SWE/SRE Engineering Manager 15 years

@hatchman76

Almost always in person

~100

Recruiter Screen

SRE Mindset

Management & Leadership

15 hours

Apache Web Server Diagnosis

Code challenge

Code refactor

System Design

Management and Leadership

Lunch!

2 - 3 days

Please share your ideas in Slack!

We don't just hire for raw tech skills

We love to simplify complexity

5 Whys

Even though we know we can grossly generalize

But what if something could reduce this reliance?

Remember me?

I wrote a book once in 1911

It has 82% likes on Goodreads!

Separate Thinking, From Doing

...but only if you don't care about worker engagement, happiness and innovation!

And haven't been for decades

"relax humans, I got this!"

the speed at which we generate beliefs,

the plausibility of those beliefs, and

the distance between apparent certainty and actual understanding

It removes difficulty right at the point where struggle would deepen understanding.

It takes the "happy path", avoids rich corrective feedback, just fabricates fluent answers.

It lets novices skip building mental models and jump straight to polished artifacts.

"...GPT Base helped students solve about 48% more practice problems correctly, but when the exam came without AI, those same students scored 17% worse than the kids who never used AI at all..."

https://knowledge.wharton.upenn.edu/article/without-guardrails-generative-ai-can-harm-education

Hoffman et al. (2014). Accelerated Expertise.

Novices vs experts need different conditions:

What helps experts posessing learned causal models, will hurt novices who still need to build mental models.

Desirable difficulty:

Training that's too easy → feels good, looks productive, but produces shallow learning.

Scenario-based practice in messy, real contexts:

You need exposure to realistic, varied situations and feedback, not just perfectly guided walkthroughs.

Experts seek out corrective feedback:

They want to be told where they're wrong; they don't want a tool that just makes them look right.

Understanding comes through trial and error, adaptation at the edge, testing

This is the fundamental mindset of good SREs

This is the capability we hire for and must cultivate on the job

And becomes the Operator?

SREs?

or....

Understand interactions between components, build awareness of unintended consequences

Adapt to dynamic conditions, rebuild mental models

Think critically, make trade-offs under pressure

But as an Operator it threatens to enshittify your systems

The ability to learn and build expertise in these system is critical to their success and safety

AI must be our partner, not a replacement