Drafted: 2026-04-10
The previous brainstorms (ai-value-p2p-protocol-brainstorm.md and future-of-work-ai-blockchain-p2p-brainstorm.md) explored who captures value when AI does the work, and how a P2P protocol could route that value back to contributing humans.
This note addresses a question that comes before value distribution: who gets to use AI at all?
Drafted: 2026-03-25
This note captures an evolving thesis about the future of work in the AI era, and extends it into a concrete startup brainstorm at the intersection of AI, blockchain, continuous micro-work, micro-payments, and peer-to-peer value sharing.
The main question is not only whether AI can do most intelligent tasks. The deeper question is: who captures the value when AI does the work, and how can humans still benefit?
| from pathlib import Path | |
| from bitcoin.core import CBlock | |
| import pbk | |
| import concurrent.futures | |
| import threading | |
| # ChainstateManager is currently not threadsafe so we need to wrap it | |
| class ThreadSafeChainstateManager: | |
| def __init__(self, chainman): | |
| self.chainman = chainman |
| #!/bin/bash | |
| COMMIT_HASH=${1:-"v0.1.0a2"} | |
| # Install dependencies | |
| apt-get update | |
| apt-get install -y libboost-dev | |
| # Install py-bitcoinkernel from a specific commit | |
| pip install git+https://github.com/stickies-v/py-bitcoinkernel@${COMMIT_HASH} |
| ## Data set in https://archive.ics.uci.edu/ml/datasets/bank+marketing does not contain timestamp. | |
| ## We can "deduce" them from the ordedr of records and use month and day_of_week columns to create a full date | |
| ## We know the first record was from 05-05-2008 | |
| import pandas as pd | |
| import numpy as np | |
| import datetime as dt | |
| import sys | |
| # read records from May 2008 to November 2010 |
| xgb.plot_tree(booster, num_trees=1, rankdir='LR') | |
| fig = matplotlib.pyplot.gcf() | |
| fig.set_size_inches(80, 20) | |
| fig.savefig('tree.png') |
| import numpy as np | |
| import pandas as pd | |
| import xgboost as xgb | |
| from sklearn.model_selection import train_test_split | |
| from sklearn.datasets import make_regression | |
| import lime.lime_tabular | |
| ### make data |
| # coding: utf-8 | |
| # In[1]: | |
| ### using XGBoost model with SHAP | |
| import numpy as np | |
| import pandas as pd | |
| import xgboost as xgb |
| sudo apt-key adv --keyserver hkp://p80.pool.sks-keyservers.net:80 --recv-keys 58118E89F3A912897C070ADBF76221572C52609D | |
| sudo echo "deb https://apt.dockerproject.org/repo ubuntu-trusty main" >> /etc/apt/sources.list.d/docker.list | |
| sudo apt-get update | |
| sudo apt-get purge lxc-docker | |
| sudo apt-cache policy docker-engine | |
| sudo apt-get install docker-engine | |
| sudo service docker start |