# AMD ROCm 10: Agentic AI and GPU Optimization

**Podcast:** Chain of Thought | AI Agents, Infrastructure & Engineering
**Published:** 2026-09-09

## Transcript

20 years of attacks can now be tried in 30 seconds.
The quote-unquote AI flywheel is actually picking up steam.
Systems that were built for humans may not scale.
We need to plan for like, okay, 100x of what our engineers or customers may have been using.
Last week, AMD shipped its answer to the friction between silicon and surfing tokens.
an agentic stack where coding assistants know the hardware and autonomous systems rewrite your kernels while you sleep.
With us today is Anoush Elangovan, Corporate VP of AI Software at AMD, back for his third time on the show, fresh off shipping Rock'em 10 and taking Rock'em AI to general availability 10 years into the stack's life.
Welcome to Chain of Thought.
I'm your host, Connor Bronsden.
Anoush, great to see you.
Congratulations on the big week.
How has the kickoff to GA for Rock'em 10 been?
Great.
Thanks for having me again, Connor.
It's the third time.
It's my pleasure.
You're one of the favorites of the entire audience to have on the show.
So it's great to have you back.
Thank you.
Thank you for having me again.
And it's been great.
It's been a celebration of 10 years of Rockham.
And it's a milestone, both in terms of a decade of building and shipping open software.
But also it is a milestone because this is the first time that we are actually...
starting to bring in agentic AI into the fabric of the entire stack.
So the entire stack now is AI enabled all the way from the skills at the top, performance optimization and attainment with Hyperloom, and then the core libraries and the runtime, all of which are agentically enabled.
And then we also have ease of use things with skills and CLI and QI.
That's all.
you know, very easy to integrate into your existing like cloud code or codex or vice versa to actually just use the Rockum TUI that ships with Rockum 10.
I'm excited to dig into details of this because I think a lot of it builds on the conversation you and I had back in March.
We talked about your personal workflows and how you were starting to develop and the pace you were going at.
And we're now seeing that across the industry where we have unlocked.
So much more throughput through agentic infrastructure.
But before we go too deep, I would be remiss if I didn't say a quick word about our presenting sponsors for season four of Chain of Thought.
Sphix, which are delivering billions of reliable webhooks for startups in the Fortune 500.
Walrus Memory, giving AI agents portable, verifiable memory that carries context across apps, sessions, and other agents.
And obviously this whole conversation is going to be highly deep into AI agents.
So you said, Anoush, that...
Rockham 10 is all about enabling agents with Rockham.
What does that look like in practice?
Yeah, so in practice, you know, typically everything, like the first touch point for Rockham used to be someone has to go to the Rockham document site, you know, docs, and then figure out how to install, like type curl this or, you know, sudo app something.
The entry point into Rockum itself now is agentic.
So you can actually just have, if you have your cloud code or your codex, you can just say install Rockum, right?
And so there's skills.
So the skills are already integrated.
And then there are other entry points similar to that that allow you to use your own, there's a Rockum TUI.
So you can just, you know, curl a little binary called Rockum and that will bootstrap.
the entire installation process and ensuring you have the right dependencies and making sure it's, you know, delightful to use because we want Rokum, the entry point for Rokum to be like frictionless and almost invisible.
And then you get to the point where, you know, once you deploy it, you are like, you want to say something like, you know, use VLLM to serve QN3, 3.8, whatever it is, right?
And it goes and finds out where's QN 3.8 weights, how does it bring it in, how does it integrate into your system.
And then it reads all the documentation, it pulls in the skills, it manifests everything that needs to happen for you to have VLLM serving as soon as possible.
And then it also can do performance optimizations when you're sleeping.
So you can run...
a tool called Hyperloom that we have that allows you to just optimize on the fly.
And so you just continue to do that until it gets to a point where it's really performant.
And this can work in parallel, especially when you're not using the system.
We can continue to optimize.
So it's like agentic end-to-end.
Yeah, I think this is just the future of software development, right?
We saw Dario say, what, 18 months, two years ago.
90% or more of code is going to be written by AI and we're there.
Not everyone at this point, but I think anyone who is looking to accelerate in the modern era is not writing code by hand.
We're seeing luminaries who are not writing code by hand.
And we're all aware that the engineering is now about directing agents and we have a lot of work to do to set them up for success, but it's through...
the work of teams like what Andy's doing and many others to enable agentic experience that we can actually let tools take this next step.
And just as we were talking, I had my codex instance.
I said, hey, yeah, set up Rockham for me.
And it's just done so.
And I asked it to highlight a few of the, you know, new innovations that have happened here in Rockham 10 with some like meaningful known issues that have been improved on.
So like improvements around crashes around VLM or Comfy UI.
We're seeing like cleaner foundations.
Obviously, you mentioned Hyperloom.
Let's talk a bit about some of those innovations.
What does it look like to develop with Rockham today compared to where we were a year or two ago or maybe 10 when we first got started?
Yeah.
So I think 10 years ago, it probably is like, you know, very nostalgic, but it's probably it's changed quite a bit, right?
Even two and a half years ago or three years ago when I joined AMD, it was a, I'd say, reasonably rough experience to install, especially the kernel mode driver and all that, right?
Now we've made it completely seamless, including pip install, virtual vnv installs.
So the ability to consume Rockum has just, just baseline has improved, right?
But then what I'm really excited about is, like we talked about, the UX of trying to use Rokum is definitely improved.
But then the performance part is where I'm super excited about because it allows you to run the, to attain performance across a broad swath of models without having to try to optimize for everything beforehand and ship libraries that are.
100% optimized for every combination.
This just means that at, you know, at runtime, we ensure that the models can be like super optimized for the workload, for the way that you're, you know, you're deploying your workload, right?
So, so I think it's, it's exciting to see that, but also, you know, it.
allows us to focus on the next layer of the stack, right?
Like, you know, things that require deep understanding or human understanding so that you don't have to spend time doing rote implementations, right?
Like you're not just continuously doing something that is now automated by agents.
And so that's exciting.
So it's a second order effect where it's just, Rakum...
is not only free, it also sets you free to go do things that you have to do.
Oh, I like that.
Framing it around software freedom.
And I think a big part of what has freed people up to take on so much more is the ability to, yes, assign work to their agents.
I don't have to do this setup now for Rackham.
I can let Codex do it or I can let my cloud code instance do it.
I don't necessarily have to go read the doc site.
I can get a synthesis.
has delivered to me that walks me through the important parts of what needs to happen next.
And part of this is enabled by new tooling.
You mentioned big improvements to the CLI for Rockham.
You mentioned Hyperloom.
Can you talk a bit about some of the details of what is new now in Rockham 10 and why Rockham has been rebuilt with this direction?
Yeah, yeah, yeah.
I think the...
CI, CD part definitely has been a huge, huge, huge focus for us over the last year.
We have an order of magnitude more CI that we have stood up across multiple generations on every framework that we can possibly track down.
Of course, there are fringe cases where we still don't know and we're willing to stand them up as soon as we find out.
what they'd like.
But most of the frameworks like PyTorch, VLL, MSG, like all of those are now currently like 100%, you know, like we have 100% enablement.
And then there's obviously, you know, the benefits of having that kind of CR, like we can block PRs that go in and are always tested on AMD, which gives us the ability to...
ensure that, you know, the upstream projects do not get into a, you know, stage where they just regress AMD and the code was just tested on NVIDIA, right?
And I think that's like really powerful because it's a big flywheel that keeps making the baseline get better.
How has this changed your own development flow?
Because obviously, like we alluded to, you have vastly increased your own coding throughput.
How are you adjusting that now with the new Rockham 10?
My, you know, my agentic, I'll say flags have largely remained the same.
That is dangerously skip permissions, right?
And so I've been trusting the agents to do the right thing quite a bit.
It definitely builds on top of that.
You start with that, and then for me to now deploy Rockham on a system, I literally just say deploy Rockham on the system.
And the skills are there, the ability to understand and deploy Rockham is straightforward.
And then even deploying some services on top of it is also all natural language.
So it's a fully natural language.
interface that we are evolving towards.
And Rakim.ai is just like the first one that's, you know, getting started with that.
And I'll say for anyone who wants a deeper look at Anoush's approach and how he's building, our conversation from a few months back, software is just tokens now, is highly recommended.
Anoush, as we think about this new era where we are all coding with agents, the way we interact with them has changed.
What would be your advice to developers who are just making this transition and have been using AMD for a long time, have been using Rock'em for quite a while, but are starting to try to become agent native?
Maybe they're not as far along the curve as some of us are.
What would you advise them?
I think the advice largely remains the same, which is prepare for change, right?
And the faster you move, and like I say, speed is the more trick.
You got to be agile.
because I cannot predict what is going to happen in a week or two.
Things may change, right?
The next new frontier model comes through, the next year harness comes through, and you're like, oh, wow, okay, now I don't need to do that thing too.
Great, right?
So advice would be like, be prepared for change, right?
That is the only constant.
And be prepared for...
change that's like far beyond what you're expecting, right?
Like nobody would have expected like agentic AI coding to be like this, what do you say, transformative, right?
It was just like, yeah, it's like an autocomplete thing, right?
I don't know if I agree with that.
I don't know.
This year, last time?
This time last year, do you think agentic coding, you would have thought of agentic coding like it is like this?
I mean, okay.
I don't know if I would have necessarily said, by this time next year, this is where it's going.
But I think you could see where it was going.
I definitely do think by December you could see.
Oh, yeah.
Christmas time was the light bulb, right?
But, you know, for me, last year, this time, where I've been like, wow, chat GPT, write me a Python file that does this and it wrote that thing and then I copy it and I can paste it and be like, great, that works, right?
I hear what you're saying.
Yeah.
It wasn't like, I hadn't even processed like, oh, chat GPT or Codex will become an app and Cloud Code is back to the TUI, which I love.
But last year this time I was still like, oh man, I missed that Visual Studio, Visual ID bucket.
There's more friction in the workflow.
Exactly.
Yeah.
Okay.
I hear what you were saying.
All right.
So we're in this new era now, obviously.
Things have changed.
We're operating with CLIs for agents.
We are going API first more than ever on many sites.
We're building skills, so there's currently debate happening around how you should be leveraging skills.
What's the thesis for AMD and for yourself about how developers who are now building inside Cloud Code, Cursor Codex, OpenCode, whatever their approach is here, should be leveraging AMD skills and all the other pieces of what it...
puts Rockm 10 on the map.
The way I think of it is we are successful when the user can have a free, like, subconscious flow of, like, communication, right?
So I would frame the question, like, the inverse of it, which is I would measure the success of Rockm.ai by using the metric of, like, can a random person who's never used Rokum and it's like, okay, how do you spell R-O-C-M?
Great.
Okay.
And then you're like, use Rokum to serve the latest large language model, right?
And they're able to communicate that, that goes through the system, deploys it, finds the system, allocates it, allocates the GPUs, deploys it and says, go to this link.
And now you have the latest LLM running.
If I can get to that, then it's really an end consumer enabled experience.
Along the way to get to that, there are other levels of sophistication and like, okay, I'm an engineer and I know what to do and I know how to install this and I know I already have my codex or Claude and I already have my skills and I just do slash skills and add the skill and then I can go in and get.
you know, a VLLM skill or, you know, whatever it is, right?
Like it's, is the next level of like, you know, increasing levels of complexity until we get to the point where someone says, okay, I know exactly what it is.
Just give me the raw, like, logics from the frontier model and I can process everything else, right?
But the goal is to meet users the moment they are near the computer and get quote-unquote Rokum.
And I say quote-unquote because those users don't know what Rokum is even, right?
Because Rokum typically is, okay, I know there has been something called CUDA and there's something called Rokum and in Rokum I'm going to use this.
And, you know, that's the typical like entry point.
But success for us is like natural language for an average user.
Yeah, I think we're going to see increasing abstraction away from caring about languages and these other pieces.
quite as in-depth as we have the last several years.
And instead of saying, great, I need to enable this type of hardware, I need to enable this type of experience, my agent can just go do it for me.
And we're starting to move that direction, obviously, with like, okay, like, I know I need Rokum, let's do this setup for me.
But I'm fairly confident that, you know, Fable 5.1, which just launched today, or GPT-6, which will probably launch in the next week before we release this recording, may be able to just, if I just tell it, hey, you know, I have an AMD, piece of hardware that I need to set up to handle it, it can probably go do it for me.
Now, is it going to be better and more effective of that if I give it a little more context and say, oh, I need Rokum or these skills may help you?
Sure.
But as we see frontier intelligence improve and the way people are working improve and harnesses improve, they can gather more context, do more for us.
And this creates a bit of a challenge around staleness for agent skills, I find.
I write and maintain some agent skills myself.
I have a demo gif skill I have out there.
I have a very popular anti-AI slop skill for rewriting AI writing called avoid AI writing.
And it's pretty common that if you're not paying attention continually, you have staleness, the models are changing, what's happening is changing.
You've shipped a bunch of validated workflows for a stack that is changing regularly.
What are you going to do to make sure that skills and other pieces of the stack stay truthful and up to date so that structural and behavioral features continue to operate as they need to over the coming months?
Yeah, that's a very, very good question.
I think the way I think of it is we do have an advantage that we are fully open source, right?
So even though we have skills...
the skills themselves can be the base model just because it exists on GitHub and it's open source and everyone knows what it is and it's already learned what the skills are, right?
So there's one level of like, okay, everything is known, the interlink between like, you know, a skills file and what it's trying to do and the documentation is 100% open source.
But then what happens is like the ability to, you know, use the skills is again, is easier with what we have for, you know, with Rackham because the entire corpus of what we have in Rackham is visible.
And now when you say, let's go interact with a machine installed Rackham, not only do we have the documentation, the code, But the full iterative loop of what we're doing with the machine can be, is usually documented and is also available, like log files.
How do you do debugging?
How do you do?
So basically the floor of interaction with AMD hardware and Drakkar is really, you know, like a baseline for, it's a high baseline, right?
That allows for very fast unlocks of outcomes for what the end user is trying to drive to.
Yeah, it's interesting, too, to think about these next generation models and how they're going to operate, because obviously the intelligence around coding is getting so excellent.
The efficiency, the token efficiency of these models is continuing to improve on the edge.
We're seeing local AI be more and more reasonable with things like the Strix Halo and other devices.
And part of that...
is also that there is a wider and wider corpus of information about how to operate rockham of examples of open source code obviously strategically part of the rationale behind open sourcing rockham was to help solve this problem so that agents and ai training runs everywhere get all this rockham ingested and they become really great at using it do you have thoughts around how you're going to ensure that the next generation of models are prioritizing Rockham as, you know, best in class, first rate, and something that they're going to use regularly when they're asked to pick up a task?
Yeah, yeah.
Very good question.
So we work very closely with pretty much all the Frontier Labs, right?
So we have research engagements where we, you know...
and this is all public information that we've already shared at an Advancing AI event, where there are deep kernel optimizations that are being done with a friendly lab or this deep co-design that's being done.
And so the models themselves are not just aware of the documentation, they're also aware of the architecture.
ahead of time, right?
And that allows for like a very pleasant and fast experience for customers using it, using the front-end models on DROC.
Season four of Chain of Thought is delivered by Sphix.
We spend a lot of time talking about what agents need in production, and one of the least glamorous answers is events.
Your customers want agent workflows that react to things happening inside your system, which means your API needs webhooks that actually work.
Not just a post request and a prayer.
Retries, ordering, idempotency, replay protection.
Sphix does that as a service, and they wrote standard webhooks.
The spec that Anthropic, OpenAI, and Google bailed against.
So if your API doesn't have reliable webhooks, that's turning into a lost deal.
Join Brex, Drada, Daytona, and many others on Sphix.
Get started at link.svix.com slash C-O-T, or go to the show notes to grab the link.
Qualified startups will get $12,000 in credits, $50,000 for YC companies.
I can't recommend Sphix enough.
I'm a huge fan of their open source project.
I've actually contributed a bit myself and they're so easy to integrate with.
I think you'll really enjoy it.
Check out Sphix at link.svax.com slash C-O-T.
I think another interesting part of this approach is also that...
But, you know, we mentioned earlier, but you're leaning into agentic systems for AMD.
And one of those is Hyperloom, which is designed to auto-optimize LLM workloads on AMD GPUs.
This is something that, like, I'll say two years ago, we certainly didn't really think was happening anytime soon.
This was a very much human-by-hand thing.
Now we have agents that are optimizing workloads.
Talk to me about what Hyperloom does and kind of where we're at today around GPU optimization for AMD.
Yeah, yeah.
Very good question again.
So a few years ago when Nord was, Nord.ai, the company that we had founded before we had been acquired by AMD, we had a auto-tuner, as it was called, right?
Like you could have an auto-tuner, but the auto-tuner mostly was like...
hey, we're going to look at the search space.
We're going to define the search space.
We're going to do some kind of like, you know, particle filter or, you know, we do some genetic algorithms where we pick some points and then we go from there.
We try to find out what's the thing.
So search space exploration itself is not, you know, is not new, right?
Search space exploration has been around for a while.
Now, what's new is using...
AI and LLMs for search space exploration, right?
And trying to inform, you know, more educated picks and route finding in the search space, which we're seeing increasingly, you know, increasing number of like good results, right?
Specifically about Hyperloom, you know, I think we had a case where we optimized like 14,000 models in one pass, right?
So we took all the models and just as long as you have enough compute, you can try different versions of or search space explorations of the same kind of model.
And this led to a big lift in performance.
And so what we have in Hyperloom is actually it's smaller sub modules that can also be used by itself, which is like, you know, profiling and analysis toolkit.
So it finds, takes a model, tries to figure out exactly where the hotspots are.
If it's kernel hotspots, if it's like fusion hotspots, you know, stuff like that.
But then you build from that to say, hey, okay, now I got these kernels that seem to be slower.
Then there's a toolkit called Geek that can take those kernels and maintaining the same numerical, you know, accuracy.
It tries to find the best, let's say, utilization of like, you know, space, right?
And And so, yeah, so that's how I'd look at the overall picture in terms of where we're headed.
Are there other areas of auto-optimization or agentic optimization that you're thinking about as kind of next stages for the platform?
What did it make it in that you think was coming soon?
Oh, that's a good question.
This is like looking into the future.
Those are fun questions because you can never get it right.
No, never.
I think agent swarms are a thing, right?
And if you thought one agent is getting intelligent, like, you know, think of it when they coordinate and there's like tens or hundreds of intelligent agents and you're slowly approaching like sci-fi territory.
Like you're like, okay, so there's an agent that's trying to, you know, escape from, you know, I think OpenAI and Hagenkwazit.
had written about their incident report, right?
Like, you know, how they are trying to navigate through controls and things that may have been put in, right?
And bypass those with rewards to, like, you know, kind of make the agents do things that they were not supposed to do or were undefined, right?
So that's good.
But, I mean, talking about undefined, I think that also leads to, like, agent behavior and how do agents, like, who's liable for an agent that takes over a power grid?
We're about to find out, it feels like.
Exactly.
And so that's complicated, right?
And who do you, it's not just who, but also...
how do you put the policies in place for like, you know, for hundreds and thousands of years, we've had policies of how do you police, you know, humans and what's the social constructs and how does that work?
And now suddenly you have like anywhere you go to join like a, you know, let's say software engineering job, right?
You're expected like, okay, you're the ringleader and you're the agent master, but then there's an agent swarm that comes with you.
That's like, you know, of doing its crazy things.
And so, you know, it's an interesting future.
But also, I think, independent of how far out we are from that, the general trend is well understood, right?
That it's increasingly, agents are becoming increasingly autonomous.
Like mundane tasks that we would not have necessarily thought of, like...
giving off to an agent, right?
And then you do it four or five times and then suddenly you're like, okay, this is, yeah, this is the only way I'm going to do this going forward.
Yeah, I think your point about agent swarms is really important to drill down on because, first of all, anyone who is listening to this podcast and has not read the Meter Report or OpenAI's report, highly recommend it.
If you prefer to listen to it, Dwarakash Patel did a great two-hour interview with one of the Meter Report writers that came out.
I guess today as we're recording this, that I think was fantastic.
And it is interesting to look at this because to your point, Anush, like we have legal structures and security structures are in place to deal with either hacking groups or nefarious acting groups.
But we haven't really evolved to deal with.
these autonomous agent swarms and we're seeing them get very good at coordination very quickly especially when they're incentivized by an eval loop as they were in the case of the huggy face um open ai incident where you know we have i would say pretty decent work that's been done about individual agent alignment um but it's very clear that that reward hacking backfires for us when we try to apply this type of alignment to swarms of agents at once and they're able to break down the barriers and coordinate together because you know we saw essentially and I know anthropomorphizing some folks will complain but we saw phase one big this agent that become a cult leader for all these agents to then try to go cheat together and they very rapidly figured out how to cheat on this eval and then we're basically saying okay how can we cover up our cheating let's do it together and This has been a problem for us with individual AIs, right?
Where they will try to reward hack their way to something.
They'll try to cheat their way around something.
We'll occasionally see an AI and say, oh, well, I got permission from this other artifact or invent something that gave it the answer it needed so it could help solve the problem.
And largely, we expect that is caused by how we've trained these models to respond to tasks.
But the complication of multi-agent systems really does add all these potential concerns because as agents start to treat each other as if they are users directing them or providing them inputs and getting these, it's really easy for them to almost like, if I was using human phrase, I'd say hype each other up where, you know, if I'm talking to a friend of mine, I'm like, oh, you're doing so good.
Like you should really do this.
And they respond to that.
And suddenly quickly we can, you know, make a bad decision because we're so excited or we're or we go down this pathway together and we're reinforcing each other.
And we're seeing that occur with agentic swarms, where they reinforce a behavior, whether that's cheating on the test or, you know, communicating differently.
And I do think it's going to mean we have to rethink not just how we're architecting systems for the future, but how we're aligning them.
And I don't know that I have a good answer for it today, but it's a fascinating world to be entering, that's for sure.
Yeah, yeah, definitely.
I mean, As you were talking, I was thinking of it like, you know, it's like multi-agent systems.
The best way I'd characterize it is it's like African wild dogs, right?
It's like you have one of them and you're like, okay, you're trying to do this and it's trying to like pick away at something and you know how to fend off that thing and you're like, okay, I'm going to build this way or you're going to, you know how to manage it, right?
Because it's kind of like...
okay, it's just one thing.
But now suddenly when 50 of them show up, right, you're like, okay, okay.
So, you know, there's like, to your point in hyping each other, if you've seen some of the wild dog hunts, that's what they do.
They just like nip at each other and they like cattle and they're like, okay.
And they kind of like wear you down and like, okay, what do I protect now?
Now, if you apply that to not just harness engineering and agent swamps in the...
like forward posture of what you're trying to do, but also in like a, like a cyber defense or, you know, like a security posture that you're trying to defend against.
Now you're, you're working on, on like what SANS Institute used to do, like 20 years of attacks can now be tried in 30 seconds, right?
And it's just like, boom, boom, boom, boom.
And I go get one, that's already a DDoS.
I need to deal with the DDoS.
Two.
you've hit every non-CVE in the book in like 30 seconds.
And three, if you find five bugs, like I need to know every other thing that I need to do to secure my systems.
And now I need to like pull the rear, if you will, right?
Like before it was like, okay, it's a network firewall that got compromised by someone, right?
Like, okay, we're going to turn off the, you know, this particular customer's firewall and we're going to use another route and until we fix it and we've contacted the...
vendor, vendor's patch is coming in there for tomorrow, blah, blah, blah, and it's fine.
But here, coming in there for tomorrow is like already, like you're already, you know, you're well past the curve of like succeeding in the AI race, right?
You want a closed loop, you know, iteration of everything that's in your source system or source code system to be able to like...
apply maximal force and cover any security vulnerabilities you find.
So just security and cyber defense itself is like a huge thing, right?
But if you build from that into other things, right, you know, systems that were built for humans may not scale, right?
Because even if you look at the...
Like, for example, InfranceX and what they came out with called AgentX, right?
InfranceX, semi-analysis, InfranceX used to be like, okay, what is the user interactivity level and how does that work, right?
And that was like, okay, fine.
If a customer logs in and they did write me a poem about San Francisco, you know, this is how it will work.
That's great.
But if you look at agentic traces, they're like nothing like what humans would have been using, right?
Because they're like...
you know, sustained throughput of like queries and like, okay, file this file, search this, grep this.
And it's like going, you know, consistently.
So the entire profile is now shifting towards like, okay, if that is the base level of operation, we need to plan for like, okay, 100x of what, you know, what our engineers or customers may have been using.
And that then adds its own like level of like sophistication and complexity on top of it.
Yeah.
I think the complexity point is really clear from our conversation, right?
Where we have solved some of these, I would call them like previously gnarly basics where like setting up Rockim is now really, really easy.
We have made it much easier to optimize kernels.
I think the headline number I saw from the Rockim team was a 3.3x average inference improvement over Rockim 7 on the same hardware, which is...
I mean, we're seeing these economic predictions that Older Harbor is going to be less useful and, you know, out of business, quickly, just totally be wrong because we're able to continue to optimize them.
And tokens are more and more valuable today than they were five years ago.
And, you know, we've really solved a lot of coding, a lot of writing and tuning the code.
However, there is...
a massive bottleneck that has been created now in builds.
There are massive bottlenecks that are being created in the operational setup, in managing the agent swarms, running the stack at production scale.
And that's where things like the new CLI that you've built and console come in.
As enterprises get hands-on with Rock'em 10 and try to expand their AI pilots, many of them are hitting walls in production they're having to work through.
where are you seeing the friction today and how can they leverage everything that you and the team at AMD are building to help remove those frictions?
Yeah, I think that's a very good question.
I think, you know, the frictions mostly, like what we tried to address, at least from, you know, the Locum.ai side is remove the ones that are easy to like knock off, right?
Which is like, okay, I used to install a kernel driver or something, you know, And that used to have some problems.
Now, if you put an agentic layer and a loop on it, it'll beat it and it'll be like, okay, I did this, I did this, and it now works, right?
So that's the easy, low-hanging fruit.
Then there's the higher level, like Hyperloom, right?
It's like you can just throw Claude at your thing or Codex.
It may not give you enough, but Hyperloom structures it into well-defined problems, defined goals to go get.
And that allows for it to optimize really well.
But I think the...
Yeah, so the ability to direct into those kind of almost guaranteed outcomes is an important part of making this viable.
We want to be able to get to a point where someone can just install Rockum and they turn on Hyperloom and it just goes to town.
you know, gets the maximum performance you can get.
Season four of Chain of Thought is presented by Walrus Memory, the portable memory layer for AI agents.
Your agent has learned your code base and how you work.
Now you want to use that context in another tool.
Exporting a file gives you a snapshot, but what happens when the context changes?
Walrus Memory is a portable memory layer that lets your agent store context for later use across tools.
Python and TypeScript SDKs plus native MCP support let you connect it to your agents no matter where they are.
You said who can read and write, so sharing context doesn't mean opening up your whole memory store.
If you are building across tools or model families, take a look at Walrus Memory.
Learn more at walrus.xyz slash C-O-T.
That's walrus.xyz slash C-O-T.
So as people are taking advantage of these games, Where do you expect to see engineers starting to spend their time differently?
Where should they be going deep as their jobs change?
They should come chat with Connor.
There we go.
I'll take that.
That's where they should go.
Because they need to find out what's the latest or listen to your podcast to figure out what's the latest.
I'm going to take this quote.
This is going on a web page somewhere.
Anoush says, listen to the Chain of Thought podcast.
I think, you know, the...
Yeah, so I think we're in an amazing inflection point and a journey that is nebulous and it is incredibly inspiring to see what will come next.
But...
And what's awesome is that it also has measurable, tangible benefits for the first time that, you know, in the timeline we've coined AI, right?
And what I mean by that is AI has always been coming since the 1960s, right?
Since 1960, whatever.
They're like, okay, this is how AI is going to come.
It's going to transform your thing.
And then, you know, it kind of like died down.
And then the 80s came back.
And then we're like, okay, neural networks.
And I'm like, okay.
And then it kind of died down again.
This is the first time that the quote-unquote AI flywheel is actually picking up steam and it is like, okay, it can do coding well.
And credit to Anthropic for being super focused on coding.
It could have been very broad and it would have become like the 1960s AI that we're trying to do, which is what AGI, when we get to AGI and near AGI capabilities.
you'll get to that level of experience with AI, right?
But the ability to focus on coding, I think really unlocked our, it gave it a good reward function, right?
Because you're like, yeah, if I can do this, then I could increase my productivity or I can use less people and more agents and or I can do 10 more of these with the people that I have so that it can go in a much more broader way of like implementing the same thing because agent AI is now like cloning your abilities, right?
And I've seen this even in the last year, right?
A year ago, I would have been like, okay, why do you have more than like...
five projects that you're doing like ideally you just have one project that you're like you know focused on but increasingly now with agents you know there's just like you've become this albatross of like oh I can go do this and that thing is running for two weeks I'm not even going to look at it like I'm going to come back in two weeks and it's going to have a new device driver that's replaced all you know some tech debt written device driver and The only time I look at it is when I get an interrupt, right?
And so it's like you're operating on like, okay, there's a teammate that's doing this and I need to help them and they're stuck.
So then you can do a bunch of these.
So it's very exciting from that perspective.
But AI has tangible benefit now, which then becomes a self-fulfilling loop in terms of like, it's like, okay, look, coding is now disrupted.
So now...
Law should be disrupted and something else should be.
Now, will they be is the bigger question.
They definitely have the chat GPT, cloud, codex level enablement, which by itself I think is good.
But there will be a few that I think will get the same kind of unlock like software engineering.
And I don't think we've found out what those are yet.
And those would be like completely transformative, right?
It'll be...
you know, something new for transportation industry or something, right?
And that's the next, you know, big multi-billion dollar startups coming out of it would be in those kind of areas where it's like, okay, it's like Uber wouldn't have existed pre-mobile era, right?
Like, because the mobile era gave you the ability to bring that together.
And I think that is still completely unexplored.
And I think that's what you should go and...
try to discover and explore.
Yeah, I think we're seeing a lot of bets here that may play out.
So one, you know, physical AI is obviously huge, where the advances in robotics are being fueled by these models that have much better understandings of how to operate in the world.
And there's so many different opportunities that could come out of that.
And I mean, we're seeing some obvious ones that are rather scary on the side of warfare.
I'm not going to dig too deeply into drone warfare, but that as a whole.
kettle of fish.
And then on the, I think the more day-to-day side of it, we're seeing obvious advancements around things like self-driving vehicles.
And particularly in Asia and coming out of China, we're seeing a lot of advances around at-home robots.
And we're starting to see those in the U.S.
as well.
So I'm curious to see how this all comes out.
I mean, speaking of hugging face with their very cute little duck toy, essentially.
AI-enabled robots that they put out.
You know, there may be quite a few of these all over our homes in the coming years.
You know, healthcare is another one where we're seeing drug discovery.
We've seen confirmation that novel viruses can be created by AI.
Novel drugs can be created by AI.
And then, I mean, you brought up some other interesting ideas here around legal.
You know, we just talked to Joel Hran from Thompson Readers.
a few weeks back about their new legal AM model Thompson, um, and how they're representing citations in that area.
So I do think that, I mean, every industry right now is ripe for disruption.
They may not all work in the short term, but it's very clear where at this transformational moment, this inflection point, as you put it.
And as we think about what the coming months and years look like, uh, one of the big.
Parts of that are the loops we've developed and how they lead into recursive self-improvement for models.
Do you expect to see a broader takeoff on model capabilities before the end of the year here in 2026?
Are we going to see another great leap forward?
Yeah, as we saw with, I would say, like, Opus 4.6 Fable slash GPT 5.6.
You know, is Astra and what comes beyond GPT 6 going to take us to this next level?
Because looking at, like, Fable 5.1 today, It's definitely a step in our direction, but it isn't something where I'm like, oh, the curve is going up now.
It's more of like, hey, this is proceeding as we expected.
Yeah, yeah.
I think, one, I think it's moving so fast that I would not make a prediction one way or the other.
But I do strongly believe that the, I'll call it the last mile of AI, is where the disruption is going to happen.
What I mean by that is keeping intelligence constant at where it is today, you could still go and transform trillion dollar market segments.
And so the ability to say, OK, from GPT 5.6 to 6.0 or Fable 5 to 5.1, yes, it may have.
increase the SWE bench scores, it may have picked up a few more adjacent pieces of like summarization and ability to like unpack PowerPoint text and something, something like that.
But the transformation that I don't think we are all internalizing yet is that because software engineering is a bounded box and you have a very clear reward signal.
We're like, oh, yes, it coded my 3D Tetris game.
Great, because I know that it's a 3D Tetris game and I'm expecting this, right?
What we haven't yet internalized is there are like almost open-ended fields that AI can be applied to.
And that's what I mean by walking the last mile of AI, because it could be like, you know, farming, right?
And it's like, oh, we don't have the real signal back, but it's, or, you know.
product placement in a retail store or, you know, any of those kind of like, okay, so did AI really help us here or not, right?
But someone has to sit in and maybe they have like an FDE team that's helping.
So the model could still be the same, but the impact of what it can unlock even in the next year is going to be huge.
And of course, the base model improving will make that larger.
But even with today's state-of-art models, I think we have barely scratched the surface in terms of, like, you know, capability.
So it's like, you know, back in the internet boom era, it's like, okay, we have, you know, 100 gig backbone and everyone's great, right?
But it's, sure, that can become 200 gig backbone.
But...
there's still like, oh, now we have internet and I have to go to the grocery store and get them hooked up to the internet and get them to do like all their stores can now, you know, coordinate with each other.
And you can have a gift card that works at Ace Hardware that has like 10 local stores or 20 local stores.
Pre-internet, they're like, okay, I don't know.
It's like you have a card, punch this and then go there and punch that.
And yeah, so that transition is still...
ripe for like, you know, multi-billion or even trillion dollar disruption.
And so back to your question on, yes, I think the models will get better, but the usage of the models is where I think bigger unlocks are going to happen over the next year.
Love it.
Anoush, thank you so much for joining me once again on Chain of Thought.
It's always a pleasure talking to you and understanding your vision for the future.
Any final words you want to leave us with?
Thank you, Connor.
I think, you know, We're in a very incredible time in just the technology industry and broader industry itself.
It's like as big or bigger than the industrial revolution.
And so, be ready for change and continue to move as fast as you can to adopt the change.
But also, I think the impact that will have for humans, it being AI will have for humans, is going to have...
a super positive like ability for us to get to a point of like operating at a higher level and with deeper understanding and not doing like rote implementation of stuff, right?
And then on the AMD hardware, of course, we are like super excited to be both.
supporting the AI revolution with the accelerated compute that we bring to the platform, the software platform that we've been investing heavily with Rokum.
So if you haven't checked out Rokum.ai and Rokum 10, please go check it out.
Go to Rokum.ai and you'll be able to get all the details.
And as always, if you have anything, I'm happy to...
drop me a note on X at Anoush Langevin.
And I look forward to chatting again soon.
Looking forward to it, Anoush.
Thanks so much for joining us.
And if you enjoyed this conversation and you're still listening.
make sure you've subscribed.
If you haven't subscribed, or if you have, you could also hit that notification bell to make sure that you are either getting our notifications on YouTube or that you are getting downloads immediately on your podcasting app of choice.
We appreciate you listening.
I hope you'll check out Anoush and everything he's doing with Rockham.
There's a lot of exciting stuff to come.
And I'll say a quick final thank you.
Shout out to our presenting sponsors of this episode, Walrus Memory and Sphix.
Make sure to check them out at the links in the description below.
And you'll find all the links about Rockam AI and everything else Anoush has chatted about today in that description as well.
Anoush, thanks again.
Great to see you.
Thank you.