> Average pay for manufacturing engineers is about $78k

This is more on par with pay for (top) programmers or engineers that I know of. In South Africa, actuaries get paid much more than programmers. From what I have seen on HN, US based programmers (in top-ish companies) get paid actuarial salaries. As a mathematician, I have a lot to say about actuaries, but alas, that is for another day.

Maybe someone more knowledgeable can clarify this. Median pay at tech companies is sometimes > $100k annually. In my view as a not-a-US-citizen, that is a lot of money.

When someone in the US says they make $100,000 per year, that is usually exclusive of any tax paid by the company. It is also exclusive of other benefits paid, with health insurance commonly being $10-20k paid by the company, if provided. The employee is responsible for their own income tax.

There’s confusion because in the gig/entrepreneur/consultant world, people will say they “make” $200,000 in business income. But then they bear taxes and benefits out of that amount. So you may “make” more, but do worse than someone who is an employee of a bigger company. That is why it takes a substantial increase in income in the US to justify leaving a stable employer.

When you're competing on the output of developers and the difference between first and second is Facebook and MySpace, get the best you can afford and pay them. You're spending VC money and need the best output as fast as you can get it. Good enough will kill you.

As a young South African, this reality is something of a let down. In the US, it's like you have a country with (if you normalise to mean) 30 cities the size of JHB economically. The access to cutting edge tech jobs is just incomparable. The upside is that if you do break through, your relative success is greater. In the US, someone else is already trying your idea.

But, to not sound like a completely grumpy cynic, I think they are intelligent and efficient; moreover, these days people have multiple jobs or facets and the critism is more towards the idea of an actuary than the individuals who are actuaries. I guess we are all in competition and actuaries and CAs are maybe just more honest about competing for one's income.

I am actually interested in actuarial research, FWIW.

In the middle of architecting the systems for a new pack line myself.

It is pretty safe to say that economies of scale has gotten us to where we are now with batteries. But that does not necessarily mean the trajectory will continue.

To put some numbers on it, imagine you start off delivering one package by hand a week, literally by hiring someone to drive across the country. This might cost $2000 a week in fuel, salary, depreciation etc.

Now say we're delivering a billion packages a week. The rule quoted would put it at $4 per package.

There's wiggle room to argue each end, but considering it's a rule of thumb operating over nine orders of magnitude, that it's not outright nonsensical is actually pretty good.

I agree it's not some iron law, and it's important to know when and where it can be applied.

[1]https://en.wikipedia.org/wiki/Jevons_paradox

So this applies to software systems as well. We had a system where, every time we optimized the underlying database, it became slower . This puzzled us for a while. Turns out that people were used to the slowness. When they found out that the system became faster, they actually started entering more transactions. As more people noticed, the system got overwhelmed again. Until it reached a point where it could finally handle the load.

The variable we failed to see is that people were leaving work earlier now. Even though the system's metrics appeared to degrade, that was a metric that markedly improved. They used to work longer hours to get all their work done.

One thing I've noticed is that the amount of time I have to wait when performing operations has remained more or constant through the past few decades, even as processor instruction retirement rates skyrocketed and memory/storage/network latencies plummeted. I often find myself trying to pull up a site to do something like track a project or buy a ticket, only to be waiting for 5 to 10 seconds before I am able to read the information and/or perform the operation I was there for.

I always ask myself, "Why on earth am I having to wait at all for this?" I assume it's the same concept of "induced demand" for roadways. Under that theory, in densely-populated areas, traffic will always be as bad as it is, regardless of the number of lanes. Add more lanes, and commute times drop. Then people who didn't tolerate the longer commute times before jump in and start commuting, increasing the load on the roads. This quickly raises the wait time to just below the previous wait time, with all the people in the "margin of toleration" who weren't commuting before now commuting.

In the case of the systems at my company for project tracking, I just assume that people build systems on dependencies in a way that trades latency with the convenience of using a particular API/service with an SLO. The system with the SLO costs money to maintain, and so they have a quota system in place based on priority. The project tracking software comes along and says, "Well, we could write the data into a Prostgres instance with a 10 millisecond response time, but then we've got to worry about backup/restore, availability, and all that stuff. Or we can be the lowest priority traffic for this nifty distributed storage backend that has a support team and SLO with a 7 second response time." The seven seconds of each person's life every time they use the tool is an externality, and it's something few people are ever going to complain about. So, 7 seconds it is.

I'm resigned to waiting an average of 5 or so seconds for anything I do on a computer for the rest of my life, no matter how cheap cycles, storage, or bandwidth get.

E. g. more lanes translates to higher automaker profits at expense of less overall population happiness.

Faster computers translate to higher overall rate of software use at expense of more overall time spent waiting

Ultimately though both Economy of Scale and Jevons Paradox reaches Law of Diminishing returns. Adding more capacity to network or faster DB queries will no longer yield any meaningful output.

Cell phone battery life has stayed mostly stable, even as battery capacities have gone up and processor energy/instruction has gone down. This is because mobile device performance is mostly power-constrained: manufacturers have a target of about a day of battery life as the minimum consumers will put up with, and whenever more energy is available they'll turn up performance or add features until battery life goes back down to that minimum.

CPUs, RAM, Energy and Energy storage have each reached the economy of scale point where innovation is heavily rewarded.

Single core, static dram, Oil, and lithium batteries should each fail at some point soon.

As for resource constraints, don't forget substitution.

https://en.wikipedia.org/wiki/Swanson%27s_law

That may be a feature. Otherwise you get a Raspberry Pi, with enough attack surface to drive a semi through, to control a pressure regulator.

In a bit of a twist, worth noting that Exxon Research & Engineering was involved in the invention of the rechargeable lithium ion battery [2]. Quoting from [3], "Thus Stanley Whittingham invented the first rechargeable LIB, patented in 1977 and assigned to Exxon."

[1] https://ir.exxonmobil.com/static-files/37b7e1b7-620b-4248-a3...

[2] https://patents.justia.com/patent/4009052

[3] https://www.currentscience.ac.in/Volumes/117/09/1416.pdf

Disclosure: I worked for ExxonMobil from 2012-2017 but don't have any remaining ties.

There are technologies that are irrelevant for cars or phone (which I do think attract most of the battery R&D) that would be a good fit for a battery the size of a building, designed to store the electricity for a whole city during the night/low wind period.

Government can fund the private R&D easily by, for instance, mandate that renewable sources installed on the grid must provide a baseline 24/24.

But public research is nothing to sneeze at. It brought us the lithium-ion battery (Goodenough was working at the U of Texas when inventing it). Private R&D is good at incremental improvements and solving deployment problems. Public research at funding radically new tech. They work well together.

Tesla is likely just an example of a general trend. Whether it's big leaps towards "fully automated" manufacturing or gradual, cheap labour is not as important. It's increasingly a "talent" paced industry.

Manufacturing is becoming less la