Barbaccia said.

The careful phrasing pinpoints the dilemma facing policymakers. Tools like Mythos could fundamentally change how governments defend critical infrastructure. By automating the discovery of software flaws and mapping potential attack paths, they offer the possibility of shifting cybersecurity from a reactive discipline, responding to breaches after they occur, to a more anticipatory one, where vulnerabilities are identified and addressed before they can be exploited.

But that same capability raises uncomfortable questions. A system that can rapidly identify and simulate exploitation of weaknesses could, in the wrong hands, accelerate the development of sophisticated cyberattacks. The concern is not theoretical. Security analysts have long warned that advances in automation could tilt the balance toward offense, particularly if defensive measures fail to keep pace.

This is why the U.S. government appears to be proceeding with unusual caution. Officials are exploring a controlled deployment, likely with restrictions on how the system can be queried, what data it can access, and how its outputs are monitored, rather than releasing the model broadly. The goal is to capture the defensive benefits while limiting the risk of misuse or unintended leakage.

The stakes are high

Federal agencies oversee vast networks of legacy and modern systems, many of which underpin essential services ranging from financial infrastructure to national defense. These systems are often complex, interconnected, and difficult to secure comprehensively. A tool capable of scanning such environments at scale could expose weaknesses that have gone undetected for years.

At the same time, the initiative reflects a growing recognition that the threat landscape is evolving faster than traditional defenses. State-backed actors and organized cyber groups are already experimenting with automation and machine-assisted attacks. In that context, withholding advanced tools from defenders may no longer be a viable strategy. The calculus is shifting toward controlled adoption, even if it introduces new layers of risk.

The move also highlights the changing dynamics between Washington and the private sector. Anthropic has been in discussions with the Trump administration over the deployment of Mythos, even as its relationship with the Pentagon has faced strain following a contract dispute. Treasury and Fed chiefs had earlier warned banks executives about deploying Mythos.The engagement offers Anthropic an opportunity to position itself at the center of a rapidly expanding market for AI-driven cybersecurity tools, one that is likely to attract sustained government and enterprise spending.

For the government, it is a test of whether emerging technologies can be harnessed without outpacing the institutions meant to regulate and control them.

Beyond immediate deployment questions, the introduction of systems like Mythos signals a deeper transformation. Cybersecurity is moving away from incremental improvements toward a model defined by asymmetry and speed, where the ability to process vast amounts of code and system data in real time becomes a decisive advantage. In such an environment, the distinction between defense and offense becomes increasingly blurred, and the margin for error narrows.

The absence of a clear rollout timeline indicates that officials are acutely aware of these dynamics.