zachthewf

I'm open-minded to arguments about AI being a financial bubble and a bad business.

I'm not open-minded to arguments about utility, given that I personally witnessed LLMs evolve from interesting but useless toys to insanely helpful tools I use every day.

I didn't read the posted article (I don't read this author anymore because I think it's basically anti-AI ideological propaganda).

But from the article I linked back in March 2024:

"Generative AI models are expensive and compute-intensive without providing obvious, tangible mass-market use cases. Murati and Altman's futures depend heavily on keeping the world believing that development and improvement of their models' capabilities will continue a rapacious pace of progress that has unquestionably slowed, with OpenAI admitting that GPT-4 may be worse on some tasks.

As I've written before, hallucinations are a feature not a bug. These models do not "know" anything. They are mathematical behemoths generating a best guess based on training data and labeling, and thus do not "know" what you are asking it to do. You simply cannot fix them. Hallucinations are not going away."

Since then:

- hallucinations are dramatically less of a problem

- several mass market use cases have emerged, most notably coding

- rate of progress has increased

Before you spend 20 minutes reading this article, it's worth understanding that the writer has been posting popular but consistently wrong takes for 2+ years (e.g. https://www.wheresyoured.at/peakai/ from March 2024) arguing that AI is failing, is a waste of money, is bad, will never work, etc.

Biohub is funded by Zuck and his wife - the full name is "Chan Zuckerberg Biohub"

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

or maybe the nutrition guidelines just don't matter that much.

I like the look and brand, but the animations/clickjacking are really jerky for me. Are you not having any issues with that?

Decamillionaires complaining about billionaires. A story as old as time...

No it’s a diffusion model trained on proteins

It’s not that simple. Even if you take the cynical view that the company is your adversary, the other people who work at the company (including founders, investors and execs) are actually playing a career-long collaborative game rather than a one-off prisoners dilemma.

They crossed the hurdle once but are less willing to do so repeatedly (so they churn)

Maybe. FWIW the majority of the apps that have taken off are basically AI girlfriends/AI boyfriends. Using these is fundamentally a shameful act which probably deflates retention.

There may be high retention use cases that find ways to serve user needs without embarrassing them.

We know they don't work at OpenAI or Anthropic, but beyond that have no information

Sonnet is noticeably worse in my opinion. It’s worth it to spring for Max and only use Opus

Probably glasses or airpods.

There are certainly slow-moving ecommerce platforms that I'm sure would be happy to take your business. Yet Shopify has the best product. Why do you think that is?

The argument is that vibe coding is great for little personal programs but bad for full-blown products.

I don't think you can ship a fully baked product made exclusively with AI coding yet. But it's *really* useful for investigative product development - e.g. when you have a few different ideas for how something might work but aren't quite sure what's best. I used to regret that I never mastered Figma or other mockup tools - now I never will have to.

If you're an accredited investor (make sure you meet the financial criteria) you can cold email seed/pre-seed stage companies. These companies typically raise on SAFEs and may have low minimum investments (say $5k or $10k).

YC lists all their companies here: https://www.ycombinator.com/companies.

Many companies are likely happy to take your small check if you are a nice person and can be even minimally helpful to them. Note that for YC companies you'll probably have to swallow the pill of a $20M valuation or so.

And often when YC invests in multiple companies in a space there are multiple winners:

- Gusto, Rippling, and Deel

- Mixpanel, Amplitude, Posthog

Congrats on the launch!

He went to Yale and worked at a prestigious VC firm. Most people with those credentials are at least reasonably smart.

what do you think people should do when their countries experience hyperinflation?

What company are you at? I've been working on a product to solve this problem.

If you're up for a chat my email is on my page here https://www.zachocean.com/contact/

Were you able to find good candidates from your post eventually?

I mean, inference costs have decreased like 1000x in a few years. OpenAI is the fastest growing startup by revenue, ever.

How foolish do you have be to be worrying about ROI right now? The companies that are building out the datacenters produce billions per year in free cash flow. Maybe OP would prefer a dividend?

Exactly right. Traditional VCs come up with a thesis and then buy 20% of the company they think will be the winner who fits that thesis when they're at like $1M to 10M revenue (series A)

YC can instead get ~10% of every plausible winner they come across when they're at $0 revenue

What's your use case?

Can't wait for them to move up to Seattle. We're basically San Francisco with slightly easier hills and more rain/occasional ice, so seems like a good candidate.

I love Tesla, I own the stock, but I don't think they can be considered comparable until they have a live autonomous taxi service that regular people can call from their phones and regularly rave about.

Ginormous market, 10x better product, no competition is currently close.

Plus although they have the capital cost of owning the cars they don't have the COGS of paying the drivers so presumably much higher margin per ride.

Cool, looks like this is trained on 16 million hours of audio (500B tokens at ~.11 seconds per token).

Even the large open source TTS models (see F5 TTS, Mask GCT) are mostly trained on very small audio datasets (say 100k hours) relative to the amount of audio available on the internet, so it's cool to see an open source effort to scale up training significantly.