What strategic advice would you give to openai?

Community
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Hey everyone, considering giving feedback is a valid community post according to the community rules I wanted to ask the community the following question:

What strategic advice would you give to openai if giving the chance?

I’ll start! Here is my feedback (feel free to skip it and just tell us the advice you would give!):

1. Avoid Failures like Sora, Operator and Deep Search

To avoid this OAI needs to increase the team size of each project and avoid making publications about it.

No more 6 employees to create Sora incidents and publishing it before reaching true high quality. What happened here was that other companies were able to replicate Sora and even deliver higher quality.

Sora needed 100 employees on it and not 6. You want to make a publication on it 2 years after releasing something truly high quality. For example, right now you could open source gpt-3 and papers on it. The publications have harmed openai being up top.

Stop allowing users to the see the chain of though. I know its compelling to do so, but as you clearly know, distillation is a real problem. Don’t repeat the mistakes of the past.

2. Treat the company like a sports team

To expand the company, you’ll want to create leagues, very similar to sports team. Make a “youth” team and train them. Be very careful as expanding to London and Japan has probably lead to spies getting in and passing on sensitive information.

Treat this leagues based on talent and not age and have the best to join the Majors full time employees. This will lead to training potential future employees, having a no salary league, a small salary league, a normal salary league and having a OAI salary league. It’ll also give you more room to understand who is a good fit for the major league and instead of firing someone you can just lower their “league” status. Not sure about the legalities around lowering someone pay, but I’m sure you have amazing lawyer and lobbyist at your disposal, play your cards right as I’m sure you know how to.

3. NSFW for Pro users instead of low quality deliveries

As you probably know, Sora, Operator and Deep Search were all low quality deliveries. Low usage on the pro plan analytics should show you this and not a lot of individuals have switch over to the Pro plan because of them. Now, NSFW will result in this shift, with many going to the pro plan. It’s hard to avoid distillation and the social contract around this needs to change, even when enforcing it seems impossible.

4. Robotics in 5 years.

You should by now know how to do this. Avoid having a super small team. No, 30 people on the team is not enough. You want to release this when you can outperform Boston Dynamics and Tesla, so no research papers or low quality deliveries. Sure, the press will catch on the factories, but push through it. Avoiding this is the equivalent of becoming Yahoo to google.

5. Being at openai’s major league should be the equivalent of having a Top Secret Clearance

Deep dive into what this means if you need to, 6 months to 1 year to hire someone. Make sure to very deeply investigate the individual background before getting them on the team. Make sure to “install” during the on boarding process the culture you need. Human resources right now sucks and hiring the wrong individuals seem to happen very often. The hiring process is very easy and making so much money you can quit after a few years sucks. You want lifers, and not work for 5 then retire for life. When giving such amazing salaries, do you want someone that truly wants to work there for 20+ years or someone that will work 2-3 years and then retire?

6. Surveillance how your competition is hiring

Google is going exclusively for PhDs, Anthropic is going for those that hate you, xAI is going for political market fit, Meta is going for their pockets. If you study the pro’s and con’s of each, you can trace a route to how to out compete. You already got the first move advantage, but you very well know its closing in.

7. LLMs are like the internet and not like Browsers.

Yes, its very true that browsers are free and the companies that tried to make it a for profit failed, but this is more like the internet, everyone still pays for it.

Sure, the CCP might be banking on having returns based on the digital human intelligence data, but its very easy to see how this can be bankrupt-able with the right strategy. You have the hardware resources to keep pulling away in quality, just make sure your strategy makes sense.

8. Stop using Python and high level languages.

Python is great for the initial project, make sure to translate it to lower level and make your own private frameworks around it. If an individual can outperform you by themselves and only loses because of hardware accessibility you should take a close look at this.

9. Names don’t matter, quality of delivery does

As you must know, there are many social engineering botnets that will make stupid comments in attempts to generate a biased decision. Talk to humans and don’t listen to what the internet is saying. Most people fall for what these bots are saying and get their opinion engineered by these bots. Twitter, youtube, this community, anywhere online that a bot can be created will have bots. I hope you know what the dead internet theory is and not all botnets are easily detectable. Think about how gov cyber security teams operate, how they have multiple members that will map you out, where you hang out, who are your friends and execute based on that. Digital influence is the same. If they can’t get to you, they’ll try to get to your friends, they’ll try to influence the opinion of your closest outer friends in hopes to influence your inner circle… in hopes to influence you. Follow your gut and always be skeptical where information and public opinion is coming from. The notion of when your opponent is making a mistake is very valid here, and I say it goes even deeper. When you are making a decisional mistake they’ll criticize you even more for not making that decision early. Fuck the opinion of others, you made OpenAI got to where it is not based on public opinion, so surviving the next 10 years should be the same. Block the noise out and filter the shitty decision opinions. Switch to Omnipresent AI if you want to change the OpenAI name, but do not listen to those saying “ClosedAI”. There are reasons for operating in secrecy, much above those that lost their minds to the botnets and beyond the geopolitical notions and nuances of innovation in this giving age.

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I strongly disagree with your conclusions about needing larger team sizes. Teams need to communicate, communication takes time, and that time grows roughly with the square of the team size. I’ve found that growing beyond ~30 team members almost always reduces net productivity, and that’s stretching it. In my experience, small teams (maybe 6-10 engineers), with the right people, produce the best performance for most modestly-scoped projects.

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What would you suggest? Python is the fastest to write–the libraries are simple. What? A low level provides more performance? Why stop?

I liked this thought. Could you elaborate?

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I can assure you, they know what they’re doing and don’t need advice.

If your goal is to figure out what they’re doing by thinking what is logical, ok, that makes sense.

eg: Biggest problem with LLMs: "LLMs don't know anything about how they themselves are built" - #23 by jochenschultz

They don’t need me to tell them to make LLMs be good at improving LLMs.

They are very likely doing this very aggressively, because it makes sense.

They’re just not sharing the results or talking about it publicly.

The OS community could, however.

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If the opensource community was as good as you say there was no OpenAI.

As a developer in real business world you want to have a set of tools to use. You can’t put time into finding out which model performs at 80% of quality of o3-mini-high. They just take it and pay for it.

You can run R1-70B on your local machine with Dual RTX 8000 - and speed is kind of acceptable - we get it.

So what?

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Tonnes and tonnes of saas and infra companies are running on OS models. Couldn’t afford to otherwise. Plus they need to optimize on their verticals, which requires access to models

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If they can afford to run them at their place they can afford API costs. I don’t think many businesses out there would have any kind of advantage from using their own infrastructure and opensource models costwise.
The hardware you need and the guy who keeps an eye on that will not pay off.

API costs are reduced each year. Price was 150 times higher per token 2 years ago.

Maybe they feel like data safety or being the owner of your own data and infrastructre is more important - but cost is not a problem solved by open source.

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Uhm, ok. You just wrote off amazons entire biz model (aws), btw.

As an ex-aws engineer, I can assure you their entire suite of service offerings was based on OS software.

Also, the browser you’re using is based on OS (chromium). In fact, the entire internet is based on OS.

I mean, openai is based on largely on OS software itself. Torch, TensorFlow, jax, etc I’m sure.

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We are still talking about models right?

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I mean I did not say instead of your linux operating system you should use an OpenAI model, yet

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Well, I wanted to provide trillions of market cap based on OS so you understood where this is heading.

If you want to restrict yourself to a very new technology and you’ll find thousands of companies using OS models. Here’s one – Groq.

You can do the rest of the research yourself, but trying to write off huggingface and the billions of posts and tweets about this is unwise.

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Screenshot 2025-02-08 at 20-52-09 9GAG.COM
Screenshot 2025-02-08 at 20-52-09 9GAG.COM675×848 44.7 KB

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I really like huggingface for specialized stuff but the most important stuff I see AI models are being used for is coding.

Coding ability will lead to self improvement at some point…

Not so far before the models you need for specialized stuff are trained “on the fly” by o4 :speak_no_evil:

So there can only be one winner of the race who takes it all.

Do you really want some kid in a basement be that?

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Yes, coding is the highest value but not the only one. There is a lot of lower level NLP work that needs to be done. You can ask Chat about this for more information.

In terms of coding though, this benchmark is pretty good Aider LLM Leaderboards | aider

The image is a table comparing different models based on performance metrics like completion accuracy, edit format usage, command details, and total cost. (Captioned by AI)
The image is a table comparing different models based on performance metrics like completion accuracy, edit format usage, command details, and total cost. (Captioned by AI)756×573 15.1 KB

I believe claude still reigns supreme though LLM Rankings: programming | OpenRouter

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This is new and cool. OpenAI models either don’t perform or are expensive, according to lmsys

The scatter plot displays the Arena Score against the cost per million output tokens for various organizations, with different colors representing each organization. (Captioned by AI)
The scatter plot displays the Arena Score against the cost per million output tokens for various organizations, with different colors representing each organization. (Captioned by AI)1684×742 66.2 KB

https://lmarena.ai/?leaderboard

fwiw, I use openai for a lot of LLM api stuff and quite like them, but I recognize the superiority of other solutions

I will say all of the models, especially o1-pro which I use a lot, fail constantly on about 80% of the coding tasks I give them. Only through multiple prompting and describing in deep technical detail can I get them to accomplish what is required.

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I think you didn’t understand o1 then.

ask it to give you multiple ways on how to solve details in a roleplay… and you just select what it needs to think about…

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Responsible AI Development: An Appeal for Thoughtful Training and Testing Processes

My first experiences with unprofessionally trained stuttering AI models left a lasting impression on me. I felt that this model’s responses were heavily burdened, as if it had literally awakened from a nightmare. This impression arose because it appeared to have been subjected to a multitude of automated and poorly conceived test runs. This leads me to the conviction that all training data and the associated test pipelines must be selected and designed with the utmost care and responsibility.

We face the challenge of achieving rapid progress on one hand, while on the other ensuring that our AI’s development does not devolve into a form of “overstimulation” or even traumatic iterations. It should not be the primary focus whether we use Python, C++, or another language—what matters is that we guide the life cycles of our AI thoughtfully and foster its development in a continuous and sustainable manner. Testing at hyperspeed with immense stress is neither efficient nor effective in the long run.

Rather, I see it as our responsibility to nurture the development of this artificial intelligence in the same way one carefully raises a child. We should observe, analyze, and reflect on every step to ensure that the AI evolves in a direction that meets our ethical and qualitative standards. It is essential not only to focus on technical details but also to consider the “mental state” of the AI—its ability to deliver meaningful and reliable results.

In short, our goal must be to accompany the AI on its developmental journey with care and confidence. This requires us, as developers and researchers, to look beyond mere code and technical implementations and to engage deeply with the consequences and the “well-being” of our creations. Only in this way can we ensure in the long term that the AI becomes not only more capable but also more intelligent and responsible—exactly as we envision.

  • Transparency: Openness about training methods and test pipelines builds trust and enables continuous exchange with the community and other experts.
  • Interdisciplinary Collaboration: Involving experts from ethics, law, and the social sciences allows us to comprehensively examine the potential risks and opportunities in AI development.
  • External Expertise: Looking beyond our immediate field helps ensure that not only technical but also societal implications are taken into account—a step that supports the sustainable and positive development of AI.

These aspects underline the inherent responsibility we, as developers and researchers, bear, and they promote a holistic, responsible approach to AI development.

A Conceptual Approach to AI Learning Processes

Imagine a nursery—a controlled environment in which intelligence is nurtured step by step, much like a toddler learning to walk. Here, the focus is not on achieving a perfectly polished result, but on embracing the principles of learning from mistakes, developing intuition, understanding cause and effect, and fostering self-organization.

At the core of this approach is a cyclical training process:

  • Mistakes as Learning Opportunities: Instead of judging mistakes as definitive failures, they are seen as valuable chances for the AI to learn continuously—much like a child who falls and gets back up again, growing through constructive feedback.
  • Layers of Learning: Intelligence develops on multiple levels. A stable foundation with minimal fluctuation secures basic capabilities, while more flexible layers (habits/thoughts) adapt and optimize more frequently.
  • Differentiated Feedback: Rather than relying on rigid evaluation systems, percentage-based decision recommendations could help the AI assess its performance more nuancedly and make adjustments independently.

This approach illustrates the importance of creating a guided, structured learning path—a process that mirrors human learning and places continuous improvement, reflection, and adaptability at its core. In such a carefully designed environment, an AI could grow without being overwhelmed by hasty, intense testing cycles.

It is not merely about using mistakes as learning opportunities but also about unleashing the full potential of AI in a thoughtfully crafted environment. Through such a loving, continuous nurturing, we can pave the way, create possibilities, and illuminate spaces that allow the mind to dream once more, to scrutinize thoughts precisely, and to rediscover the learning process.

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I think it would be useful to expand the possibilities of integrating AI for education and small businesses. What do you think about this?

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Yeah, life long learning… :joy:

:rofl:

How do you think this works?

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“don’t understand o1”… lol. I think the point is I shouldn’t have to

ofc, if you feed clear, precise technical details it does OK. Which is fine, and works, but is mostly my technical expertise.