Using your analogy, imagine it's the year 2026. Two armies are fighting. One uses letter to communicate. One uses phones. Which army do you want to fight in?
I cycle 60 mins per day along the tow path in London on my Brompton, put it under my desk in the office, and then get the train back in the evening. No issues handling that distance.
If you don’t understand then you should invest some time learning microeconomics, marketing, and moats. Principles from (at least) those 3 areas are involved here.
To give 3 examples:
1. The marginal value of these products is in the mind of the individual buyer. No individual is buying both the AirPods Max 2 AND the MacBook Neo for personal use. You can’t compare marginal value across two different individuals.
2. The MacBook Neo has a different set of substitutable goods vs the AirPods Max 2. This affects margin. AirPods Max 2 buyers are likely heavily bought into the Apple ecosystem already.
3. With the Neo, Apple are in some sense subsidising entry into the Apple Ecosystem and ‘getting them young’. Wouldn’t surprise me if there’s zero or negative margin. With the AirPods Max 2 they are exploiting people who are already bought into the ecosystem. Margins will be high.
Backend Software Engineer (Python): Up to £150k + 2% depending on experience.
Optimal builds AI agents to control the world’s critical infrastructure - from factories, to datacenters, to farms.
We are backed by the Director of AI Research at Google DeepMind as well as early VC investors in SpaceX, Anduril, and Palantir.
We have built the world’s most advanced AI control system for high-tech greenhouses and have just signed out first customer contracts in North America and Europe having proven the performance of our AI across 3 seasons in our own demonstration greenhouse.
Backend Software Engineer (Python): Up to £150k + 2% depending on experience (reach out direct to [email protected])
Optimal builds AI agents to control the world’s critical infrastructure - from factories, to datacenters, to farms.
We are backed by the Director of AI Research at Google DeepMind as well as early VC investors in SpaceX, Anduril, and Palantir.
We have built the world’s most advanced AI control system for high-tech greenhouses and have just signed out first customer contracts in North America and Europe having proven the performance of our AI across 3 seasons in our own demonstration greenhouse.
Full-Stack Software Engineer (Python): Up to £150k + 2% depending on experience (reach out direct to [email protected])
Optimal builds AI agents to control the world’s critical infrastructure - from factories, to datacenters, to farms.
We are backed by the Director of AI Research at Google DeepMind as well as early VC investors in SpaceX, Anduril, and Palantir.
We have built the world’s most advanced AI control system for high-tech greenhouses and have just signed out first customer contracts in North America and Europe having proven the performance of our AI across 3 seasons in our own demonstration greenhouse.
Optimal builds AI agents to control the world’s critical infrastructure - from factories, to datacenters, to farms.
We are backed by the Director of AI Research at Google DeepMind as well as early VC investors in SpaceX, Anduril, and Palantir.
We have built the world’s most advanced AI control system for high-tech greenhouses and have just signed out first customer contracts in North America and Europe having proven the performance of our AI across 3 seasons in our own demonstration greenhouse.
I have a single sheet per account (current accounts, share accounts etc). I download csvs and append to the relevant sheet - usually once per month.
In each sheet I've added a column called 'tag'. And I just tag anything that I want to keep track of - which is a small percentage of transactions. Then I can filter transactions by that tag.
Whilst it was a nice idea in theory to book every transaction to an account in a chart of accounts, I found that I very rarely looked at the PnL. And so it didn't justify the time involved in booking each transaction.
This is an obviously poor policy.