Gareth321

This happened to me and I haven't bothered to create a new account. I was a 16+ year user. Banned over something innocuous. I tried appealing but it was clearly AI which denied the appeal - despite them claiming it was a human.

Something most people don't know is that Reddit now has a kind of AI partnership with moderators of the larger subreddits. If those mods ban a user from their sub, the AI can suspend or ban a user for "breaking the terms of service." Given the state of Reddit, and the kind of people who moderate the large subreddits, it's creating a fairly violent and polarised community.

Benchmaxing is defined as targeted post-training attempts to optimise for higher benchmarks. This doesn’t change the aggregate intelligence or capability of the model, so it can make a model appear more capable than it is.

I’m hopeful mine survives until RAM and SSD prices return to Earth. Unless I drop it I suspect it will. It’s still going strong. Maybe the M8. Hopefully chips and local LLMs have developed to the degree that local inference will be good and fast.

One might argue that we have calibrated drug safety to such a high degree that the aggregate negative social impacts are far higher than the costs of side effects. Of course we don’t want another thalidomide catastrophe but I think there are large potential test groups to mine who are much less sensitive to the risks. For example, the elderly and those with terminal diagnoses. Right now we only permit drug testing in very limited cases for the latter group, and only when the treatment is related to their terminal diagnosis. I’m seeing older people taking all kinds of experimental anti-aging drugs right now, either off label, or novel. We’re not collecting any of this data empirically because what they’re doing is not legal.

Further, [we are getting much better at simulating drug interactions.](https://pubmed.ncbi.nlm.nih.gov/41155903/) I strongly suspect AI will offer new probabilistic models to funnel novel drugs into more targeted trials which are much more likely to succeed. If we had 90% certainty of success and high certainty of lower adverse reactions I think we could allow much less stringent human trials. That means much lower cost and much faster turn-around.

Elon Musk was the only investor. Which investors are you referring to? And yes, when you pay a premium for stock, you’re paying more than the intrinsic value. This was widely reported so I don’t understand why you would pretend that it’s a big secret.

> I don’t see anything in what you wrote really support that Twitter did well after firing 80% of its staff.

That’s because I didn’t make any claims about X’s financial performance. I was explaining to you why you were wrong on some important facts.

> This is objectively false. 2020 revenue for Twitter was $3.71B while 2021 came in at $5.07B.

First, you’re conflating revenue and profit, which is kind of unforgivable in this context. Second, I explained that in those years they made large profits. In my comment. Which you clearly didn’t read.

If this is just where you like to emotionally vent may I instead recommend Reddit? I like the interesting discussions here.

It is estimated that only 2% of the global workforce is paying for AI right now. If that grows to double digits - especially in the 30%+ range - and Anthropic captures a significant proportion of it, their valuation is justified. This is assuming they don’t offer virtual employees and these are 100% in the pipeline.

To be fair, if you told Amazon investors in 1997 that their investment would go up 3,300x and Amazon would be worth $2.7 trillion in 2026, they’d have called you insane too. There isn’t a mathematical reason Anthropic can’t be worth that in 2056. I don’t think it will be, but not because of the compounding returns.

Einstein once said “compound interest is the eighth wonder of the world.” It’s very hard for humans to comprehend compounding returns.

> If the company really did continue to operate in the same manner, it wouldn’t have been worth 80% less in 2024 (https://www.cnn.com/2024/10/02/business/elon-musk-twitter-x-...), nor would it need to be rolled up and financially engineered into being part of an AI company that then gets rolled into a rocket company.

Musk paid an absurd premium for the company. Far more than it was worth. It was intended to force the board to sell. So an 80% estimated drop (it’s private so it’s a very rough estimate and I won’t go into the various reasons you should not accept it at face value), sounds scary but it’s by design.

Twitter as a company was doing quite poorly leading up to the acquisition. It lost more than $1.3B in the two preceding years. A big downgrade from its previous $1B+ annual reported profits.

As for the 81% headcount reduction, Musk confirmed that, but if you think he’s lying about that for some reason, CNBC investigated it. Journalists examined internal Twitter records and reported approximately 1,300 actively working employees, with another 1,400 still receiving pay but no longer working. CNBC also interviewed two recent employees who corroborated the scale of the reductions. https://www.cnbc.com/2023/01/20/twitter-is-down-to-fewer-tha...

Prior to acquisition, Twitter's SEC filings confirmed more than 7,500 full-time employees.

I don’t see why this claim is so unbelievable. Twitter was famous for its army of social media staff whose entire day comprised two meeting between matcha tea and meditation. https://youtube.com/watch?v=buF4hB5_rFs

Musk just fired everyone who wasn’t an engineer and it turns out the engineers keep the lights on.

I’m suggesting it’s highly unlikely it will call the FBI to dob you in. That affirmatively trained behaviour, not something one should expect to accidentally happen.

Science progressed, but it didn't bring the kinds of physical innovations we expected. At least, not until recently. Where are the flying cars? Why haven't we cured ageing? Why don't we have a base on the Moon? Why isn't energy effectively unlimited and free? We had an explosion in information sciences. The internet is a marvel. iPhones and chip technology are incredible. AI is unbelievable to witness. But why did we slow down so much on physical scientific innovation?

I'm with the other guy. Humans exploring space is fun an exciting to me. Sending drones into space is cool, but far less fun and exciting. I want to explore space. I want recreational spacecraft for humans. I want technologies which allow humans to explore the outer regions of our solar system. You might think flying to Mars is a waste of time but I think that's incredible. I would have signed up before I had a family, even if it were a one way trip.

I think it is widely accepted that power corrupts. It is the basis of democracy, and why we would defend it so jealously. Democracy was fought for and won, and we should never forget the alternatives are much worse. This requires peaceful debate, free speech, and free and fair elections.

I agree. This seems like another manufactured internet culture war. One of the most interest podcasts I listened to was around his ideas on democracy and aging demographics. Specifically, as many nations become older, older voters are voting for conditions which benefit themselves at the expense of the young. As they make conditions for the young more and more intolerable, they have fewer children and exacerbate the demographic issues. The effect is self-reinforcing and inexorable. He posits, "maybe this is just how democracies end?" Many Western nations have been using high immigration to plug those demographic gaps, but this isn't a permanent solution. Nations are facing ratios of two working people for every retired person in the coming years and decades, and unless we see some pretty massive technological improvements (or radical economic transformation), things are going to collapse.

I also very much appreciate his positions on how we've allowed physical engineering and sciences to stagnate. It's true. We flew to the moon 57 years ago and basically gave up exploring space. As a space nerd, SpaceX is a breath of fresh air.

Most publicly listed companies in the West require independent audits. Those audits are published for everyone to read. The listed company and the auditors are all tightly regulated, and while fraud can happen, it's relatively rare.

I think you're making the mistake of thinking that because fraud occasionally happens in the West, it's on anywhere close to the same scale as China. If Chinese companies were capable of passing independent audits, they'd all be publishing independent audits. That would be a massive confidence booster in China and internationally, and stock prices would rise significantly. They don't, because they wouldn't pass.

I agree. I think the future for privacy conscious people is going to be an adversarial open weights model, where competing models (ideally from opposing superpowers and labs) critique each other's work to find consensus. Or at least alert for suspicious behaviour.

It could, but it's an inexact science. A little bit like tampering with the brain. Even a surgical procedure removing a tiny tumor can interfere with critical pathways. Usually abliterated models end up almost exactly as capable in benchmarks, but it requires the person doing the abliteration to anticipate or know about the censorship. In the case of Chinese model censorship it is believed that it is post-training, so the underlying data input is not the issue. It's how the model has been trained to use that data. Specifically, abliteration usually targets the refusal behaviour. I would guess that this example could be significantly improved, but I have not seen any abliteration attempts re this specifically.

I partly agree. In support of your scepticism, I also think the frontier labs are overvalued. The expensive part which is not currently priced into the subscriptions is the training. Inference is usually profitable now - at least from what we can tell from industry reports.

Where this bubble differs from the dot-com bubble is that valuations have very strong, organic user demand behind them. In 1999, prototype websites with few customers were getting IPOs worth hundreds of millions. This time, demand is insatiable. Frontier mdoels also has very low global penetration, relatively speaking, so the TAM permits exponentially more sales, in theory.

However the risks are numerous for these labs. Foremost is that Chinese labs are able to easily distill frontier models. A tiny fraction of the cost of training, and they have a close approximation. Second, models are getting much more efficient per task. Companies are already discovering what "good enough" looks like, and it often ends up being much cheaper than what Anthropic and OpenAI are selling. This will only accelerate.

My investment thesis is bullish on the entire AI stack except the frontier labs. I think power generation is going to see huge demand. Ditto for infrastructure and data centers. Ditto for the products and services created by these models.

I would love to hear more about your setup. Which hardware and models? How much context can it handle? How does it compare to frontier models?

Thanks for the tip! I've already got a home server set up. Maybe this is my next AI purchase. Still, US$2,600 here in Europe is not cheap :-/

> No political censorhip? Try asking Chinese models about Taiwan.

Self-hosting means being able to use abliterated models where the superficial instructions like not describing Taiwan as independent are burned off.

Censorship and bias are related but not synonymous. I agree that it's harder to remove political bias when the training data includes it. We know many of the frontier models are heavily trained on Reddit, for example, [which likely explains why we see such heavy left wing bias.](https://trakkr.ai/bias/findings)

More citations:

https://www.gsb.stanford.edu/faculty-research/working-papers... (article: https://news.stanford.edu/stories/2025/05/ai-models-llms-cha...)

https://www.tandfonline.com/doi/full/10.1080/29974100.2026.2...

https://ieeexplore.ieee.org/document/10817610

https://www.washingtonpost.com/technology/interactive/2026/0...

https://link.springer.com/article/10.1007/s11127-023-01097-2

It will not do that unless you specifically teach it to do that, or you use an Anthropic model. The issue in this case was Anthropic's safety monitoring built into the model. They record everything and flag potential illegal activity for human review. This occurs even over the API. I do not know if OpenAI does this, but I would not be surprised. I think the Chinese models are far less likely to do this (unless one resides in China).

It's a good first step. They're not great but they kind of work for many tasks. I think the small models can be useful in context-specific tasks but the friction is high right now because the user is forced to determine whether Siri is good enough, or a better LLM is required per task. Ideally Siri would be smart enough to intelligently route. Apple claims that's how it works but even their cloud processing is far behind frontier models.

Worryingly, they're taking a walled garden approach, and they don't want to expose user data to competitors. After the monumental failure of Siri over more than a decade, I don't trust Apple to execute competently on AI.

The inference is profitable. It’s the training which is really expensive and not being reflected in the subscription prices.

Also no political censorship, and it doesn’t report you to the police when it suspects you did something illegal. That really happened, by the way.

Now I want a local LLM server :( $25k is just so much money.

The last time I saw research on this, they found that the median age of Redditors had dropped significantly over time. I suspect they have a newer and younger user base now. I still talk with older users from time to time and we believe our views on free speech and censorship have not changed significantly over time. However Reddit policy and moderation and majority opinion have changed a lot. They are far more accepting and willing to use censorship to realise political goals.

I’m well aware that Reddit doesn’t reflect reality. Most of society is not far left, chronically online, violent, and out of touch with reality.

The study you provide isn’t related to my comment but if you’d like to shoehorn it into future discussions you should at least read it first. That paper is widely criticised, and is, ironically, the kind of thing shared on Reddit. I’ll just copy and paste existing academic criticisms of the paper because I don’t really think you’re going to take the time to seek this out yourself.

1. Inconsistent data collection: The far-right data came from an established extremism database, while the far-left data had to be assembled separately because the database did not routinely track most far-left extremism. This creates a potential sampling bias, with no demonstrated guarantee that both sides received equally comprehensive coverage.

2. Serious numerical inconsistencies: The study counts 168 deaths from the Oklahoma City bombing but reports zero far-right bombing incidents. It also contains basic arithmetic errors, including percentages totalling 110%. These inconsistencies raise legitimate concerns about data quality and the reliability of the analysis.

3. Subjective ideological classifications: The study uses broad political definitions and includes homicides with weak evidence of ideological motivation. Without a fully accessible case-level audit trail, independent researchers cannot easily verify whether incidents were classified consistently or accurately.

4. Unsupported statistical conclusions: The author suggests far-left attacks are more likely to cause multiple fatalities, but the reported difference is not statistically significant (Fisher's exact test, p ≈ 0.22). Drawing substantive conclusions from this difference is therefore questionable.

5. Disproportionate influence of one attack: The Oklahoma City bombing accounts for approximately 32% of all far-right fatalities in the study. Removing that single incident reduces the far-right-to-far-left fatality ratio from 6.7:1 to 4.6:1. The overall disparity remains, but its magnitude is significantly influenced by one exceptional event.

6. Selective historical timeframe: The study begins in 1990, excluding the 1960s–1980s, when far-left revolutionary movements were especially prominent in American domestic terrorism. This timeframe may be appropriate for the research question, but it makes broad claims about political violence across modern history misleading.

7. Conclusions beyond the evidence: The study examines ideologically motivated homicides, not political violence generally. It excludes non-fatal assaults, riots, intimidation, arson without fatalities and other violent offences. Using its findings to claim that the far-right is responsible for most actual violence is therefore a substantial and unjustified extrapolation.

Notably, the time frame used doesn’t reflect reality today, so it cannot be used to make normative claims about which ideology is more violent right now.

To be clear I’m not suggesting that that is my preferred outcome. I’m worried that that is what this new generation will vote for. Then when they get it, they’ll realise what they lost, and they’ll have to fight to get it back again.

No, and it’s very rare for Chinese companies to allow independent audits. I would not trust DeepSeek financial disclosures. However from industry data we can see that inference is often profitable. It’s the training which is very expensive. Which is not a big problem for Chinese companies since to date much of their training is just distilling frontier models.

As a now former Chinese investor, the numbers reported in China are almost entirely fictional. They are whatever the local prefect or senior leaders wants them to be. Right now they seek to imply to the West that they are competitive, so the numbers reported reflect that. Almost no Chinese company permits independent audit of any of their finances.

It’s a rabbit hole but if you have some time it’s fascinating to look into the sentiment of investing in China vs the rest of the world. Because the numbers are so unreliable, investing is considered about the same as gambling in China. You’re more likely to lose a lot of money than win, and there is no way to accurately predict strong from weak companies based on their reported financials. Occasionally senior leaders will sentence the leader of very large failed company to death to attempt to maintain confidence, but it’s not working. This is one of the reasons for the housing bubble. There just aren’t stable investing opportunities in China.