light_triad

It's too bad they didn't make it they had quite a bit of name recognition especially in SV.

But basically it's a tarpit idea (TM YC) where everyone passionately agrees that hiring is broken but there's no straightforward alternatives. It's basically a services business disguised as a tech play that doesn't scale, an attempt to disrupt a high touch sales process (and there's a reason that these processes run the way they do even though they are subpar).

Also specifically with hiring it's extremely fragmented and every company has their own internal method and preference - or rather voodoo magic they like to use.

> But after seeing how quickly A.I. was improving, he shortened his timelines. Now he believes there is a 50 percent chance that A.G.I. will arrive by 2027

Wow the guy made almost $2M making "predictions" - where do I sign up? :)

But more seriously, it's unfortunate how the NYT and major news outlet always seem to decide what the narrative should be first and then find a few folks that will fill in the blanks. It's lazy and hacky journalism at best. Once you see it you can't unsee it.

It's the same as selective universities telling every student that they should apply regardless of their chance. It makes sense they would do this since it's completely open.

It's not really a lottery, more like a messy matching algorithm for supply and demand. They give everyone a shot since they look at everything, but getting in is not evenly distributed :)

It makes sense that startup folks will gravitate to where there's VC interest as they always have (including the fakers and scammers). But regardless of the current hype cycle the same rules still apply: the reasons why you're doing the startup, if you're an expert in the space, whether you can build a 10x solution, your access to early adopters, your unique point of view on the market. AI is just a new set of tools to generate more value for your specific users, if applied in a unique way that makes a significant difference.

The whole saga is an interesting case study for theoretical versus real power.

On paper the board has the power and it’s their job to monitor and hire/fire the execs. But they’re dependent on the execs for info. Also having one investor own half, plus the founders guiding the roadmap and having sway over key hires leaving is where the real power is…

> We partner closely with X Corp to bring our technology to more than 500 million users of the X app.

Investors paid about $12 per X user/bot.

Interesting they are opting for a spinoff rather than doing this in house. Perhaps to capitalise on the hype and attract researchers who don't want the baggage of being associated with polarising brands?

The main draw to starting a startup is not to get rich from the salary, but from the equity. Founders are not really equivalent to employees, they create value from nothing and revolutionise industries, if they're doing it right.

It all comes down to the classic question: do you want a high chance (a%) of getting a decent amount of money ($b) or a very small chance (c%) of getting an enormous amount of money ($d)?

The values of a, b, c and d are different depending on your background, experience, connections, hunger for risk, etc. You have to find the decision thresholds that make sense for your situation.

There's been a paradox at the heart of the tech revolution - the world's information is more easily available than every before, for free and at everyone's fingertips. However it's making it harder to filter through the information overload, the low quality content, the misinformation, the true and the false.

There's a confusion between quantity and quality. Your skills and ability to differentiate need to increase with the tech - even though consumption is always getting easier. Also understanding the limitations of these AI systems, which relatively few people do relative to how many use these tools now (also Social Media is largely to blame for the general decrease in content quality, even though it's one of the best distribution systems ever created).

There's many factors that make Europe less competitive than the US: small fractured markets, low salaries, high cost of living relative to wages, Brexit costs, fear of being labeled a failure, working to live, focus on worker's rights, stability, and pensions, general risk aversion etc.

A big issue is there's a lack of mega successes high margin software businesses that pump knowledge and money back into the startup ecosystem. From a cultural standpoint, some countries like France are also obsessed with credentials and what you could call a notion of 'innate superiority'.

The end results is a VC ecosystem filled with young finance types that went to locally famous schools with little experience in startups that deploy small amounts with deal terms that would be considered absurd in the US.

In many ways 'consciousness' is a human projection onto machines. The whole debate might be, in some sense, irrelevant. Here's 2 scenarios:

- Your robot companion gets so good at mimicking a human, knows all your preferences and is able to have natural conversations, that over time you forget you are interacting with a robot and start to develop feelings for them.

- A robot swarm interacts with the environment to further its goals and continues to grow long after humans have disappeared from the scene.

In both cases is there consciousness? Does it matter?

Agreed it's not possible from the footage to draw any definite conclusions - the whole exchange and portrayal is deeply flawed. I wonder if anyone has seen some scientific papers with a reproducible methodology that look into the deep differences in cultural framing along those lines?

The problem with consciousness is that it’s both a vague term that’s been difficult to define and it has social roots that are not really universal.

The debate reminds me of a sequence in Werner Herzog’s 1972 documentary ‘The Flying Doctors of East Africa’ where multiple people are asked to point at a picture of an eye. Some have difficulty with the task. Even though it’s not scientific it makes you realize how much of what we see and recognize is a learned behavior through year of social training rather than an innate ability.

Here’s the scene (starts at 40min it’s in German but you can auto translate subtitles): https://youtu.be/MZ3MMEe3Qmk?si=i0ydc3DN3aohnrIO

The hype cycle repeats again this time with AI. There will be a lot of noise and fury (after blockchain, nocode, big data, cloud etc etc). The good part is that consumers will vote with their attention and wallets. Only the products that bring real value based on non trivial insights into specific markets will survive. The rest will slowly die off and be forgotten.

Also a lot of the VC money just ends up going back to the big players that have monopolized the channels to reach customers and the systems to run those ‘businesses’.

Here’s a good overview of the scaling difficulties with AI: ‘Most AI systems today aren’t quite software, in the traditional sense. And AI businesses, as a result, don’t look exactly like software businesses. They involve ongoing human support and material variable costs. These traits make AI feel, to an extent, like a services business’ https://a16z.com/the-new-business-of-ai-and-how-its-differen...

As one commenter mentioned it comes down to having suitable information exchange between the parties. Some VC said no to most big successes in SV at some point.

I recommend watching a pitch competition with lesser known VCs and companies like Pitchforce. You realize how difficult it is to understand what people are building and why it could be big. Most VCs will not be users of the product or experts in the vertical and will use other signals, however imperfect or limited as is the case here.

https://pitch-force.com/

Both super-alignment leads resigned. One way to interpret this is that it's an interesting research topic that has wide repercussions the closer they get to AGI, but it's more of a extra cost that doesn't directly help with monetisation and productisation. The real killer applications are in business verticals with domain specific expertise and customised (and proprietary) datasets, not so much the 'pure' academic research.

OpenAI dedicated 20% of compute to the effort which sounds kind of like Google's 20% side project time :)

It’s partly the late stage effect of the ad supported everything business model. Rage is very effective to generate engagement, tribalism and passion. When major politicians are ready to exploit the us vs them dynamic to their benefit it creates a perfect storm.

Also the Guardian and other news outlets have been driving this where every headline is about how one group or another is outraged it’s getting a bit ridiculous.

If you like history you should check out 19th Century Swiss historian Jacob Burckhardt's collection of essays Force and Freedom - I read it every 2 years or so. It's great summary of the different sources of power in society and how they interact during different periods. Highly recommended.

"In Force and Freedom, Burckhardt reduced the main elements of history to the state, religion, and culture, discussing the hypothetical and actual supremacy of each over the other two. “Culture” comes out best, religion worst in his value system, but the state has its dangers too..."

https://www.encyclopedia.com/people/history/historians-europ...

The series of books published by Haese Mathematics used in the International Baccalaureate (IB) programs are quite good. Lots of worked out examples, practice sets and detailed answers to problems available. You can Google for free pdfs online and print the sections you need:

https://www.haesemathematics.com/international-baccalaureate...

Sam has always been very skilled at dealing with the media: making grand pronouncements, apocalyptic statements, save the word predictions, rag to riches hero stories. Which is fine. It's hard to get anyone to pay attention to anything, and he's got a nice playbook (and product) to get people to pay attention.

The more interesting question, I think: is OpenAI actually a good business? Can they generate the resources they need to meet their goals and keep control, without selling to big tech companies that will derail their plans? Do they have enough of a moat and can they benefit from network effect to make their products more valuable over time, without getting copied? They realised they need a lot more capital then they initially thought. Time will tell if Microsoft is able to take over - see what recently happened with Inflection AI: After raising $1.3B, Inflection is eaten alive by its biggest investor, Microsoft https://techcrunch.com/2024/03/19/after-raising-1-3b-inflect...

Great job staying healthy and active! I think what you're going through is fairly normal. If you are super ambitious, want to start a company and work on side projects, realise that it puts you in sub 1% of the population. Most people around you will not have that same level of drive and ambition, especially in a small town. And that's ok. Also CS programs are 'one size fits all'. Here's some things you can try:

- Grow your side projects, learn a lot and help more people (and make more $)

- Find online communities to join like contributing to open source projects

- Focus on specific activities you like (sports, cooking, movies...) and find folks that share that specific interest, even if you don't have much in common otherwise

- Keep learning more about what you're passionate about online, do cold outreach

- Date interesting people who have very different interests and backgrounds

- Reach out to second/third degree connections working in industries you find interesting (they're always looking for great folks)

- Create a club based on your background/shared interest

Lenny recently interviewed YC's Dalton Caldwell which you might also like: https://www.youtube.com/watch?v=m7LvNTbaqSI

Great they share specific metrics and give a sense of how they evaluate companies, although all First Round articles have this tendency to be so overdesigned it takes away from the content. The discussion on Lenny's podcast is easier to digest: https://www.youtube.com/watch?v=yc1Uwhfxacs

I think the 4 P's is a valuable framework for getting to PMF: Persona, Problem, Promise (Pitch), and Product. Usually startups that don't work have a fundamental problem with a few of these (they try to solve too many problems for too many people in a subpar way - i.e. they don't have a strong answer to: which problem are you solving, for who and why it is better than alternatives?). Startups that are willing to iterate over the 4 Ps have a shot at getting to PMF, those that don't usually will struggle and then die.

Instead of cloning well known sites I would talk to people and try to solve their problems. There's a big difference between people telling you they'll use your product and people actually using it. The bar is extremely high since your product has to solve their problem, it has to be better than the alternative, they have to remember to use it (or you have to remind them) etc. You will learn a ton and might produce something useful. Wish I had done that more especially as a student!

Totally agree. What I mean is many research topics can be very interesting from a technical perspective but don't translate into solving problems that are frequent and important enough to build a business around. In the case of Search many business gurus didn't understand at the time that you can make a fortune with a free product. What matters is who has the traffic and who they're selling it to = eyeball meets ad. Page and Brin initially "expected that advertising funded search engines will be inherently biased towards the advertisers and away from the needs of the consumers" (foreshadowing). Also they tried to sell to Yahoo for $1M so it took time to see the full potential, as you describe.

Hopefully 2 Stanford CS PhD students hacking on their project would be funded by YC today :)

You bring up a good point about starting from the tech rather than the problem. Usually the advice from VCs is to start from the problem and iterate on the tech until you solve it. What was very fortunate for Google is that the tech translated into a great business problem. Open up any CS textbook and 'Search' is always a major section. It was also a great business because the problem is important, frequent, and had not been properly solved by the big players in the mid 90s.

"And then we realized that we had a querying tool..." (Page)

I agree with you I meant reputational risk and the amount of output per level of input. It's hard to be as hungry once you've 'made it' or at least, your drive changes. Arguably at that level you can have more impact as a mentor of a large number of domain experts doing great work rather than trying to becoming one yourself.

As Michael has said in one of the shows getting to PMF is a miracle. If you’re already set for life it’s less risky to play the odds by investing in hundreds of startups rather than starting one. Also there’s altruistic motives in giving back to the community etc.

Great resources - thanks for sharing!

You can also access their 990 form: https://projects.propublica.org/nonprofits/organizations/810...

The critical issues for OpenAI is that structurally the cost of collecting data and training models is huge and makes the previous wave of software + physical business models (i.e. Uber, Airbnb, etc) look cheap to operate in comparison. That makes OAI more reliant on cloud providers for compute. Also their moat & network effect is dependent on a more indirect supply of user generated content. Perhaps there's an advantage of using IP to train on as a non profit as some of the articles above argue.

Thank you for your reply and for the links. Much appreciated.