AI Doesn’t Make You Faster. Discipline Does.
Let’s skip the usual “AI is neither magic nor hype” preamble. You’ve read that article a hundred times. It was probably written by AI.
Here’s what we’ve learned at Ryde Ventures: AI is a genuine game-changer. It compresses months into weeks. It lets small teams punch absurdly above their weight. The productivity gains are real.
But there’s a catch nobody likes to talk about:
AI makes sloppy teams ship faster and call it progress.
The New Hire You Never Onboard
Watch how most people use AI tools. They throw a vague request into a chat box, accept whatever comes back, skim the first paragraph, copy-paste it somewhere, and move on. When the output is mediocre, they blame the model.
Now imagine doing this with a new employee.
Day one, you hand them a laptop and say:
“Build me something good. You figure out what.”
No context about the company. No explanation of the customer. No clarity on what success looks like. No access to strategy or prior decisions. You’d never do that to a person. You’d onboard them. You’d give them the “why,” the constraints, the history, the examples. You’d set them up to win.
Yet people do the opposite with AI and then conclude: “AI isn’t that useful for our work.”
Here’s the thing: LLMs respond to context the way humans do. Give a smart new hire rich context, the why behind the what, the constraints, the tradeoffs, the vision, and they’ll produce remarkable work. Give them nothing, and you’ll get generic output that technically fulfills the brief and completely misses the point.
There’s a term floating around: context engineering. The idea that managing what information an AI sees is becoming its own discipline.
True.
But most discussions miss the deeper point: context isn’t only an engineering problem. It’s a cultural one.
You can build the most sophisticated pipeline in the world. If your team can’t be bothered to write down what you’re trying to achieve, you’ll still get garbage in, garbage out.
The Lazy Loop
We see the same pattern repeatedly with founders and teams:
1. Write a lazy prompt
2. Get a decent-but-not-great response
3. Don’t read it carefully (it’s long, you’re busy)
4. Miss the three things that need adjustment
5. Ship something 70% right
6. Decide AI “doesn’t really work that well”
7. Go back to doing things the slow way
8. Post about it online
The tragedy isn’t that the output was bad. It’s that excellent output was two or three iterations away. Iterations that never happened because nobody wanted to do the work.
This is the uncomfortable truth:
AI rewards the meticulous and punishes the sloppy.
The gap between “acceptable” and “exceptional” is still closed by human attention.
AI is a multiplier. But 10× zero is still zero.
How We Actually Work at Ryde
At Ryde Ventures, we treat context as a first-class citizen. Not as a slogan — as a practice.
Every venture we build starts with a living document: our vision and strategy. Not a dusty 50-page PDF made for investors and never opened again. A concise, evolving articulation of:
- what we’re building
- why it matters
- who it’s for
- what we believe
- what we won’t do
- how we make decisions
Every person in the organisation — human or AI — uses this as the starting point.
When we brief AI on a venture or a problem, we include:
- Strategic context: what we’re trying to achieve and why
- The customer: specific, not “SMBs” or “people who want to grow”
- Constraints: budget, timeline, values, non-negotiables
- What good looks like: examples, references, prior work
- What we’ve tried: so we don’t repeat rejected paths
It takes fifteen minutes. It saves fifteen hours.
And when someone prompts an AI without proper context, we call it out. Not because we’re pedantic. Because we’ve seen the difference: same person, same tool, same task — with context versus without — produces dramatically different results.
That’s not a tooling upgrade. That’s discipline.
The Real Unlock
The founders who get the most out of AI aren’t the ones chasing the newest models or building elaborate automation.
They’re the ones who do the basics, relentlessly:
- Write things down. Vision, strategy, customer insights, lessons learned — documented and accessible. Not in someone’s head. Not buried in Slack.
- Treat AI like a smart colleague. Would you send this brief to a new hire? If not, don’t send it to your model either.
- Read the output. Carefully. The whole thing. Then iterate. Yes, it’s long. That’s the job.
- Stay in the loop. AI proposes, humans decide. Every time.
This isn’t revolutionary. It’s the same discipline that has always separated enduring companies from forgettable ones. The difference now is that the disciplined will compound faster, and the sloppy will produce polished mediocrity at scale.
Which, if you think about it, might be worse than regular mediocrity — because now you can produce more of it, faster, with more confidence.
AI is an amplifier. It amplifies whatever you bring to it.
Bring rigor, get remarkable. Bring shortcuts, get output that looks finished and isn’t.
We know which one we’re optimizing for.
The question is: are you?
(We’re building Onoma to solve the context problem at the infrastructure level — but that’s a different post.)
