While consumer attention has focused on the generative AI battles between OpenAI and Google, Anthropic has executed a disciplined enterprise strategy centered on coding — potentially the most dear enterprise AI use case. The results have gotten increasingly clear: Claude is positioning itself because the LLM that matters most for businesses.
The evidence? Anthropic’s Claude 3.7 Sonnet, released just two weeks ago, set recent benchmark records for coding performance. Simultaneously, the corporate launched Claude Code, a command-line AI agent that helps developers construct applications faster. Meanwhile, Cursor — an AI-powered code editor that defaults to Anthropic’s Claude model — has surged to a reported $100 million in annual recurring revenue in only 12 months.
Anthropic’s deliberate concentrate on coding comes as enterprises increasingly recognize the ability of AI coding agents, which enable each seasoned developers and non-coders to construct applications with unprecedented speed and efficiency. “Anthropic continues to return out on top,” said Guillermo Rauch, CEO of Vercel, one other fast-growing company that lets developers, including non-coders, deploy front-end applications. Last 12 months, Vercel switched its lead coding model from OpenAI’s GPT to Anthropic’s Claude after evaluating the models’ performance on key coding tasks.
Claude 3.7: Setting recent benchmarks for AI coding
Released February 24, Claude 3.7 Sonnet leads on nearly all coding benchmarks. It scored a powerful 70.3% on the respected SWE-bench benchmark, which measures an agent’s software development skills, handily outperforming nearest competitors OpenAI’s o1 (48.9%) and DeepSeek-R1 (49.2%). It also outperforms competitors on agentic tasks.
Developer communities have quickly verified these ends in real-world testing. Reddit threads comparing Claude 3.7 with Grok 3, the newly released model from Elon Musk’s xAI, consistently favor Anthropic’s model for coding tasks. “Based on what I’ve tested, Claude 3.7 appears to be the perfect for writing code (at the least for me),” said a top commenter. (Update: Even Manus, the brand new Chinese multi-purpose agent that took the world by storm earlier this week, when it launched saying it was higher than Open AI’s Deep Research and other autonomous tasks, was largely built on Claude.)
Alongside the three.7 Sonnet release, Anthropic launched Claude Code, an AI coding agent that works directly through the command line. This complements the corporate’s October release of Computer Use, which enables Claude to interact with a user’s computer, including using a browser to look the net, opening applications, and inputting text.
Most notable is what Anthropic hasn’t done. Unlike competitors that rush to match one another feature-for-feature, the corporate hasn’t even bothered to integrate web search functionality into its app — a basic feature most users expect. This calculated omission signals that Anthropic isn’t competing for general consumers but is laser-focused on the enterprise market, where coding capabilities deliver much higher ROI than search.
Hands-on with Claude’s coding capabilities
To test the real-world capabilities of those coding agents, I experimented with constructing a database to store VentureBeat articles using three different approaches: Claude 3.7 Sonnet through Anthropic’s app; Cursor’s coding agent; and Claude Code.
Using Claude 3.7 directly through Anthropic’s app, I discovered the answer provided remarkable guidance for a non-coder like myself. It really helpful several options, from very robust solutions using things like PostgreSQL database, to easier, lightweight ones like using Airtable. I selected the lightweight solution, and Claude methodically walked me through find out how to pull articles from the VentureBeat API into Airtable using Make.com for connections. The process took about two hours, including some authentication challenges, but resulted in a functional system. You could say that as an alternative of all the code for me, it showed me a master plan on to do it.
Cursor, which defaults to Claude’s models, is a full-fledged code editor and was more wanting to automate the method. However, it required permission at every step, making a somewhat tedious workflow.
Claude Code offered one more approach, running directly within the terminal and using SQLite to create a neighborhood database that pulled articles from our RSS feed. This solution was simpler and more reliable when it comes to getting me to my end goal, but definitely less robust and feature-rich than the Airtable implementation. I’m now understanding the character of those tradeoffs, and know that the coding agent I pick really is dependent upon the particular project.
The key insight: Even as a non-developer, I used to be capable of construct functional database applications using all three approaches — something that will have been unthinkable only a 12 months ago. And all of them relied on Claude under the hood.
For a more detailed review of find out how to do that so-called “vibe coding,” where you depend on agents to code things while not doing any coding yourself, read this great piece by developer Simon Willison published yesterday. The process might be very buggy, and frustrating at times, but with the best concessions to this, you possibly can go a great distance.
The strategy: Why coding is Anthropic’s enterprise play
Anthropic’s singular concentrate on coding capabilities isn’t accidental. According to projections reportedly leaked to The Information, Anthropic goals to reach $34.5 billion in revenue by 2027 — an 86-fold increase from current levels. Approximately 67% of this projected revenue would come from API business, with enterprise coding applications as the first driver. While Anthropic hasn’t released exact numbers for its revenue up to now, it said its coding revenue surged 1,000% during the last quarter of 2024. Last week, Anthropic announced it had raised $3.5 billion more in funding at a $61.5 billion valuation.
This coding bet is supported by Anthropic’s own Economic Index, which found that 37.2% of queries sent to Claude were within the “computer and mathematical” category, primarily covering software engineering tasks like code modification, debugging and network troubleshooting.
Anthropic appears to be marching to its own beat — at a time when competitors are distracted, rushing to cover each enterprise and consumer markets with feature parity. OpenAI’s lead is reinforced from its early consumer recognition and usage, and it’s stuck attempting to serve each regular users and businesses with multiple models and functionality. Google is chasing this trend too, attempting to have considered one of all the pieces.
Anthropic’s comparatively disciplined strategy extends to its product decisions. Instead of chasing consumer market share, the corporate has prioritized enterprise features like GitHub integration, audit logs, customizable permissions and domain-specific security controls. Six months ago, it introduced an enormous 500,000-token context window for developers, while Google limited its 1-million-token window to non-public testers. The result’s a comprehensive coding-focused offering that enterprises are increasingly adopting.
The company recently introduced features allowing non-coders to publish AI-created applications inside their organizations, and just last week upgraded its console with enhanced collaboration capabilities, including shareable prompts and templates. This democratization reflects a type of Trojan Horse strategy: First enable developers to construct powerful foundations, then expand access to the broader enterprise workforce, including up into the company suite.
The coding agent ecosystem: Cursor and beyond
Perhaps probably the most telling sign of Anthropic’s success is the explosive growth of Cursor, an AI code editor that reportedly has 360,000 users, with greater than 40,000 of them paying customers, after just 12 months — making it possibly the fastest SaaS company to achieve that milestone.
Cursor’s success is inextricably linked to Claude. “You’ve got to think their primary customer is Cursor,” noted Sam Witteveen, cofounder of Red Dragon, an independent developer of AI agents. “Most people on (Cursor) were using the Claude Sonnet model — the three.5 models — already. And now it seems everyone’s just migrating over to three.7.”
The relationship between Anthropic and its ecosystem extends beyond individual corporations like Cursor. In November, Anthropic released its Model Context Protocol (MCP) as an open standard, allowing developers to construct tools that interact with Claude models. The standard is being widely adopted by developers.
“By launching this as an open protocol, they’re type of saying, ‘Hey, everyone, have at it,’” explained Witteveen. “You can develop whatever you wish that matches this protocol. We’re going to support this protocol.”
This approach creates a virtuous cycle: Developers construct tools for Claude, which makes Claude more worthwhile to enterprises, which drives more adoption, which attracts more developers.
The competition: Microsoft, OpenAI, Google and open source
While Anthropic has found its focus, competitors are pursuing different strategies with various results.
Microsoft maintains significant momentum through its GitHub Copilot, which has 1.3 million paid users and has been adopted by greater than 77,000 organizations in roughly two years. Companies like Honeywell, State Street, TD Bank Group and Levi’s are amongst its users. This widespread adoption stems largely from Microsoft’s existing enterprise relationships and its first-mover advantage, whereby it invested early into OpenAI and used that company’s models to power Copilot.
However, even Microsoft has acknowledged Anthropic’s strength. In October, it allowed GitHub Copilot users to decide on Anthropic’s models as an alternative choice to OpenAI. And OpenAI’s recent models — o1 and the newer o3, which emphasize reasoning through prolonged considering — haven’t demonstrated particular strengths in coding or agentic tasks.
Google has made its own play by recently making its Code Assist free, but this move seems more defensive than strategic.
The open source movement is one other significant force on this landscape. Meta’s Llama models have gained substantial enterprise traction, with major corporations like AT&T, DoorDash and Goldman Sachs deploying Llama-based models for various applications. The open-source approach offers enterprises greater control, customization options and value advantages that closed models can’t match, as VentureBeat reported last 12 months.
Rather than seeing this as a direct threat, Anthropic appears to be positioning itself as complementary to open source. Enterprise customers can use Claude alongside open-source models depending on specific needs, a hybrid approach that maximizes the strengths of every.
In fact, most enterprise corporations of scale I’ve talked with over the past several months are explicitly multimodal, in that their AI workflows allow them to make use of whatever model is best for a given case. Intuit was an early example of an organization that had bet on OpenAI as a default for its tax return applications, but then last 12 months switched to Claude since it was superior in some cases. The pain of switching led Intuit to create an AI orchestration framework that allowed switching between models to be far more seamless, as Nhung Ho, Intuit’s VP of AI, told VentureBeat on the time.
Most other enterprise corporations have since followed an analogous practice. They use whatever model is best for the particular case, pulling in models with easy API calls. In some cases, an open-source model like Llama might work well, but in others — for instance, in calculations where accuracy is vital — Claude is the alternative, Intuit’s Ho explained at VentureBeat’s VB Transform event last 12 months.
Over the past couple of days, I’ve been attending the HumanX conference in Las Vegas, where a whole lot of developers gathered to discuss AI. Claude comes up almost all the time every time the subject of agents or coding is raised. Over lunch yesterday, Julianne Averill, managing director at Danforth Advisors, which advises life science corporations, said her company had found Claude superior for a lot of such tasks, including constructing structured evaluation tables.
Vercel CEO Guillermo Rauch, one other attendee, said his company, which has surpassed $100 million in annual revenue, selected Claude last 12 months as its default model to assist developers code after doing rigorous evaluations of all models. “3.7 is king,” Rauch told VentureBeat. He agreed it’s vital to supply developers a alternative of models, because the breakneck pace of advances means there can’t be loyalty to a single model. But while Vercel’s V0 product, which lets users generate web user interfaces (UIs) using natural-language prompts, offers that alternative, it has to choose a default model to assist users during their initial ideation and reasoning phase. That model is Claude Sonnet. “You need the architect model that’s able to reasoning and does the lion’s share of code generation,” he said. “A big chunk of our pipeline is powered by Anthropic Sonnet.” Adobe, Chick-Fil-A and Bed Bath and Beyond are Vercel’s customers.
Still Rauch cautioned that fluidity within the LLM race stays, and the lead model could change at any time. Vercel experimented with China’s DeepSeek, he said, but found it fell just wanting matching Claude’s Sonnet. Similarly, he said, Alibaba’s Qwen model has gotten superb.
Enterprise implications: Making the shift to coding agents
For enterprise decision-makers, this rapidly evolving landscape presents each opportunities and challenges.
Security stays a top concern, but a recent independent report found Claude 3.7 Sonnet to be probably the most secure model yet — the just one tested that proved “jailbreak-proof.” This security stance, combined with Anthropic’s backing from each Google and Amazon (and integration into AWS Bedrock), positions it well for enterprise adoption.
The rise of coding agents isn’t just changing how applications are built — it’s democratizing the method. According to GitHub, 92% of U.S.-based developers at enterprise corporations were already using AI-powered coding tools at work 18 months ago. That number has likely grown substantially since then.
“The challenge that individuals are having (due to) not being a coder is admittedly that they don’t know a variety of the terminology. They don’t know best practices,” explained Witteveen. AI coding agents increasingly bridge this gap, allowing technical and non-technical team members to collaborate more effectively.
For enterprise adoption, Witteveen recommends a balanced approach: “It’s the balance between security and experimentation in the meanwhile. Clearly, on the developer side, persons are beginning to construct real-world apps with these things.”
For a deeper exploration of those issues, try my recent YouTube video conversation with Witteveen, where we take a deep dive into the state of coding agents and what they mean for enterprise AI strategy.
Looking ahead: the long run of enterprise coding
The rise of AI coding agents signals a fundamental shift in enterprise software development. When used effectively, these tools don’t replace developers but transform their roles, allowing them to concentrate on architecture and innovation fairly than implementation details.
Anthropic’s disciplined approach in focusing specifically on coding capabilities while competitors chase multiple priorities appears to be paying dividends for the corporate. By the tip of 2025, we may look back on this era because the moment when AI coding agents became essential enterprise tools — with Claude leading the best way.
For technical decision-makers, the message is obvious: Start experimenting with these tools now or risk falling behind competitors who’re already using them to speed up development cycles dramatically. This moment echoes the early days of the iPhone revolution, when corporations initially tried to dam “unsanctioned” devices from their corporate networks, only to eventually embrace BYOD policies as worker demand became overwhelming. Some corporations VentureBeat has talked with, like Honeywell, have recently similarly tried to shut down “rogue” use of AI coding tools not approved by IT.
Speaking Monday on the HumanX conference, James Reggio, the CTO of Brex, an organization that gives bank cards and other financial services to small and mid-sized enterprises, said his company initially also tried to implement a top-down approach to AI model selection, in an effort to achieve perfection. But the corporate faced revolt amongst its developer employees, and shortly realized this was futile. It decided to permit users to experiment freely. Smart corporations are already organising secure sandbox environments to permit controlled experimentation. Organizations that create clear guardrails while encouraging innovation will profit from each worker enthusiasm and insights about how these tools can best serve their unique needs — positioning themselves ahead of competitors who resist change. And Anthropic’s Claude, at the least for now, is a giant beneficiary of this movement.
Watch my video with developer Sam Witteveen here for a full deep dive into the coding agent trend:
