gravypod

Weren't many of these reports after 404's reporting?

If they, through standard accounting practices, cannot show they are profitable how do we know they are profitable? I assume they can't just say "we are profitable assuming you ignore the costs we paid to build up XYZ". I'm guessing they could pre - rent compute for 10 years or something and maybe that's all on the balance sheet but you'd just amortize that and I think that falls under GAAP?

Could the original chart be including non-cash consumption? (Private jet flights paid for by company, stocks, bonds, etc)

I could start a business with a similar growth trajectory. We can mail people $100 bills for a low low payment of $10. I just need $500 billion dollars of startup capital and I can show you a 10x yoy growth for a few years.

Yes, but no one had time to cycle through shodan and find the right things to attack. Now you can just attack them all.

I think our biggest advantage in hardening is inaccessibility of swift and how many circuit breakers are in place to prevent large moves of money.

> When someone wielding a non-safeguarded model deletes the money in everyone’s bank account, I look forward to the HN comments claiming it’s an attempt by Anthropic to pull off regulatory capture.

This statement portrays a fundamental misunderstanding of how the infrastructure which powers these systems work. Note: I am not saying there are no risks, I am just saying the risk you are focusing on is the least likely one of all I have seen people be upset by.

Far higher risks one could outline are:

1. Network-connected PLCs for big infrastructure (drinking water, sewage, power, etc) being tampered with.

2. Extremely persistent malware tailored for every permutation of hardware + software.

3. Cyber criminals improve in technical capabilities (phishing sites, scam calling, propaganda campaigns, etc).

But, the cork is out of the bottle on this one. With even basic models you can begin a loop of training specialized models on low cost hardware which can be used to do specific hacking tasks.

I don't know what the best antidote to this is but I doubt that it will be in limiting access to OSS models to people in the USA as all of the threats listed above come from *outside actors*.

This part of the article really stands out:

> On Sept. 17, NIST’s Center for AI Standards and Innovation (CAISI) published its own assessment of GLM-5.3’s cyber capabilities. CAISI found that GLM-5.3 is “the most cyber-capable open-weight model released to date” and that it lags the US frontier by about four months on an aggregate of CAISI’s cyber benchmarks.

To translate: "This free model, you can host yourself, is at max 4 months behind Anthropic - as confirmed by Anthropic and the US Government - and it won't reject your requests"

Interesting play before an IPO...

In an era of digital records keeping, this does not sound impossible.

It's harder to replace in history.

If your company is really interested in doing AI stuff you may be able to sell people on the idea of a "fast agentic CI" where you:

1. Track code coverage from all current test cases in your big 8hr runs.

2. Index the coverage so it goes testcase name -> methods touched.

3. On every commit run an agent which looks at the diffs, tries to predict which tests the code being edited is touching, and run just those tests (or a random subset if there are too many).

Once you have this you can also automatically kick off builds every 8hr that contain all the previously submitted patches. Once that is done, if a test begins to fail, it can automatically bisect the history to find change, and notify the author.

You could also get these benefits with a Bazel like build system which can cache test executions so others don't need to rerun them if the binary is not changed.

LLMs will make tradeoffs without consulting you. This is sometimes a feature and often a bug. Many times a decision made up front can have non obvious implications down the line. This is one of the biggest values I bring to be table as a more experienced swe.

I think if I fully understand everything the agent is going to do and trivially verify the output quality, LLMs are a win. For one off prototypes, the same is true. For software which is unique, complex, and performing a task which has not been fully specified I find myself needing to drop into my editor more and more.

Recently I've been moved to a research focused team and I really need to have proof that something is happening. I've had Claude Opus 5 gaslight me by telling me it did something and when I read the code it obviously did not. This happens more and more with my tasks that are kind of complex.

I've found a lot of the claims by AI people have been 6mo - 2 years "ahead" of my experience. I think we are now in an era where harness engineering is highly valuable (making a test, looping an agent, manually annealing with new ideas) but the claims that no one writes code manually seems like it may not be fully there for all code.