Learning Compression Factor

About

Source - [1]

OSE is predicting a bold, 50-100 learning compression factor. Ie - the factor of speed increase to gain any marketable skill - as measured by the fact that a learner gets paid from this work. We can take the case for OSE vs industry standard.

We simple take the time it take for the OSE apprentice to get to $100k earnings - vs the time it takes in the industry to get to $100k (comparable sectors - builders and General Contractors). $100k is the predicted earnings, and now it is time for data collection to determine how much faster an FBA apprentice takes to get there.

How to interpret this cleanly?

Actionable Items

• Measure LCF
• Measure coninuing learning - how any producer reduces time to professional grade plateau. What is the rate to attaining professiona grade? This means a person makes more per hour - they are incentivized to keep improving.
• This is reset every so often - if a radical shift converts the activity to zero marginal cost - that is great. Other work is done elsewhere - other constraints are negotiated for value. Value never ends, pay never ends.
• In other words - In an abundant, collaborative system, being replaced by a robot means you’ve succeeded—and now it’s time to build the next layer. [2]

The Future Builders Enterprise Track is not school. It is market exposure with a safety net.

Discussion

Joshua -

Related to management - we are getting serious here. December was a major success on attendance, though no go on Apprenticeship. So taking it to the next level in the Future Builders Enterprise Track:

• 6 months*cost $10k*Students start their own business, OSE buys complete house module kits from them at $10k each after materials, so in 6 months our peeps are starting at $100k/year. We can sustain 5-10 purchases per month as is, and would need to develop more marketing capacity to achieve 50/month. Now that is a couple a million dollar per month enterprise - around the corner.

This is serious learning, and we can formalize to the Learning Compression Factor of open source (ratio of time to learn in proprietary way / time to learn in open source framework), the type of shit you espouse so dearly. So I am getting rigorous on calculating and documenting.

https://chatgpt.com/share/696bfed2-cd40-8010-9100-2bd4978b401b

Aiming for 50 people in the program, starting May 1. Serious data collection, where we document time to learn, build times, etc. Same old same old, just getting rigorous and framing the Rapid Learning Facility as not an education venue, but a civilization grade production data engine towards the zero marginal cost society.

I was thinking this would make a great organizational theory paper. Does it sound compelling? I'm shaking this down in chatGPT, but initial considerations indicate LCF>>10, and i think we can propose a rigorous thesis and data analysis around this - based on the current ~1000 hour build time of a house with absolutely all of its systems including HVAC and PV modules.

Would you be interested in collaborating on a paper on this? We can provide the data engine - 50 Future Builders Enterprise Track students focusing on build time documentation and validation etc across the whole house. With extreme fire under their ass - because if they don't achieve at the predicted rate, they will not be making $100k/year until they do - so the incentive structure is clear as the outcome is financial independence.

Marcin

Quantification of an Integrated Human

So the thread [3] ended at ICI - an Integrated (Human) Capacity Index - an integrated human distributive capacity index, based on the Learning Compression Factor - which actually correlates with one's pay once enterprise competency is included, and looks like the following. This is entertaining, but ChatGPT suggests that it is actually useful to track this for any integrated human if we are talking about human evolution. Clearly this and 50 cents can get me a can of coke in a soda machine.

Synthesis

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