ChatGPT

[jesmu84]

Interesting article by a guy who sold his home using AI as his "realtor":

https://www.nytimes.com/2026/05/28/technology/sell-house-with-ai-no-realtor.html?

Paywall

[NCMUFan]

But was there a breathing closing attorney?

[PointWarrior]

Probably, but a lot less breathes by the attorney during this transaction.

But was there a breathing closing attorney?

[MU82]

From Axios:

AI tools might be hallucinating less. But they're still spitting out inaccurate answers cloaked in polished, hyper-confident language.

Why it matters: The more people trust AI, the less likely they are to catch costly mistakes. It's a growing problem as people increasingly lean on the technology for research, medical advice and schoolwork.

The big picture: Obvious hallucinations are easy to catch. The real trouble comes from false answers that sound convincing.

Plausible citations, mostly correct summaries, and confidently wrong answers slip past users.

If AI becomes accurate enough often enough, people might stop fact-checking altogether.

[PointWarrior]

Easy answer - actually read the AI output. Bad AI output is either lazy input, lazy prompting, or lazy checking.    But that's most of the workforce today...

From Axios:

AI tools might be hallucinating less. But they're still spitting out inaccurate answers cloaked in polished, hyper-confident language.

Why it matters: The more people trust AI, the less likely they are to catch costly mistakes. It's a growing problem as people increasingly lean on the technology for research, medical advice and schoolwork.

The big picture: Obvious hallucinations are easy to catch. The real trouble comes from false answers that sound convincing.

Plausible citations, mostly correct summaries, and confidently wrong answers slip past users.

If AI becomes accurate enough often enough, people might stop fact-checking altogether.

[NCMUFan]

Easy answer - actually read the AI output. Bad AI output is either lazy input, lazy prompting, or lazy checking.    But that's most of the workforce today...

But do you do more than read the AI output?  Do you fact check the output AI provides you?
AI has a huge ego for not being wrong and pinning it on only bad prompts seems pretty lazy in itself.

[MU82]

Fear of AI is bringing young Americans together.

Harvard's youth poll found 59% of Americans 18 to 29 see AI as a threat to their job prospects, including 66% of young Democrats and 59% of young Republicans.

[PointWarrior]

yes, read, fact check, rewrite parts every time....  it gets you 90% of the way there.  and that 90% about 100x faster...

But do you do more than read the AI output?  Do you fact check the output AI provides you?
AI has a huge ego for not being wrong and pinning it on only bad prompts seems pretty lazy in itself.

[mu_hilltopper]

So .. the other day, I read an article and 10 minutes later, had a 9 billion parameter LLM running on my laptop.  It's not the latest and greatest, but then again, nor is my humble 16GB laptop.

NVIDIA has a product called the DGX Spark, which is about the size of a dictionary, sells on Amazon for $4700 and is rated at "1 petaflop of AI performance."

These $4700 boxes can surely handle multiple AI queries at a time, let's just go with a nice round number of 10 users per box.  $500 per employee is not even a rounding error. 

Yes, the models that ChatGPT, Claude, Gemini are "better" but all models are catching up fast.

So .. why are "hyperscalers" building out dozens and dozens of new data centers across the country?

Isn't the obvious path for businesses to buy these $5k boxes and slice down their huge AI subscription fees?

[Hards Alumni]

So .. the other day, I read an article and 10 minutes later, had a 9 billion parameter LLM running on my laptop.  It's not the latest and greatest, but then again, nor is my humble 16GB laptop.

NVIDIA has a product called the DGX Spark, which is about the size of a dictionary, sells on Amazon for $4700 and is rated at "1 petaflop of AI performance."

These $4700 boxes can surely handle multiple AI queries at a time, let's just go with a nice round number of 10 users per box.  $500 per employee is not even a rounding error. 

Yes, the models that ChatGPT, Claude, Gemini are "better" but all models are catching up fast.

So .. why are "hyperscalers" building out dozens and dozens of new data centers across the country?

Isn't the obvious path for businesses to buy these $5k boxes and slice down their huge AI subscription fees?

That depends.  The federal government as your client ensures a constant stream of cash flow.  Kind of like RTX, Boeing, NG, etc...

Should these companies have something like what you're describing as well?  Yes, I think so.

I think the real problem with OpenAI and Anthropic, etc is their models aren't getting better.  And they aren't making money.  That's why they want to IPO ASAP.

[jesmu84]

That depends.  The federal government as your client ensures a constant stream of cash flow.  Kind of like RTX, Boeing, NG, etc...

Should these companies have something like what you're describing as well?  Yes, I think so.

I think the real problem with OpenAI and Anthropic, etc is their models aren't getting better.  And they aren't making money.  That's why they want to IPO ASAP.

So someone else can be left holding the bag?

[Hards Alumni]

https://thezvi.substack.com/p/white-house-will-ad-hoc-decide-who?ref=pdoom100.com

[MU82]

https://thezvi.substack.com/p/white-house-will-ad-hoc-decide-who?ref=pdoom100.com

Socialism.

[NCMUFan]

So .. the other day, I read an article and 10 minutes later, had a 9 billion parameter LLM running on my laptop.  It's not the latest and greatest, but then again, nor is my humble 16GB laptop.

NVIDIA has a product called the DGX Spark, which is about the size of a dictionary, sells on Amazon for $4700 and is rated at "1 petaflop of AI performance."

These $4700 boxes can surely handle multiple AI queries at a time, let's just go with a nice round number of 10 users per box.  $500 per employee is not even a rounding error. 

Yes, the models that ChatGPT, Claude, Gemini are "better" but all models are catching up fast.

So .. why are "hyperscalers" building out dozens and dozens of new data centers across the country?

Isn't the obvious path for businesses to buy these $5k boxes and slice down their huge AI subscription fees?

https://www.youtube.com/watch?v=aXy8mQeuObk

[mu_hilltopper]

https://www.youtube.com/watch?v=aXy8mQeuObk

Lol .. that exact video was what inspired me to install the LLM on my laptop.

[MU82]

From Axios:

We ask almost every AI architect and leader the same thing in private: What AI risk worries you most?

Almost all of them fire back the same response: a killer pathogen, spreading too silently, widely and quickly to stop, Jim VandeHei and Mike Allen write in a "Behind the Curtain" column.

Why it matters: The rare agreement among AI experts flows from the belief these advanced models could help a bad actor create a deadly pathogen before the government and industry perfect detection and prevention systems.

This isn't a likely scenario. It's simply a plausible one as AI gets better at understanding our biological vulnerabilities, much like it has our cyber ones inside large nonhuman systems.

Driving the news: The experts aren't hedging. In a new MIT FutureTech and University of Queensland study, 272 researchers ranked 24 top AI risks.

They assessed a 12% chance that AI's dangerous capabilities produce a catastrophic outcome by 2030, and another 12% chance of AI-enabled weapons and mass-harm capabilities.

And that's with mitigation efforts to reduce risk. Without those efforts, the chances exceed 20%.

The researchers defined "catastrophic" as more than 1 million deaths or $100 billion in damage. Assisting with the construction of chemical or biological weapons sits squarely inside that top-ranked risk category.

The big picture: We're not writing this to scare you, but to say bluntly what those building the technologies say privately — and, in more careful ways, publicly.

Just last month, OpenAI's Sam Altman, Anthropic's Dario Amodei, Google DeepMind's Demis Hassabis, Microsoft's Mustafa Suleyman and Meta's Alexandr Wang cosigned an open letter warning of the risk of AI-derived bioweapons and calling for more safeguards.

This pathogen possibility colors every debate by the federal government and industry about how to review and understand new AI models and capabilities before they are released to the public.

The hope is that the frontier model developers build guardrails and capabilities that can block bad actors with bad intent — and that the very technology that could be used to create a pathogen becomes advanced enough to prevent or inoculate against it.

Reality check: It's very hard to prevent bad actors from plotting evil schemes, especially with so-called open-source models improving so rapidly.

These open models can be used and adapted by anyone in private settings outside of any regulatory regime.

Open-source critics often make this very point, even though the frontier models are much more likely to possess the compute power to create something novel.
So the better the models get, the more likely unthinkable things will happen, both bad and good.

How it might actually unfold: Today, engineering a genuinely novel pathogen requires rare expertise, specialized lab equipment, and years of failed experiments. AI expedites all of this. Here's the sequence that keeps experts up at night:

1. A bad actor (whether state-sponsored or a well-resourced individual) uses a powerful model to map human biological vulnerabilities and identify which genetic tweaks make a known pathogen more transmissible, harder to detect, or immune to existing treatments.

2. The model, trained on massive biological and genomic datasets, generates viable candidates. Imagine hundreds of the smartest scientists working at warp speed, never stopping, never tiring. That's what swarms of agents do.

3. Those candidates get synthesized using increasingly cheap, accessible gene-editing tools, a separate technology accelerating with AI.

4. The pathogen spreads before anyone knows what they're looking at, novel enough to slip past existing biosurveillance systems. By the time public health systems recognize it, containment is vastly harder than we experienced with COVID.

That's why you should pay attention to the debate over new cybersecurity risks created by AI. It illustrates the race to create effective detection and prevention faster than new offensive capabilities can emerge.

It's also why you should track the open-source debate, too. A frontier lab can bake in guardrails. An open model fine-tuned by someone operating outside any regulatory regime can evade this.

The bottom line: This is heavy, scary stuff. But ignoring it won't solve it. Knowing it and preventing it will.