I have to think a bit to organize why and advantages and disadvantages of this, so I thought I'd share.

I define "tokenizing shares" as using a distributed blockchain with universal ability to transact as a way to trade shares.

Advantages of tokenizing shares:

* Universal access to fractional trading, not just inside a single trading platform. (Most likely, anyway.)
* Near zero transaction costs outside the spread. There are trading platforms that offer $0 commissions for regular retail customers, but there's still regulatory fees and these could potentially be skipped. (NOTE: There is likely a larger spread on a distributed ledger than a regular exchange, due to slower transactions. See below.)
* Ability to trade 24h
* Access to purchasers that don't want to or cannot use traditional platforms (e.g, crypto fans, people in developing countries). This may be stopped by "Permissioned ledgers", where there contracts that stop the transfer of stocks into a wallet unless it's on a whitelist.
* Resistance to total failure (due to the distributed nature of the setup)

Changes with good and bad sides:

* Slower trades; expect max speed of milliseconds rather than 1us
* Thus, a loss of the ability to do HFT (High Frequency Trading), which means that we lose the information transmission and market making ability of HFTs, but we also lose their ability to do front-running and decrease their ability to cause flash crashes and similar
* Loss of ability to shut down the trading. This remove an abusable tool of power, but it also lose a tool used to stop bad situation (like flash crashes), which is used to deal with bad market situations (like flash crashes due to trading software bugs), to avoid insider trading when major news is about to hit, and to deal with regulatory concerns.
* All trades are final. In the regular market, trades can be reversed by the courts or banks if done quickly (clearing in the US is in one business day, in Europe it's mostly two business days); in an anonymous, immediate clearing system based on a final distributed ledger, trades can't be reversed. This removes some types of risk, but adds in risks of theft (as we've seen with cryptocurrency) and makes mistakes more difficult to reverse.

Disadvantage if both on a regular exchange and a blockchain:

* The slower trades on the blockchain will lead to latency arbitrage. To avoid losses from price sniping, the blockchain market has to have wider spreads. Market makers will also typically provide limited liquidity in the slower market, to minimize the amount of loss the times they do get sniped.

Disadvantages of stocks on a blockchain in general:

* The distributed nature of the blockchain will allow frontrunning: A retail investor place an order in a market, and a frontrunning company with faster routing setup will then buy the stock elsewhere at a lower price, and re-sell it to the investor at the higher price. If there are distributed validators for transaction order, frontrunners may instead use techniques to bribe the validators (e.g, they may run validators themselves, and have their purchase jump the queue). The net result of both is that instead of negative spread going as profit to the retail investors, it goes to the frontrunners.
* It provides larger liquidity pools for criminals and terrorist organizations to do money laundering; e.g. Russia may be able to move billions around outside sanctions (though there are protocols in place that try to stop this)

Disadvantages for this particular blockchain at this time, for the companies that are resistant to getting on it:

* It is cheap to say no now, and they can't say no later
* There is little time to evaluate
* There is likely some level of legal risk

That's a quick attempt to sort my thoughts - they're not strongly researched for this particular blockchain, though I did a couple of searches to fill in details that I wasn't certain about.

That's nowhere near true. The current system is administered by regulated entities that have a decent amount of information about most owners. Are there ways to play the system? Yes. But it's nowhere near the same level of anonymity and flexibility as making stocks into tokens that can be freely transferred to anonymous accounts.

> Also, it was pretty racist with the whole English thing.

The "whole English thing" is about having someone that it is possible to communicate with in English. I'm fairly sure they would be OK with speaking English through a translation system, as long as it didn't get in the way of the communication.

Also, as someone that is from a non-English-speaking country: Somebody that isn't able to operate in English is unlikely to be able to do good open source work. English is the language that is used to communicate about programming globally, and anybody that does not tap into that communication is at a very large disadvantage. The overall situation is unfortunate, and it can be argued that the situation is systemically racist - but it isn't individually racist to require the ability to operate in English. Fortunately, AI tools are continually making it easier to operate in languages you don't speak, so this disadvantage will become less and less of a problem.

This report is largely an advertisement for Originaâ(TM)s contract negotiation services. It is sparse on detail and should be viewed skeptically.

Methodology

* Research firm: Censuswide
* Commissioned by: Origina
* Fieldwork: May 11â"19, 2026
* Sample: 2,018 senior IT decision-makers
* Geography: US 1,011; UK 1,007
* Seniority: Director level or above
* Eligibility: Direct influence over software budgets and renewals
* Organisation size: Revenue of $1bn+ or 10,000+ employees
* Vendor exposure: IBM, HCL, VMware/Broadcom or SAP at meaningful scale
* Industries: Financial services, insurance, telecommunications, public sector and retail

Key findings

* 95%: Had paid for a vendor-driven change they expected to reduce performance, reliability or business value.
* 78%: Said their primary vendor controlled the timing and terms of change.
* 76%: Reported little real negotiating leverage.
* 80%: Said switching major vendors would cause significant disruption.
* $12.87m: Average reported cost of the most recent vendor-driven change.
* 28%: Average share of enterprise software budgets spent on vendor-driven changes organisations would not have actively chosen.
* 84%: Said senior engineering staff often or always maintained vendor-driven changes instead of building future capability.

Contract negotiation priorities

* Renewal pricing: Cap annual increases and define expansion prices.
* Support: Set minimum support periods and advance notice of withdrawal.
* Upgrades: Limit mandatory changes; require written justification and cost estimates.
* Exit: Agree migration fees, assistance and transition rates in advance.
* Portability: Guarantee data export, documented APIs and transition access.
* Maintenance: Permit independent support where feasible.
* Security: Define patching obligations and response times.
* Cost tracking: Include engineering time, downtime and hardware replacement in change assessments.

Note that considering a thriving open source community is missing from their messaging.

You have a medium memoruy. I remember how USENET was, prior to the web forums you complain about. Reddit has blocked the possibility of a real discussion forum that works for complicated topics, locking in a short term popularity contest with ever-shifting "hot topics".

The compute cost for DeepSeek V3 was $5.6M. Training costs for traditional US frontier labs model is about $70 to $200M+, with estimates of up to $1B+ to train the next generation models. It's expensive, but it's not even in the ballpark of "There is no viable economic model to do so." Google revenue in Q2 was $1.32 billion *per day*. $1B is trivially less than a day; $5.6M is just 6 minutes of revenue.

The cost problem is in the compute buildout and the subsidized compute to capture users.

> It remains to be seen if even using their models can be profitable,

I presume we can stipulate there exists some tasks that are cheaper to do using an LLM than doing it "by hand". As a such, using an LLM model can be profitable. To me, it is also obvious that the business of serving an LLM model can be profitable. There's large benefits to being able to get tokens quickly, and you can expect people to pay for that, even if we presume they could run the model locally with no maintenance overhead. There's even cases where using the cloud is cheaper than you can serve locally at all - I saw one guy that measured his added cost of electricity when his computer ran a specific LLM, and it was more expensive to run it locally than to buy tokens from the cheapest inference provider for the same model, presumably because they had better hardware and cheaper electricity.

I love a term a friend of mine used to use when talking about AI: "Narrowly superhuman" (before "superintelligence" got to be a thing).

AI is narrowly superhuman. It is able to do specific things that used to be defined as a "hallmark of intelligence" much better than a human. The easiest one? Summing numbers. It's both faster and more precise than a human.

AI gets more and more such areas it can do better than a human.

If we define "Superintelligent" to be "A computer system able to do absolutely everything we can somehow call intelligence better than any human", we will *never* have superintelligence. The things we define as intelligence will be in conflict; we'll land at the system being better at one at the cost of another.

I'm in favor with being careful about this development. But "prohibiting the development of superintelligence" isn't an option. It's already here.

Dividing the yearly US spend on Internet advertising by the number of people in the US that has Internet access gives $900-$970 spend per person per year, or $75-$81 per month.

Please choose the option you want:

1. I want to pay about $75 per month per member in my family into the ad auctions to not see any ads. Ie, keep the infrastructure, but let you opt to pay your share through money rather than attention.
2. I want to prohibit ads, and accept that everybody will have to pay for the sites they want access to, that there will be much fewer services, that there will be much higher transaction costs, and that the poor will have much less ability to access services. (Microtransactions won't solve this, due to the need for trust evaluation of same.)
3. I want some combination of 1 and 2, where some sides of ads are prohibited so it is possible to buy my way out of them at a lower price,. I accept that this will result in the loss of some services and lower access to others for the poor.
4. I want legislation that makes Internet ad blockers work more consistently. I accept that this just shifts the attention cost to other people, and have me getting the benefit without paying the cost.
5. I want to remove Internet ads and subsidize the sites through taxes.
6. I want to increase the requirements of Trust and Safety checks and format restrictions for ads, potentially accepting higher overhead (in what goes to content vs infrastructure) for less annoying/bad ads. I accept that doing this creates significant advantages of scale for ad networks.
7. Other, please describe, including where costs gets shifted with your proposal.

The above are my best attempt at listing possible options and their correct caveats. In most situations, I find ads annoying (who doesn't). Unfortunately, they are the main money highway of Internet content.

DISCLOSURE: I used to work for Google, where I rang infrastructure for stopping bad ads/advertisers and for removing fake clicks. I've also been interested at the use of sub-cent microtransactions to pay for content since the 1990s, though I've become disillusioned as I've learned more about the attention economics involved. Approving individual microtransactions has a higher attention cost than dealing with the ads, and bulking them per-site (through micro-subscriptions) will limit the number of sites that can have content.

OK, which particular aspect of the LLM learning do you think doesn't exist in the human brain? Backprop exists (and the other training methods the brain uses has similar effects), embeddings more or less exists and seems to be very similar to the LLM embeddings (shown by brain activation in various areas linearly matching with embeddings) - see e.g. High-level visual representations in the human brain are aligned with large language models (Adrien Doerig et al, 2025), and speech/text generation by humans has similar error patterns as for LLMs.

They obviously don't learn in the EXACT same way, but there's also clearly a lot of bits that are the same, and I'm fairly sure nobody knows how much is similar yet, particularly not weighted by importance.
 

Some may not recognize the Brazil reference.

No need to be antagonistic. Respond with inflation adjusted BLS salary average and percentile for the job and region.

https://www.bls.gov/oes/curren...

Itâ(TM)s unclear what happens when one aspect of their system changes unless other parts also change. The bags flying free was designed to make boarding more efficient, which reduces gate time. If bag check is not free, then itâ(TM)ll mean more people bringing their bags on plane. This means boarding will be more complex and time consuming. Itâ(TM)s not clear their âoefirst come first chooseâ seat process will work as well, as people trying to backtrack, or holding up the entire boarding process.

I get that AI is costly, hard to run on the average desktop, and hence APIs. However, it seems we've shifted as a community to fully supporting proprietary web APIs where we not only don't have source code but even lack the binaries. Couldn't slashdot moderators help by promoting truly open source tools, especially AI, that can be run locally, even if it requires a 10k hardware? It seems very reasonable if a company sells API as cost-sharing model. Even so, at least we could support entities that fully open their models and have active "hacker" communities that are not bound to a proprietary service.

Presuming they used DNA to find the suspect, located with some statistical certainty (say one in a million). I'm wondering then if that person's DNA match was the presented as proof, eg "one in a million this isn't the killer"? If so, isn't this a form of double dipping? If you "used up" the one in a million chance to find the suspect, can you use the statistics dice again to claim that this suspect is also the killer?

The existing practice of human car drivers in this situation is to slowly inch though the crowd. Amend the regulations to reflect practice, then update wamo to behave according to the regulations.