A recent interview with Paul Jay revealed a nuanced editorial position on both capitalism and socialism: these great social experiements of the 20th century ended up giving us essentially the same result.
The negative end-result of both capitalism and socialism as human organizing frameworks seems to be extreme concentration of power and wealth at the top. Extreme socialism produced oligarchs just as extreme capitalism produced Wall Street.
It's a great and thoughtful interview, and a fair hearing of both that you won't get in corporate media. This discussion is a timely and important one, given that we are in the grip of a revolt by the U.S. electorate whose desire for income equality and a basic social safety net has been thwarted by its paymasters and is rapidly mutating into an angry fascist right-wing uprising, not unlike what was seen in the run up to World War II in Germany.
Around the 16:15 mark, Paul suggests that public ownership is one of the great counter-weights to concentrated private wealth. He asks some very important questions:
"How do we break up private ownership? How do we diversify public ownership?"
Paul suggests that Artificial Intelligence may play a significant role in a new economy in which public assets and spending are coordinated by this benevolent technology, and that public programs such as the Green New Deal could be run using AI. I think everyone seeking a just transition from fossil fuels can all agree: This is a fantastic vision.
While I agree that AI, also known as Machine Learning (ML), will likely have a large role to play in the farther future later on -- if humans survive that long -- we have to consider that AI/ML won't just arrive at a benevolent outcome on its own. Just like humans, AI/ML is a product of its inputs, many of which contain biases that are unaccounted for (and are themselves, unaccountable).
Furthermore, some of the most advanced AI/ML computations are impossible for humans to understand at this point and may unintentionally ensnare humanity into a gridwork of opaque algorithms, which in the wrong hands, could produce unthinkably horrible outcomes.
Just as we have to walk before we can run, we need to step back and envision a strategic roadmap of just exactly how we arrive at this last step: a benevolent AI (or set of benevolent AIs) working in concert as a social coordination technology for the benefit of humanity -- and the planet as a whole.
At the risk of sounding like a tech douchebag, hear me out: The blueprint of the first step in this journey may be blockchain technology. You've probably heard of Bitcoin, and you've maybe even heard of Ethereum at this point. At their core, they demonstrate near perfect systems of trackable assets.
Blockchain technology could completely eliminate corruption and misappropriation of public funds, as long as it is operated by a diverse set of concerned actors (government agencies, NGOs, individual citizens and political interest groups). Probably the best blockchain solution would be a public permissioned blockchain. Public as in: anyone can run a node in the network and reproduce/audit all transactions. Permissioned as in: only certain actors in the system can "write" transactions into the ledger.
I won't get into the extreme nooks and crannies of the tech, but if the chain were not permissioned it would create the risk of bad actors (insert James Bond villain) taking control of the network by seizing 51% of all compute power in the network to misappropriate funds. If you're confused, there's an episode of Silicon Valley, the TV show, called "Fifty-One Percent" that will explain (season 5 episode 8).
Putting public funds on a distributed blockchain ledger will not "decentralize" the government. But it could produce a verifiable trail for every penny of public funds collected, open for any and all to audit at any time.
The process of putting assets on an auditable blockchain (or other Distributed Ledger Technology) is called "tokenization", and it is happening behind closed doors in the FinTech sector and Wall St. at a dizzying pace (despite the very public recent collapse of Bitcoin and other cryptocurrency prices, which may or may not end up having anything to do with tokenization of assets in the long run).
Imagine a world in which you pay your taxes to a "smart contract" on a public, permissioned blockchain which then appropriates every dollar or fraction thereof to each government agency or program that the house and senate have agreed upon in the annual budget. Each of these agencies and programs then pays its workers and contractors via smart contracts of their own, and every penny ends up where its supposed to be with an immutable audit trail.
After we get everything auditable, the next step in the roadmap towards benevolent AI running our Green Economy would be applying very narrowly focused Machine Learning algorithms on this public ledger and combining it with public, verifiable data that is controlled very carefully for bias. The scope of these ML programs should be very narrow and the algorithms that are used must be thoroughly understood by humans and open-sourced so that they may be scrutinized and challenged by any and all.
Rule By Nobody: Algorithms update bureaucracy’s long-standing strategy for evasion -- by Adam Clair for RealLifeMag.com
The Dark Secret at the Heart of AI -- by Will Knight for MIT Technology Review
Fifty-One Percent -- Silicon Valley on Amazon.com
Smart contracts - Simply Explained -- by Simply Explained on YouTube.com
Why your AI might be racist -- by Jerry Caplan of Stanford's Democracy, Development and the Rule of Law for Washington Post
AI Experts Want To End 'Black Box' Algorithms In Government -- by Tom Simonite for Wired.com