Do Your Engineers Know How to Leverage AI?¶
This past spring, two senior engineers at different companies received the same challenge from their CEOs: "We need to move faster. Use AI to get there."
Both were talented, experienced engineers leading small teams. But six months later, one team had grown from 5 to 13 people and was still struggling to meet deadlines. The other stayed the same size but was shipping twice as fast.
What Made The Difference
The difference wasn't talent or effort. One engineer treats AI like a better search engine. The other treats AI like an entire engineering team working in concert.
The Reality Most Engineers Face
Your team is already swamped. They don't have time to follow every AI launch or watch dozens of tutorials. When they do try AI tools, it's between meetings or after hours when they're exhausted.
The AI landscape moves faster than anyone can keep up with. The "best practices" you read about are often written by people who have time to experiment, not engineers who need to ship production code.
The Knowledge Gap That's Widening
The engineers winning right now learned the complete workflow—the orchestration that turns AI from a productivity hack into a fundamental shift in how software gets built.
Most engineers are trying to piece together knowledge from scattered content while their actual work piles up. Companies are sending entire teams to get concentrated, practical knowledge in three days instead of months of content consumption.
A Tale of Two Workflows
Both engineers started with 90-minute product planning meetings.
David took notes in Notion, then copy-pasted portions into ChatGPT: "Help me plan this feature." ChatGPT responded in isolation, without any context about their actual codebase or existing architecture.
Elena had Granola transcription running. Fifteen minutes after the meeting ended, she had fed the complete transcript into OpenAI Codex—directly in their codebase, and used Wispr Flow to dictate any lingering context that was missing. The agent simultaneously read their source files, understood their existing architecture, and generated:
- A comprehensive plan.md that aligned with their current system, files to edit, and tests to write, and documentation to reference and update
- A detailed plan.md file that considered both edge cases and existing code patterns
By the time David was wrestling with generic ChatGPT responses with copy and paste, Elena's team had a plan that was already integrated with their actual codebase, reviewed by their team, and ready to to start agentically coding up.
The New Reality of Software Development
Right now, I am looking at metrics from companies that have figured this out. The numbers are striking:
- Shopify: Managing 5,000+ repositories with 30% fewer engineers than 2 years ago
- Coinbase: 40% of daily code is AI-generated
- Y Combinator startups: Teams of 3 shipping what used to require teams of 15
The difference isn't in having AI tools. Everyone has ChatGPT. Everyone can get Cursor. Everyone can open up 15 coding agents in 15 separate terminal windows and have them 'work' in parallel.
But if you're an engineering leader, you've learned that AI product demos rarely translate to production reality. So why would coding agent demos be any different? Those flashy videos of parallel agents aren't how real software gets built at scale.
The difference is understanding that AI has fundamentally changed the entire workflow of software development—not just the tools you use, but how you orchestrate them into actual production systems.
The Workflow That Changes Everything
The modern AI engineering workflow looks like this:
Each step precisely feeds the next. It's not about "prompt engineering." It's about exploring, planning, and executing.
When Elena's team ships a feature now, they also ship:
- Comprehensive tests (because AI makes them easier to write)
- Load testing suites (built in an afternoon instead of never)
- CLI tools for operations (tiny utilities that wouldn't have been worth the time before)
- Documentation that's actually current (generated from the code)
Knowledge Is Power
You see startups with tiny teams shipping impossibly fast. You hear about companies doing more with less.
It's not hype. It's knowledge—specific, practical knowledge about how to transform your engineering workflow with AI. Your engineers need to learn the complete workflow that turns AI from a toy into a transformation.
An Opportunity to Lead
Put our statements to the proof by sending your key engineers to the AI Coding Accelerator this October. For three days, they'll learn from engineers who've actually done this transformation at scale.
- Day 1: The complete workflow—from meeting to production
- Day 2: Guest Speakers from the people building these tools (Linear, Cursor, Sourcegraph, OpenAI, Cognition)
- Day 3: An afternoon hackathon with your actual code and challenges, and a whole day of office hours and live debugging.
The investment is $600 per engineer for our October cohort (rising to $1,200 afterward).
If your engineers become even 1% more efficient, you've paid for the training in weeks. But we're not talking about 1% gains—look at the numbers: Shopify managing with 30% fewer engineers, Y Combinator startups with teams of 3 shipping what used to require teams of 15. That's like getting 5x the output without the hiring costs, onboarding time, or management overhead.
What's included:
- 3 days of hands-on training with the complete AI toolchain
- Direct access to the teams building Claude Code, Linear, Cursor, Devin, Codex, and Sourcegraph
- 6-month Windsurf trial + 1 month Devin Team access
- $50/person credit from AMP
- Lifetime access to materials and private Discord community
- Most importantly: The knowledge of how to orchestrate these tools into a multiplier
A Guarantee of Value
If your team isn't leveraging AI workflows to ship faster by the end of the quarter, we'll refund your investment in full. We're that confident your engineers will transform how they build software.
About Those Two Engineers
David and Elena started in the same place—talented engineers asked to leverage AI. What made their paths diverge?
Practical knowledge. Proven workflows. No flashy demos—just systems that ship production code.
The Competitive Truth
Six months from now, the gap between teams that understand AI workflows and teams that don't will be insurmountable.
Every week your team spends copy-pasting into ChatGPT is a week your competitors are pulling further ahead. Three days of learning creates months of competitive advantage.
Reserve Your Spots
October 15-17, 2025¶
Sincerely,
Jason Liu & Vignesh Mohankumar Creators of the AI Coding Accelerator
P.S. — Brian Armstrong fired engineers who wouldn't adapt to AI. That's one approach. We prefer to give engineers the knowledge they need to thrive. The choice—and the opportunity—is yours.