Nobody I've ever met would expect a breakthrough from a 20 something year old no matter how much of a genius they are. Communicating a breakthrough requires time, effort, and credibility to begin with, which nobody has at that age.

Your 30's are when you can start to really do great things. And then depending on the field you can kind of just keep going as long as you have the energy for it. But lots of people begin to wear out into their 40's (for lots of different reasons).

In terms of great breakthroughs. If you haven't had your great idea by 40. It's probably increasingly unlikely that you'll have one later in life (but not impossible). Not everyone needs to have a paradigm changing idea to have a successful career though.

If it's not costing thousands of dollars, why would I hire a software engineer to build this for me.

Mucking around with cmake adds enough friction that everyone can take a beat for thoughtful decision-making.

It's probably more accurate to use a software analogy about performance metrics. We measure random request spikes now and again that strain the system. It's probably fine, but later on down the line, something could change that results in an outage during one of these spikes. Do we proactively fix the problem even if no change is expected? Or do we wait till there is definitely a problem before taking action?

Maybe the right example is the role of tractors in agriculture. Prior to tractors you had lots of people do the work, or maybe animals. But tractors and engines eliminate a whole class of labor. You could still till a field by hand or with a horse if you want, but it's probably not commercially viable.

Open-source seems to be fragmented into three groups now. Large enterprise open-source like Kubernetes or OpenStack where the license seems more like a legal agreement amongst vendors to not sue each other. Legacy open-source projects that are getting by on brand recognition and sheer willpower. And a whole bunch of noise from people who are looking to leverage open-source into a job of some sort.

I'm not sure what the solution is...

If what you're doing is proprietary, or even a little bit novel. There is a really good chance that AI will screw it up. After all, how can it possibly know how to solve a problem it has never seen before?

Moore's law was supposed to make it simpler and cheaper to do more computationally expensive tasks. But in the meantime, everyone kept inflating the difficulty of a task faster than Moore could keep up.

I think some of this is because of the incredible amounts of capital that startups seem to be able to acquire. If startups had to demonstrate profitability before they were given any money to scale, the story would be very different I think.

I like the Root-Bernstein narrative more. That in the monotonous execution of routine experiments for something unrelated an unusual observation 'forced' them to discover penicillins antibacterial properties.

Not an accidental discovery by good fortune in a serendipitous sense. An accidental discovery of a brute force exhaustive search. The narrative of we spent months meticulously examining hundreds of samples is less romantic, but is one that supports the importance of funding scientific inquiry.

We won't make progress by hoping people leave culture plates out on window sills. We make progress when we fund meticulous exhaustive efforts of discovery.

It turns out that people are more likely to think a model is good when it kisses their ass than if it has a terrible personality. This is arguably a design flaw of the human brain.

My own thinking is that the big competitive advantage that big tech firms have over small ones is the power to mobilize very large numbers of developers onto a project.

Large projects that don't depend on a small core group of irreplaceable competent individuals are more repeatable for a business. So it makes sense to focus on making the repeatable processes more likely to succeed than simply hope that you happen to have the right team assembled for the job.

Assuming that any of your engineers are above or below average hurts the ability for the business to plan.

The main reason for being unwilling to merge AI code is going to be that it sets a precedent that AI code is acceptable. Suddenly, maintainers need to be able to make judgement calls on a case-by-case basis of what constitutes an acceptable AI contribution, and AI is going to be able to generate far more slop than people will ever have the time to review and agree upon.