---
title: Tech is broken and AI won't fix it
weight: 5
tags:
 - rejekts
---

{{% button href="https://www.youtube.com/watch?v=CAPtQnH4rPY" style="warning" icon="video" %}}Watch talk on YouTube{{% /button %}}
<!-- {{% button href="https://docs.google.com/presentation/d/1nEK0CVC_yQgIDqwsdh-PRihB6dc9RyT-" style="tip" icon="person-chalkboard" %}}Slides{{% /button %}} -->

## Recruitment & Staffing

> The background for this talk is a very bad experience in this sector

- "We want someone opinionated" -> No you don't

### We don't know how to recruit engineers

- How many rounds of interviews?
- Technical tests?
    - Not allways show your real worlds skills
    - Many different kinds that usually don't fit (telling a junior)
    - Other fields don't do this
- Is there an accurate feedback loop?
    - The anwer to the qustion "how was the interview process" usually depends if there is an result and the kind of result
- Many just try to follow fang
    - Plot twist you are not fang, you are a small company
    - Mostly tests resilience and not really technical skills

### We're not very good at training for the future

- Many companies "only recruit senior engineers"
- Junior engineers are sometimes viewed as a cost center
- But they are important:
    - Force seniors to explain stuff better
    - Force collobaration
    - Are a bit like the usual clueless customer

## Teamwork

- Depending on the team size and skillset the requirements for leadership change (Lead Engineer vs Engineering Manager)
- Don't hang up on stuff like "this is our team's senior/junior engineer" - everyone servers a role that changes depending on context (everyone is )

### Agile is broken

- The core of agile is about 
    - Individuals&Interaction > Process&Tools
    - Working Software > Documentation
    - Customer collaboration > Contract negotiation
    - Responing to Change > Following a plan
- Agile never planned for all of the meetings
- If you have to have standups: Keep them short with rules

## Bias

- Everyone has biases and uncontious biases
- Example: The doctor test "I went to the doctor" -> Most people assume a man
- AI results in the same stereotype "Draw a doctor" -> While man with glasses
- Reality usually contradicts these biases

## Coding

- In the old days we copied from stackoverflow, now we let ai do the copying
- If GenAI uses LLM it bases everything on past code, will we reach a point where noting is new?

## What can we do

- Be an empathetic human (by gut feel or just by asking others)
- Develop engineers
- Never say something is shit
- Treat AI as an unreliable colleague (never assume it's allways right or wrong)
