Over the past five years, Intuit went through a total cloud transformation—they closed the data centers, built out a modern SaaS development environment, and got cloud native with foundational building blocks like containers and Kubernetes. Now they are looking to continue transforming into an AI-driven organization that leverages the data they have to make their customers’ lives easier. Along the way, they realized that their internal systems have the same requirements to leverage the data they have for AI-driven insights.
Episode notesWadher notes that Intuit uses development velocity, not developer velocity. The thinking is that an engineering org should focus on shipping products and features faster, not making individual devs more productive.
No, the robots aren’t coming for your jobs. Wadher says their AI strategy relies on helping experts make better insights. The goal is to arm those experts, not replace them.
In terms of sheer volume, the AI/ML program at Intuit is massive. They make 58 billion ML predictions daily, enable 730 million AI-driven customer interactions every year, and maintain over two million personalized AI models.
Intuit’s not here to hoard secrets. They’ve outsourced their DevOps pipeline tool, Argo. They found that a lot of companies used it for AI and data pipelines, and have recently launched Numaproj, which open sources a lot of the tools and capabilities that they use internally.
Congrats to Lifeboat badge winner Bill Karwin for their answer to Understanding MySQL licensing.
An open-source development paradigm
Would you board a plane safety-tested by GenAI?
How to train your dream machine
OverflowAI and the holy grail of search
Spreading the gospel of Python
Between hyper-focus and burnout: Developing with ADHD
Reshaping the future of API platforms
The reverse mullett model of software engineering
Net neutrality is in; TikTok and noncompetes are out
Supporting the world’s most-used database engine through 2050
Is GenAI the next dot-com bubble?
Why configuration is so complicated
If everyone is building AI, why aren't more projects in production?
How do you evaluate an LLM? Try an LLM.
Diverting more backdoor disasters
Climbing the GenAI decision tree
Want to be a great software engineer? Don’t be a jerk.
What a year building AI has taught Stack Overflow
Are long context windows the end of RAG?
Will antitrust suits benefit developers?
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