Play out the most likely pessimist’s scenario: LLMs are useful but frontier models are overkill so businesses just use dirt cheap open weight models on their own hardware and/or they rent hardware instead of paying per token. Then what for OpenAI and Anthropic?

OpenAI’s business collapses if customers are happy with an LLM that costs $0.10 per million tokens even if it only costs OpenAI $0.05 in inference per million tokens. The insane bonkers claim from Garry Tan that in 2 years we will be using 90,000x as many tokens as today is… well, obviously not true.

The fixed costs that OpenAI and Anthropic have created need inference demand far beyond what is plausible.

edit: and hand waving away the vast losses of companies like OpenAI because of “training” is ridiculous. Anthropic are spending a billion dollars per month to rent additional capacity from xAI for inference, not training. The models don’t need to get better: if there is a case for LLMs to change business forever, GPT 5.4 is just as capable of achieving it as GPT 5.5.

"Corgi builds insurance structures that allow us to best serve the needs of our customers. For technology companies, operating a technology liability line through a Risk Retention Group is not unusual, improper, or exotic; it is a standard insurance structure for specialty liability risks where similarly situated businesses benefit from tailored underwriting, specialized coverage, and risk alignment. The suggestion that Corgi customers are unknowingly taking on “balance sheet risk,” member-assessment risk, or responsibility for unrelated insureds’ liabilities is false.

Your draft’s statement that “Corgi will help you share that risk,” combined with the question whether customers understand the risk of other companies in the group, does not merely describe RRGs in the abstract. It falsely implies that Corgi leaves customers exposed to open ended financial liability for other insureds. That implication is defamatory and false. RRGs are regulated insurance carriers subject to financial, reserve, governance, and regulatory requirements. They are not informal risk sharing clubs where policyholders unknowingly become responsible for each other’s balance sheets.

The RRG structure unique to Corgi. Major insurance groups use different insurer structures for different classes of risk because different risks are best served by different structures. Berkshire Hathaway, which the draft itself invokes, has affiliated insurance operations involving Risk Retention Groups in specialty liability markets, including medical and legal professional liability. That underscores the point: RRGs are a widely recognized insurance structure for specialty liability lines, and allow insurers to provide more tailored coverage options rather than issuing a generic policy.

The draft’s statement that Corgi “innovated with AI in a regulated industry by cutting corners” is also false and defamatory. Corgi raised millions pre-revenue and spent nearly two years building and obtaining regulatory approvals for its insurance operations, including approvals and requirements relating to reserves, pricing, liquidity, governance, and compliance. That is the opposite of “cutting corners.” Any allegation that Corgi used AI to evade regulatory approval, underwriting standards, reserve requirements, pricing controls, liquidity controls, or other compliance obligations is false.

Any article suggesting otherwise, including by implying that Corgi misleads customers, conceals the RRG structure, exposes policyholders to undisclosed balance-sheet risk, or uses RRGs and AI to evade proper underwriting or regulatory obligations, is false and highly damaging.

To be clear, if you publish these false statements or defamatory implications, Corgi will sue you personally and will pursue all available claims and remedies against you and any other responsible parties. Corgi has enforced its rights before and will do so again. You should not mistake this for an abstract legal reservation."

Comments URL: https://news.ycombinator.com/item?id=48697953

Points: 6

# Comments: 0

I guess when you're working 7 days a week you've gotta find something to fill the time with.

A rare but sensible insurance tech startup would use external underwriters and reinsurance and provide insolvency protection.

Corgi doesn’t have any external underwriters, doesn’t have any insolvency protection, doesn’t have any reinsurance.

I think they’re bad on all 3 points, not just the underwriting?

A startup raises ~$100m at a ~$3bn valuation and forms a team of 12 employees including their Head of Operations to build a clone of a product they pay less than $1,000/month for while they have more than 50 open roles they are hiring for.

Hmmm, yes, a very good use of available resources.

https://xcancel.com/SergioGarc20223/status/20702512486962956...

Delve’s first drama was around copying from other startups, it was later that their betrayal came out. Corgi is currently at the copying from other startups stage… one might choose to believe there is a path they’re following rather than this being a one off.

For example, I outlined in another comment how their product is not what it seems, it is not traditional insurance, it takes advantage of an esoteric piece of insurance regulation. They’re doing very aggressive underwriting without any of the traditional insurance regulatory protections applying to them.

https://news.ycombinator.com/item?id=48672328

Someone might believe that their conduct + very high risk product + exposure to a large number of YC companies means they’re very similar to Delve.

Plus the founders are at the top of another funnel… Forbes 30 under 30. 30u30 is practically a kiss of death.

Normally getting insurance from a startup like Corgi would be a very bad idea because what’s to say they’ll be able to pay out claims? I assume other YC startups are happy because a) they can’t get insurance anywhere with good underwriting b) they figure YC will bail Corgi out when it goes wrong because seemingly every YC startup depends on them.

https://en.wikipedia.org/wiki/Risk_retention_group

“Policyholders should be aware that certain Specialty Insurance Carriers may not be admitted insurers in the state in which the insured risk is located. Policies issued by non-admitted insurers, risk retention groups, captive insurers, and certain other Specialty Insurance Carriers may not be subject to all of the insurance laws and regulations of your state. State insurance insolvency guaranty funds may not be available for policies issued by non-admitted insurers, risk retention groups, captive insurance companies, offshore insurers, or other non-admitted Specialty Insurance Carriers. In the event of the insolvency of such a carrier, policyholders may not have access to state guaranty fund protection and may bear the risk of the carrier's inability to pay claims.”

https://www.corgi.insure/disclaimers

Papermark is an open source alternative to DocSend. Papermark is very popular, as it is a much more cost effective alternative to DocSend — self-host or hosted.

Corgi is a YC backed insurance startup that sells insurance to other YC startups. Nico is a founder. Recently they raised $100m at a ~$3bn valuation. They’re one of the darlings of YC right now, endless fawning over them.

Since insurance underwriting involves lots of documents, Corgi were paying Dropbox thousands of dollars per month for DocSend. For some reason, Corgi ostensibly formed a team of 12 to build their own DocSend alternative, called Dataroom. And Corgi decided to make it into a SaaS product, pitched as a cheaper DocSend from just $10/month, in an already crowded space.

Papermark noticed immediately that Corgi’s Dataroom used a lot of identical language and structure that Papermark’s open source product does. Papermark assumed that Corgi had taken Papermark’s work without attribution. Corgi have denied it, claiming it is just a coincidence that there are word for word matches between the products.

Another YC startup, Delve, got caught doing what Corgi are accused of (and much more) which led to their removal from YC.

That doesn’t make any sense, it doesn’t add up. Have you seen how much money they’re raising and burning? We know that training does not cost tens of billions.

Brockman said OpenAI expects to spend $50 billion on compute this year. OpenAI’s revenue run rate is less than $50 billion for this year! For 90% margins to be possible on inference, you are suggesting that less than $5 billion of that compute spend is inference and over $45 billion of that compute is training.

Anthropic have been desperately trying to juggle capacity by shaping user behavior through peak time usage limits because they are struggling with capacity for inference.

Plan based usage is widely acknowledged to be subsidized, you are probably the only person on earth suggesting that plans are profitable.

Money is made on the subset of inference that is charged at cost + margin via their APIs. API usage is so high because customers are still finding their feet, trying to understand how to measure the value they get from their spend, erring on the side of spend.

Yes, in a world of unmeasured value and tokenmaxxing, inference is profitable on SOTA models because all capacity is being consumed at all times, driving down marginal costs, but what about a world in which capacity isn’t constrained? There are still huge fixed costs.

Even the most optimistic leaks with the current high prices put the margin on API token inference at around 50%. How can SOTA models ever come close to competing on price? Price always matters. Offering the best model with the most brand recognition does not exempt OpenAI from the basic rules of business.

Historically, software has been such a successful business because the margins are incredible, 95%+ in many cases, driven by direct measurable value to customers that dwarfs the cost. A 50% margin at a time when your customers are falling over themselves to spend as much money as they can is not a good sign, it is a very bad sign, it leaves no room to ever achieve traditional technology margins, and inevitably leads to very weak margins.

Inference needs to become an order of magnitude cheaper than the value it delivers to ever have a chance of delivering on this wildly profitable vision. The cheap model providers have a much better chance of achieving that.

Outside of coding, almost every business case for AI doesn’t need above human intelligence, it doesn’t even need human intelligence, or half a human intelligence, a business can extract a lot of value from a machine that has a fraction of a human’s intelligence. Most human work does not use our intelligence, it is rote, a monkey could do it, and that’s where AI will be used most. Who is going to pay $10 per million tokens when they could pay $0.10 to get the same outcomes?

https://warcraft.wiki.gg/wiki/Server_blade

https://en.wikipedia.org/wiki/Blade_server

1. Energy is very expensive relative to the U.S. 2. Houses are old old and retrofitting air conditioning is very difficult 3. Houses are more than 1 story with many small rooms making portable and window mounted units unsuitable for a whole house

All modern apartment buildings in London are built with air conditioning because a central air system and district power make it cost efficient.

If you visit a hot place like Dubai or Bangkok, there are endless indoor malls with air conditioning that serve as a place to shop and a third space. Much of Europe doesn’t have that.

The U.S. specifically is also very car-centric. You walk out of your air conditioned house into your air conditioned car and drive to your air conditioned mall. Much of Europe… isn’t. People walk, you can’t avoid the heat.

Yes, certainly, there is a cultural resistance to air conditioning but adding air conditioning to homes isn’t going to stop people dying, homes are the least consequential part of heat in day to day life. Health advice in a heatwave is pretty much: don't go outside.

“I think the cause was that Workspace and certain leaders (and projects) were afraid of being disrupted.”

Suggests that there is much more to it. I suspect it’s actually about disregarding Google’s internal processes (which is forgivable) and then demanding to work unilaterally (unforgivable). The amount of positive feedback may have given the author too much confidence that he could dictate to leadership what comes next.

A Google Workspace CLI is a useful project idea but it isn’t groundbreaking, it’s something that the Google Workspace team should be involved in. I suspect he just wanted go steamroll over them. Shipping stuff in a team is never about just producing the code.

And anyway, orbital data centers have no chance. They’re just like the Mars colony, an Elon boondoggle.

The U.K’s highest earner for a few years running was the founder of a U.K. betting site, she had something like a 500 million salary and there is an entire town’s economy supported by her business.

https://en.wikipedia.org/wiki/Bet365

I found the section titled “Local feature branches. Server main. One squash.” most interesting.