# Choosing the right task matters more than adding compute Markdown copy of https://realalpha.app/s/choosing-the-right-task-matters-more-than-adding-compute/ on Real Alpha, the best minutes from long interviews with the people building AI. Cite and link the page, not this copy. - Speaker: [Diogo Almeida](https://realalpha.app/builders/diogo-almeida/) · Diogo Almeida is co-founder and CEO of TypeSafe. · Built Jev, a decision model returning typed probabilities; co-invented RLHF and InstructGPT at OpenAI. - Host: [Sam Charrington](https://realalpha.app/people/sam-charrington/) · Host, The TWIML AI Podcast - Show: The TWIML AI Podcast - Episode: [Asking JEV's Creator the Hard Questions About This Hot New AI Model](https://realalpha.app/v/9Aato-NfjoU/), Oct 6, 2026 - Moment: 8:02 to 12:19 of the episode (4 min), playing on https://realalpha.app/s/choosing-the-right-task-matters-more-than-adding-compute/; the episode from there: https://realalpha.app/v/9Aato-NfjoU/?t=482 ## Quote > “And more important than data is **you need the right task**. So this is the most important thing” > — [Diogo Almeida](https://realalpha.app/builders/diogo-almeida/) at [9:14](https://realalpha.app/v/9Aato-NfjoU/?t=554) · The TWIML AI Podcast ## Takeaways 1. The task you optimize for is the most important choice. 2. Data is often a tighter constraint than compute. 3. Major ML gains can begin with creating a new task. ## About this segment Diogo Almeida argues that progress depends first on choosing the right objective, then gathering data to train for it. RLHF took off because it created a new task and the data to solve it. From “Asking JEV's Creator the Hard Questions About This Hot New AI Model”, Oct 6, 2026. [Watch the full episode](https://realalpha.app/v/9Aato-NfjoU/) ## Transcript Transcribed by machine (Whisper) from the episode’s audio, so a word may be off here and there. [8:07](https://realalpha.app/v/9Aato-NfjoU/?t=487) I love this. Have you seen my blog post on the bitterest lesson? No, tell me about it. I can just explain it. So there's Sutton's bitter lesson. I'm paraphrasing to be useful for me. So this is not exact, but roughly I'm simplifying. It's hard to simplify actually good writing, right? So I'm simplifying a bit to be beneficial to my narrative. Everybody else simplifies the bitter lesson. So you might as well too. [8:30](https://realalpha.app/v/9Aato-NfjoU/?t=510) Yeah, I might as well too, but I just want to emphasize that there's actually nuance to it. um but here it is so it's roughly speaking that compute and scaling matters more than algorithms and this is bitter because ml people love algorithms it's always the sexy thing to work on it's fun it's cool this is what they've studied um and it's bitter because you know like who wants to do that systemsy stuff and like getting gpus is hard and especially in the back in the academia days, you didn't have that much compute. And this birthed an entire era of scaling-pilled ML practitioners for mixed reasons. I actually think that there's pros and cons to being scaling-pilled. [9:11](https://realalpha.app/v/9Aato-NfjoU/?t=551) But I believe that data matters a lot more than compute. So you actually need data to put the compute on. And more important than data is you need the right task. So this is the most important thing in all of the all of the ml that we do and the right task is kind of the north star that you get what you optimize for you know something like pre-training loss pre-training loss is like maybe obvious in hindsight it was quite a it was quite a big leap of faith that pre-training loss would lead to such awesome ai stuff and was by no means guaranteed so it's actually a rare few that i think can actually make a new task and that is the you get what you optimize for part of it And if I could add slight nuance to Sutton's take, in the case of games, there's no question what the right task is because that's constrained by the rules. [10:02](https://realalpha.app/v/9Aato-NfjoU/?t=602) And there's no data for games because you're doing on-policy RL such that you get data from compute by doing rollouts. So I would say that in the case of RL games, Sutton's bitter lesson is the equivalent one of what I'm describing. but by extension in real world uh application data is the constrained resource more so than compute figure out the direction of the data as well so um i won't talk out necessarily about jav and rlcd right now because you know it won't be fine we can but let's talk about rlhf right when we made there's many definitions of rlhf there was the original rlhf that paul cristiano did to teach robots to backflip. No LLMs involved. There was another RLHF learning to summarize where some people in the OpenAI team, I think Paul also was pivotal for that, taught the models to [10:57](https://realalpha.app/v/9Aato-NfjoU/?t=657) summarize, like a GPT-2 size model, to summarize text better than humans did. I'm not sure about that part. And then there's RLHF, which is what everyone refers to now. which is creating the task of instruction following, like teaching the models to chat and follow instructions. So when I refer to RLHF, this is a task that is being done. And before RLHF existed, there was literally no human feedback data on the internet of the shape that we were looking for because we had to create it, right? [11:30](https://realalpha.app/v/9Aato-NfjoU/?t=690) Like why would anyone have like preferences between two completions from language models that were somewhat on policy? Because that's such a weird thing to have. So it actually took some genius from the team to realize that this is a direction to go down. And once you have that direction, first off, when you're in the early days of the direction, you are not sure it's the right direction by definition, right? [11:51](https://realalpha.app/v/9Aato-NfjoU/?t=711) Like it's not yet working. You need like a lot of data compute and algorithms to make it work. And you just have a strong conviction that it's the way to go. But eventually you get the data and all the other stuff, but the data needs to be gotten from scratch. And that eventually becomes something phenomenal in the world. And that tends to be how you get like the gigantic jumps in ML. You know, chat to GPT was a gigantic jump. [12:15](https://realalpha.app/v/9Aato-NfjoU/?t=735) O1 was a gigantic jump.