Cisco Gave One Engineer Unlimited AI Tokens to Rewrite 98%

Cisco president Jeetu Patel found Padmanabhan Krishnamurthy five levels below him in the org chart during a visit to the company's San Francisco office. Krishnamurthy is 27, the former CEO of translation startup EzDubs (acquired by Cisco in 2025), and was — in Patel's words — "buried away doing something unstrategic." Patel pulled him out and gave him a mandate: take a two-person team, unlimited AI tokens, and rewrite 98% of Cisco's legacy codebase within 24 months. The instruction was simple: "go nuts."

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Read the original Fortune article →

The story hit Fortune on October 30, 2026, and the internet split cleanly down the middle. Hacker News gave it 282 points and 330 comments, mostly bullish — commenters reported their own 50,000-line migrations finishing in a week. Reddit's r/technology gave it 12,933 upvotes and 1,895 comments, mostly skeptical — a "PR stunt" that conveniently eliminates the need for entry-level engineers.

That split is the story. Not whether coding agents can translate syntax — they can, and have been doing it reliably for months. The real question is whether a $429 billion enterprise can reorganize itself to accept, review, test, and trust millions of lines of AI-generated code. That is an organizational problem wearing an engineering costume.

ℹ️ The Cisco Experiment at a Glance

• Engineer: Padmanabhan Krishnamurthy, 27, ex-CEO of EzDubs
• Mandate: Rewrite 98% of Cisco's legacy codebase
• Timeline: 4-week proof of concept, 24-month full execution
• Team size: 2 people
• Resources: Unlimited AI tokens
• Budget context: Cisco is a $429B company with 85% of engineers already using AI tools

The Case For: Language Translation Is AI's Sweet Spot

The HN thread reads like a support group for engineers who have already done what Cisco is proposing. The top comment is concrete:

"We just recently used Claude Code to port an old ColdFusion site to Java Spring and it went amazingly well. It's around 50K lines of Java, one of our engineers did it in his spare time over about a week, using the $200/mo Claude plan."

View the full HN discussion →

This is not an isolated anecdote. The second-most-upvoted comment makes the structural argument: "Translating from one language/platform to another is something AI is very very good at, so this sounds like a good use of it, bonus points if they create a comprehensive test suite first."

The logic holds. Code translation is a bounded problem with well-defined inputs and outputs. Unlike open-ended feature development, you know what the code is supposed to do — it is already doing it. The agent's job is to preserve behavior while changing syntax and architecture. This is closer to compilation than creativity.

Nick Hodges, the former Delphi product manager, made this case explicitly in an InfoWorld opinion piece two weeks before the Cisco story broke. He argues that Joel Spolsky's famous 2000 warning against rewrites — that legacy code holds hard-won knowledge, and rewrites discard it — no longer applies. Coding agents can read the spaghetti, trace the tangled procedures, and carry the edge-case knowledge forward. His estimate: agents could handle "about 98% of a modernization, finished in a person-month or two."

Read the InfoWorld article →

Cisco's own track record supports the bullish read. Patel told the Financial Times that one product is now written entirely by AI, with no human-written lines. The company has saved over 1,500 engineering hours per month using OpenAI's Codex. And he predicts 70% of Cisco's product portfolio will be fully AI-generated by the end of 2027.

The Case Against: Organizational Antibodies Kill More Rewrites Than Bad Code

Reddit saw the same Fortune article and reached the opposite conclusion. The r/technology thread's dominant read: this is a job-elimination narrative dressed in innovation language.

View the Reddit discussion →

The skeptics are not wrong to be suspicious. The Fortune article opens by noting that 40% of UK employers plan to hire fewer graduates because AI can do the work more cheaply. Patel's language reinforces this — he wants to hire Gen Z engineers specifically because they are "native" to AI tools and will "challenge the status quo." One cynical reading: a 27-year-old with unlimited tokens is cheaper than 200 senior engineers with institutional knowledge.

The same day, a 466-point HN thread argued that AI is not replacing SREs — it is deskilling them. The concern is not that AI cannot write code, but that organizations will mistake AI output for verified output and erode the engineering expertise needed to catch what the agent gets wrong.

View the HN thread on AI deskilling →

But the deeper objection is not about labor politics. It is about the organizational machinery required to absorb a rewrite.

Joel Spolsky's 2000 essay was not really about code quality. It was about the invisible specifications embedded in a running system. Every bug fix is a learned lesson. Every edge case is a customer conversation. Every weird conditional is a compliance requirement that nobody documented. Rewriting the code does not just risk losing those lessons — it requires the organization to re-learn them, usually in production.

⚠️ The Contrarian Take

Cisco's "98% rewrite" may be the right engineering decision and still fail as an organizational one. The bottleneck is not code generation — it is code review, testing, and institutional trust. A two-person team generating millions of lines of code creates a verification crisis: who reviews it? Who signs off that the edge cases survived translation? A 50K-line ColdFusion port done "in spare time" is a different beast from a fleet-wide rewrite of a $429B company's production systems.

This is not theoretical. Spotify's Honk agent has merged over 1,500 AI-generated pull requests and reports 60-90% time savings on migrations. But Spotify's own post-mortem is revealing: the success created a new bottleneck in PR review. At QCon London 2026, the team reported that what used to take three months to hit 1,000 merged PRs now takes ten days — but the review queue exploded.

Read the Spotify Engineering post →

200OK Solutions tested five AI coding agents on a legacy codebase and found that every single agent made at least one change that would have caused a production issue if merged without review. Claude Code scored high on reasoning but weak on undocumented business rules. This is exactly the failure mode Spolsky predicted — the knowledge that lives in the code but not in any spec.

The Evidence From the Field

The Cisco experiment does not exist in a vacuum. A pattern is emerging across enterprise engineering:

Cisco's own numbers: 85% of engineers already use AI tools daily. One product shipped with 100% AI-generated code. Over 1,500 engineering hours saved monthly via Codex. The 98% rewrite target builds on proven tooling, not speculative capability.

Spotify's Honk: 1,500+ PRs merged, 60-90% time savings on dataset migrations. The background coding agent now handles language modernization (Java records), breaking-change upgrades, UI component migrations, and config changes. But it took a purpose-built fleet orchestration system and custom review tooling to make it work.

Meanwhile, another HN post about "building an AI-native engineering org" was called out for vanity metrics — LOC, tokens, releases — with no revenue data. The community consensus: output metrics without quality gates are marketing.

View the HN thread on AI-native engineering →

The Sonar reality check: Sonar's 2026 research shows that LOC output is up 2,450% with AI agents, but actual releases only increased 30%. The productivity multiplier on raw output does not translate linearly to shipped product. And 96% of developers say they do not fully trust AI-generated code — which means every line needs review. The 10x PRs, 1x reviewers problem is now the industry's central bottleneck.

IBM Technology's warning: IBM's recent analysis of AI exposing legacy code makes a complementary point: AI is forcing teams back into abandoned legacy systems before hidden dependency risks bite. The push to rewrite is not just about modernization — it is about risk management for code nobody has touched in years.

The $30 Billion Question: Who Owns the Risk?

The legacy code modernization market is now estimated at $30 billion and growing. Anthropic has committed $100 million to a partner network focused on code modernization. IBM's watsonx Code Assistant for Z converts COBOL to Java. GitHub Copilot has launched a dedicated migration workflow. Morgan Stanley built an in-house tool because commercial solutions "lacked expertise in company-specific legacy languages."

The common pattern: every enterprise that has successfully used agents for migration built custom tooling around the agent, not the agent alone. Spotify built Honk and Fleet Management. Morgan Stanley built a proprietary translation layer. The agent is necessary but not sufficient — the harness is the moat.

This is what makes Cisco's experiment genuinely interesting. A two-person team with unlimited tokens is not a production organization. It is a proof of concept. The four-week trial will show whether AI can translate Cisco's legacy code. The 24-month execution will show whether Cisco's organization can absorb the output.

ℹ️ What the Legacy Rewrite Market Looks Like in 2026

• Market size: $30B+ for legacy code modernization
• Active vendors: Anthropic ($100M partner fund), IBM (watsonx Code Assistant), GitHub (Copilot migration CLI), OpenAI (Codex)
• Enterprise evidence: Spotify (1,500+ PRs), Cisco (product shipped 100% AI), Morgan Stanley (custom tooling)
• Key finding: Every successful migration at scale used custom harness tooling, not raw agent output
• Failure rate: 60% of enterprise AI agent pilots fail to scale past pilot stage

What This Means for Teams Building with Agents

If you are an engineering leader evaluating coding agents for legacy migration, the Cisco story offers three lessons:

1. Start with translation, not creation. The HN commenters are right — language-to-language migration is AI's sweet spot. It is bounded, testable, and does not require the agent to invent new architecture. Your first agent project should be a code translation, not a greenfield build.

2. Build the review infrastructure first. Spotify learned this the hard way: the bottleneck moves from code generation to code review faster than you expect. Before you give an agent unlimited tokens, build the quality gates that will process its output. Automated test generation, diff review tooling, and merge approval workflows are the prerequisite, not the afterthought.

3. Measure organizational absorption, not engineering output. The 2,450% increase in LOC output is meaningless if releases only go up 30%. The metric that matters is not "how much code did the agent write" but "how much code did the organization ship, verify, and trust." Track merged-and-deployed, not generated.

The Cisco experiment will be a fascinating case study over the next 24 months. If Krishnamurthy's two-person team actually rewrites 98% of a $429B company's legacy code, it will be the most dramatic demonstration of coding agent ROI in enterprise history. If it stalls — and the Reddit skeptics are betting it will — it will confirm that the agent was never the bottleneck. The organization was.

Either way, the debate is settled on one point: legacy code migration is now the default first use case for enterprise coding agents. The question is no longer "can AI rewrite code?" It is "can your company handle it when it does?"

Originally published at AgentConn