[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"article-qodo-raises-70m-to-verify-ai-written-code-en":3,"tags-qodo-raises-70m-to-verify-ai-written-code-en":30,"related-lang-qodo-raises-70m-to-verify-ai-written-code-en":41,"related-posts-qodo-raises-70m-to-verify-ai-written-code-en":45,"series-tools-1a2cf7c2-dc85-44cd-969e-91c9a585cf73":82},{"id":4,"title":5,"content":6,"summary":7,"source":8,"source_url":9,"author":10,"image_url":11,"keywords":12,"language":18,"translated_content":10,"views":19,"is_premium":20,"created_at":21,"updated_at":21,"cover_image":11,"published_at":22,"rewrite_status":23,"rewrite_error":10,"rewritten_from_id":24,"slug":25,"category":26,"related_article_id":27,"status":28,"google_indexed_at":29,"x_posted_at":10,"tweet_text":10,"title_rewritten_at":10,"title_original":10,"key_takeaways":10,"topic_cluster_id":10,"embedding":10,"is_canonical_seed":20},"1a2cf7c2-dc85-44cd-969e-91c9a585cf73","Qodo raises $70M to verify AI-written code","\u003Cp>AI \u003Ca href=\"\u002Fnews\u002Fai-coding-tool-prices-2026-free-vs-paid-en\">coding tool\u003C\u002Fa>s are pumping out billions of lines of code every month, but that speed has created a new problem: who checks the work? \u003Ca href=\"https:\u002F\u002Fqodo.ai\" target=\"_blank\" rel=\"noopener\">Qodo\u003C\u002Fa> thinks the answer is code verification, and it just raised $70 million to push that idea into enterprise workflows.\u003C\u002Fp>\u003Cp>The New York-based startup, founded in 2022 by Itamar Friedman, says the real bottleneck is no longer writing code. It is making sure AI-\u003Ca href=\"\u002Fnews\u002Fqodo-raises-70m-verify-ai-generated-code-en\">generated code\u003C\u002Fa> matches company rules, fits the rest of the system, and does not quietly introduce bugs that only show up after deployment.\u003C\u002Fp>\u003Cp>That pitch is arriving at exactly the right moment. Teams are adopting tools like \u003Ca href=\"https:\u002F\u002Fopenai.com\" target=\"_blank\" rel=\"noopener\">OpenAI\u003C\u002Fa>’s coding features and \u003Ca href=\"https:\u002F\u002Fwww.anthropic.com\u002Fclaude-code\" target=\"_blank\" rel=\"noopener\">Claude Code\u003C\u002Fa>, but faster output has not made review easier. It has made review more important.\u003C\u002Fp>\u003Ch2>Why Qodo thinks verification is the next bottleneck\u003C\u002Fh2>\u003Cp>Qodo’s core argument is simple: generating code and verifying code are different jobs. A model can draft a function quickly, but it does not automatically know an engineering team’s standards, historic tradeoffs, or the edge cases buried in a large codebase.\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775122218385-snre.png\" alt=\"Qodo raises $70M to verify AI-written code\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>Friedman’s background helps explain why he is making that bet. He previously co-founded Visualead and led the machine vision business at Alibaba after its acquisition of Visualead. Before that, he worked at Mellanox, later acquired by Nvidia, where he saw automated verification applied to hardware. That experience shaped his view that software generation and software verification need different systems.\u003C\u002Fp>\u003Cp>Qodo is building AI agents for \u003Ca href=\"\u002Fnews\u002Fintuit-qodo-ai-code-review-investor-angle-en\">code review\u003C\u002Fa>, testing, and governance. Instead of looking only at the lines that changed, the company says its system evaluates how a change affects the wider application, internal standards, and risk tolerance.\u003C\u002Fp>\u003Cul>\u003Cli>\u003Cstrong>Funding:\u003C\u002Fstrong> $70 million Series B\u003C\u002Fli>\u003Cli>\u003Cstrong>Total raised:\u003C\u002Fstrong> $120 million\u003C\u002Fli>\u003Cli>\u003Cstrong>Founded:\u003C\u002Fstrong> 2022\u003C\u002Fli>\u003Cli>\u003Cstrong>Headquarters:\u003C\u002Fstrong> New York\u003C\u002Fli>\u003Cli>\u003Cstrong>Lead investor:\u003C\u002Fstrong> Qumra Capital\u003C\u002Fli>\u003C\u002Ful>\u003Cp>The round included Maor Ventures, Phoenix Venture Partners, S Ventures, Square Peg, Susa Ventures, TLV Partners, Vine Ventures, Peter Welinder of OpenAI, and Clara Shih of Meta. That mix matters because it signals interest from both traditional enterprise investors and people who have spent years inside major AI companies.\u003C\u002Fp>\u003Cp>Qodo is also trying to solve a trust problem that is already visible in developer behavior. A recent survey cited by TechCrunch found that 95% of developers do not fully trust AI-generated code, while only 48% consistently review it before committing. That gap is where Qodo wants to live.\u003C\u002Fp>\u003Ch2>The company’s pitch is about context, not just detection\u003C\u002Fh2>\u003Cp>Most code review tools focus on what changed. Qodo says that is too narrow. A change that looks fine in isolation can still break a service if it clashes with a legacy module, an internal rule, or a pattern the team has already rejected.\u003C\u002Fp>\u003Cp>That is why Friedman keeps coming back to context. In his words, quality is subjective and depends on organizational standards, past decisions, and tribal knowledge. A reviewer who joins a company today does not know those things instantly, and neither does a generic model.\u003C\u002Fp>\u003Cblockquote>“Generating systems and verifying systems require very different approaches (different tools, different thinking).” — Itamar Friedman\u003C\u002Fblockquote>\u003Cp>That quote gets to the heart of Qodo’s strategy. The company is not trying to outdo foundation models at writing code. It is trying to become the layer that decides whether that code belongs in production.\u003C\u002Fp>\u003Cp>There is also a practical reason this approach matters. AI coding tools can produce a lot of acceptable-looking code very quickly, which means review teams can get buried in output. If verification tools add too much noise, engineers stop trusting them. If they miss important issues, they are useless. Qodo says its system is tuned to catch tricky logic bugs and cross-file issues without flooding developers with false alarms.\u003C\u002Fp>\u003Cul>\u003Cli>\u003Cstrong>Martian Code Review Bench score:\u003C\u002Fstrong> 64.3%\u003C\u002Fli>\u003Cli>\u003Cstrong>Lead over next competitor:\u003C\u002Fstrong> more than 10 points\u003C\u002Fli>\u003Cli>\u003Cstrong>Lead over Claude Code Review:\u003C\u002Fstrong> 25 points\u003C\u002Fli>\u003Cli>\u003Cstrong>Qodo 2.0:\u003C\u002Fstrong> multi-agent code review system launched recently\u003C\u002Fli>\u003C\u002Ful>\u003Cp>That benchmark result is the strongest public evidence behind the company’s claims so far. Benchmarks are never the whole story, but a double-digit lead over the nearest competitor is hard to ignore, especially in a category where false positives can kill adoption.\u003C\u002Fp>\u003Ch2>How Qodo compares with the rest of the market\u003C\u002Fh2>\u003Cp>Qodo is not the only company trying to make AI-generated code safer. But its positioning is different from the big model vendors and different from smaller code-quality startups that focus on narrow review tasks. Friedman says \u003Ca href=\"https:\u002F\u002Fopenai.com\" target=\"_blank\" rel=\"noopener\">OpenAI\u003C\u002Fa> and \u003Ca href=\"https:\u002F\u002Fwww.anthropic.com\" target=\"_blank\" rel=\"noopener\">Anthropic\u003C\u002Fa> are shaping the broader AI story, yet they are still focused on features rather than full enterprise verification systems.\u003C\u002Fp>\n\u003Cfigure class=\"my-6\">\u003Cimg src=\"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775122235090-li0i.png\" alt=\"Qodo raises $70M to verify AI-written code\" class=\"rounded-xl w-full\" loading=\"lazy\" \u002F>\u003C\u002Ffigure>\n\u003Cp>That distinction matters because enterprises do not buy code review in a vacuum. They want tools that understand their repositories, their policies, and their tolerance for risk. A generic assistant can comment on a pull request. A verification system has to fit into a larger software governance process.\u003C\u002Fp>\u003Cp>Qodo says it already works with Nvidia, Walmart, Red Hat, Intuit, Texas Instruments, Monday.com, and JFrog. Those are not pilot-project logos. They are the kind of names that suggest the company is getting real evaluation time inside serious engineering organizations.\u003C\u002Fp>\u003Cul>\u003Cli>\u003Cstrong>Enterprise customers named by Qodo:\u003C\u002Fstrong> Nvidia, Walmart, Red Hat, Intuit, Texas Instruments\u003C\u002Fli>\u003Cli>\u003Cstrong>High-growth software customers:\u003C\u002Fstrong> Monday.com, JFrog\u003C\u002Fli>\u003Cli>\u003Cstrong>Benchmark focus:\u003C\u002Fstrong> logic bugs and cross-file issues\u003C\u002Fli>\u003Cli>\u003Cstrong>Product direction:\u003C\u002Fstrong> multi-agent review plus org-specific quality rules\u003C\u002Fli>\u003C\u002Ful>\u003Cp>Compare that with the way most AI coding products are sold today. Many are optimized for developer speed, autocomplete, or task completion. Qodo is pushing into a slower, more expensive, and more defensible layer: deciding whether generated code is acceptable at all.\u003C\u002Fp>\u003Cp>That could be a smart bet. As more companies adopt AI coding assistants, the cost of a bad review rises. A single missed bug in a payment flow, an access-control check, or a data pipeline can wipe out the time saved by generating code faster.\u003C\u002Fp>\u003Ch2>What this funding round says about AI coding\u003C\u002Fh2>\u003Cp>Qodo’s raise is a signal that the market is maturing. The first wave of AI coding products made developers faster. The next wave will be judged by whether they make software safer, easier to audit, and simpler to govern.\u003C\u002Fp>\u003Cp>Friedman framed that shift as a move from stateless AI to stateful systems, or from intelligence to what he calls “artificial wisdom.” That phrase may sound a little theatrical, but the underlying point is solid: enterprise software needs memory, context, and rules, not just fluent text generation.\u003C\u002Fp>\u003Cp>For engineering leaders, the takeaway is immediate. If your team is adopting AI coding tools, review workflows need to change at the same time. Otherwise you get more code, more quickly, with the same human bottlenecks and a higher chance of missing something important.\u003C\u002Fp>\u003Cp>My read is that verification will become a budget line item in the same way observability and security scanners did. The companies that win this category will be the ones that can prove they reduce review time without lowering trust. Qodo has made a strong first move, but the next test is whether those benchmark wins translate into daily use inside large codebases.\u003C\u002Fp>\u003Cp>So the real question is not whether AI will keep writing more code. It will. The question is which tools become the default gatekeepers before that code lands in production, and Qodo just put $70 million behind its answer.\u003C\u002Fp>","Qodo raised $70M to tackle AI code quality. Its bet: verification, not generation, will decide which teams trust AI in production.","techcrunch.com","https:\u002F\u002Ftechcrunch.com\u002F2026\u002F03\u002F30\u002Fqodo-bets-on-code-verification-as-ai-coding-scales-raises-70m\u002F",null,"https:\u002F\u002Fxxdpdyhzhpamafnrdkyq.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Fcovers\u002Finline-1775122218385-snre.png",[13,14,15,16,17],"Qodo","AI code review","code verification","enterprise software","developer 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