Josh English

VP of Eng @ Chronicle Studios | ex-Google

[Josh English @JoshSeriesAI]

For the entire history of engineering, we designed organizations around code being expensive. Code is becoming cheap, and the org chart hasn’t caught up yet.

Most of the conversation is still about productivity: faster coding, more output, fewer bottlenecks.

The far more interesting question is how much more of the product one engineer can responsibly own, and what this means for teams, architecture, and management.

I explored what that might look like here: https://t.co/uid4JBfIBD

[Josh English @JoshSeriesAI]

@blader 😅

[Josh English @JoshSeriesAI]

@TimSweeneyEpic darn, wish i was in town for this one

[Josh English @JoshSeriesAI]

@VictorTaelin you don't seem to understand the math behind why optimization and approximation-theory scaled neural in the first place

the continuous-to-discrete gap is not an engineering detail

[Josh English @JoshSeriesAI]

@Attack @spawn i'm having lots of fun with spawn - https://t.co/VylsP2L4x6

[Josh English @JoshSeriesAI]

@JoshLu and people wonder why GDC is dying

[Josh English @JoshSeriesAI]

@TimSweeneyEpic @jack_mathews All it will accomplish is force more studios to lie about using AI

[Josh English @JoshSeriesAI]

@Devon_Eriksen_ The older you get, the more obvious it becomes that aligning incentives solves so many problems

[Josh English @JoshSeriesAI]

of course, i had to make a tailer

[Josh English @JoshSeriesAI]

having a blast working with @spawn - https://t.co/VylsP2L4x6

[Josh English @JoshSeriesAI]

having a blast working with @spawn - https://t.co/VylsP2L4x6

[Josh English @JoshSeriesAI]

@jsnnsa tbh i expected your agent to suck - i'm impressed with the sophisticated understanding of the engine (and what decisions mean downstream)

[Josh English @JoshSeriesAI]

@JNavok certainly an unenviable position having a large global org & facing AI transformation

[Josh English @JoshSeriesAI]

@Austen there's no problem that magical space lasers cannot solve

[Josh English @JoshSeriesAI]

@clduab11 I used Opus 5.5, GPT-6.1, Blender and @tripoai

[Josh English @JoshSeriesAI]

I wanted to see just how far I could push what I could build in under two hours on a Saturday.

So, naturally, I made a 3rd-person sci-fi shooter.

I started with a shooter physics prototype I'd already built, and turned it into EDEN//ZERO. Biomechanical enemies, assault rifles, missile launchers, plasma swords, motion-captured animation, and a ridiculous amount of visual polish for a Saturday morning.

All running in a browser.

It's still a prototype. There are plenty of rough edges, and making something playable is a very different problem from making a great game.

But here's what I find fascinating.

I've spent most of my career building products alongside incredibly talented engineers, artists, designers, and other specialists. I know how much work and coordination goes into bringing something like this together.

Now a single engineer can get surprisingly far in just a few hours.

And this isn't just about games.

We're watching the economics of software engineering and creative production change in real time. I'm still wrapping my head around just how far this goes.

Anyway, here's what happened when I spent a couple of hours having fun on Saturday morning.

[Josh English @JoshSeriesAI]

@clduab11 You're absolutely right, I didn't include the ~10 minutes I spent on this: https://t.co/WX9wKrsbkV

[Josh English @JoshSeriesAI]

spent ~10 upgrading my browser-based shooter. @tripoai generated the character & weapons, NVIDIA MotionBricks handles the animation, and the whole thing runs locally in the browser at 60 FPS

[Josh English @JoshSeriesAI]

@sundarpichai @antigravity nice one!

[Josh English @JoshSeriesAI]

@lulumeservey finding your own voice, and then using it is a good first step

[Josh English @JoshSeriesAI]

What is a product? A product is fundamentally a hypothesis about an audience.

A bet that someone, somewhere, will find what you've built valuable enough to spend their time, attention, or money on it.

I've spent decades building products, and we've gotten extraordinarily good at testing whether things work. Unit tests, integration tests, performance benchmarks, automated QA. Now we even have AI agents that can write the code, run the tests, find the bugs, and fix them.

We can verify that what we've built works exactly as intended.

But how do we know whether the original idea was any good?

You can build a technically flawless game that nobody enjoys playing. A beautiful film nobody wants to watch. An enterprise application that solves a problem nobody cares enough to pay for.

The engineering can be spectacular and the product can still be a complete failure.

For most of my career, building something was the expensive part. Engineering talent, infrastructure, production budgets, months or years of execution. The barriers were enormous.

AI is dismantling those barriers at an astonishing rate.

And that creates a fascinating problem: we're becoming extraordinarily good at making things nobody wants.

Our ability to produce things is growing exponentially. The amount of human attention available to consume them isn't.

Which brings us back to that original hypothesis.

Who actually wants this? Under what circumstances? Compared to what alternatives? At what price?

And perhaps most importantly, how do we know?

One of the biggest opportunities (and hardest challenges) in AI is applying the same rigor we've brought to engineering to understanding audiences.

Not simply asking people what they want, but forming hypotheses, running experiments, measuring what people actually respond to, and using those results to inform what we build next. And every experiment should make the next hypothesis a little better.

We've spent decades getting better at building things right.

Now we have an opportunity to get better at building the right things.

[Josh English @JoshSeriesAI]

@jsnnsa wait, what?