---
title: 'Stripe Buys OpenRouter, Ramp''s AI Index & IBM''s OpenAI Deal'
source: 'https://youtube.com/watch?v=8LNH7TxvD14'
video_id: '8LNH7TxvD14'
date: 2026-08-23
duration_sec: 2151
channel: 'IBM Technology'
---

# Stripe Buys OpenRouter, Ramp's AI Index & IBM's OpenAI Deal

> Source: [Stripe Buys OpenRouter, Ramp's AI Index & IBM's OpenAI Deal](https://youtube.com/watch?v=8LNH7TxvD14)

## Summary

This episode of Mixture of Experts discusses three major AI industry developments: IBM's partnership with OpenAI, Stripe's acquisition of OpenRouter, and Ramp's AI Index report. The panel explores the strategic implications of these moves, the shift from model ownership to orchestration and integration, and the growing importance of cost management and governance in enterprise AI adoption.

### Key Points

- **Heartbeat of AI is around routing** [00:01] — The panel identifies routing as the current focal point of the AI industry, emphasizing the shift from model development to the orchestration of multiple models.
- **Topics: Stripe-OpenRouter, IBM-OpenAI, AI legislation** [00:39] — The episode covers three main topics: Stripe buying OpenRouter, IBM's partnership with OpenAI, and AI's role in Congress.
- **IBM-OpenAI partnership** [01:07] — IBM announced a large partnership with OpenAI, following a similar deal with Anthropic, to train AI practice within IBM consulting.
- **IBM's strategy: forward deployed units** [02:03] — IBM plans to use OpenAI's capabilities combined with its own to deploy forward units that meet clients where they are, signaling a shift in enterprise AI value from owning models to integrating them.
- **Anthropic vs OpenAI roles** [03:01] — Anthropic is geared towards IBM software engineering, while OpenAI handles more complex tasks and planning, with IBM acting as an AI integrator offering both options.
- **Everyone competing at every layer** [04:17] — The panel notes that companies are competing across all layers, with model providers also launching deployment companies and consulting arms, leading to a complex landscape.
- **Best model for what?** [05:16] — Different models offer varying costs, latencies, and performance, making model selection context-dependent. Geographic availability and scaling limits are also key considerations.
- **Scaling AI is costly** [06:09] — Giving AI to 100,000 employees is expensive, with token costs potentially unsustainable. Partnerships provide economies of scale and access to leading-edge models.
- **IBM's distribution advantage** [08:16] — IBM's legacy plumbing and consulting pipes are valuable to OpenAI and Anthropic, as 80% of enterprise value is locked in legacy systems. IBM positions itself as a neutral orchestrator.
- **No one-size-fits-all model** [09:20] — The best model depends on enterprise-specific factors, and orchestration layers like watsonx Govern provide audit, compliance, and model hot-swapping capabilities.
- **AI is change management** [10:02] — The real challenge is not model intelligence but change management, system integration, and governance. IBM consulting weaponizes OpenAI and Anthropic models for digital transformation.
- **Stripe buys OpenRouter for $7B** [11:40] — Stripe acquired OpenRouter for over $7 billion, more than five times its $1.3 billion valuation from 82 days prior, highlighting market exuberance.
- **OpenRouter as a gateway** [12:37] — OpenRouter provides a single platform for accessing multiple models, with features like semantic caching and routing, which are essential for enterprise AI management.
- **Why Stripe?** [14:30] — Stripe aims to be the 'Visa card' for AI agents, taking a small cut of every transaction. This positions them at the center of AI-driven commerce.
- **Stripe's neutrality** [15:56] — Unlike cloud providers, Stripe is model-agnostic and doesn't care where code runs, making it a flexible payment layer for AI.
- **Post-model market** [16:40] — The focus is shifting from models to routers and gateways, indicating a 'post-model market' where orchestration is key.
- **Routing commoditization** [17:28] — Routing is becoming a crowded space with many competitors (LiteLLM, Cloudflare, Portkey), and commoditization is inevitable.
- **Token growth 5x** [18:19] — Token usage grew from 5 trillion to 25 trillion in six months, driving Stripe's bet on AI infrastructure.
- **OpenRouter's user base** [19:30] — OpenRouter has 150,000 active users and 50-100 employees, with a valuation implying $70 million per employee, a bet on future growth.
- **Stripe's tollbooth analogy** [20:32] — Stripe sits at the bridge and collects a small fee on every AI transaction, similar to a tollbooth, while model makers compete on benchmarks.
- **Open gateways** [21:12] — There are over 100 open-source AI gateways, evolving into agent management platforms with governance, trust, and observability features.
- **Ramp's AI Index** [22:41] — Ramp's August 2026 report analyzes 70,000 businesses, showing AI adoption is rising but uneven, with top 1% spending $7,400/month per employee vs median $650.
- **Ceiling on AI spend** [24:46] — Companies are metering AI usage to control costs, especially as digital workers become more prevalent.
- **AI cost vs salary** [25:12] — Top 1% AI spend per employee approaches the salary of engineers, raising questions about productivity vs revenue.
- **End of AI free-for-all** [25:42] — 2026 marks the end of unmetered experimentation; CFOs demand measurable unit economic payback, exposing vulnerabilities in thin wrapper startups.
- **Cost per outcome** [27:02] — Evaluating AI on cost per resolved ticket (e.g., $18 to cents) or cost per PR merge ($400 to $12) is more meaningful than per-seat spend.
- **Segmented adoption** [28:01] — Software developers use AI substantially more than other teams, indicating ROI varies by department.
- **Shift from POC to production** [28:47] — Enterprises are moving from 'help us use AI' to managing hundreds of agents, with cost and governance becoming primary concerns.
- **Regulatory and TCO costs** [29:56] — EU AI Act fines and total cost of ownership (people, resources, governance) add to token costs, making AI expensive to manage.
- **Cost runaway** [30:26] — Organizations lack mechanisms for budgeting, token quotas, and enforcement, leading to cost overruns.
- **AI is easy, cost is hard** [31:10] — While AI is getting cheaper and more capable, token spend increased 13x in a year, and predictable costs and ROI remain elusive.
- **FinOps era for AI** [32:01] — Similar to cloud computing's maturation, enterprise AI is entering a FinOps era, focusing on spending smarter, not less.
- **Engineering tools justified** [32:48] — Tools like Cursor and Claude Code are becoming indispensable developer infrastructure, justifying costs, but other areas may not see similar ROI.
- **AI slop in Congress** [33:16] — Politico reports that the House of Representatives is awash in AI-generated bills, highlighting the need for guidelines and governance.
- **AI mistakes and responsibility** [34:13] — AI will make mistakes, and users remain responsible for the end result. Secure, governed AI use is essential, not just using ChatGPT.
- **Guidelines for AI use** [35:12] — Rather than fighting AI, clear guidelines on safe use and empowering smart adoption are needed, including in legislation.

### Conclusion

The AI industry is shifting from model development to orchestration, integration, and cost management. Partnerships like IBM-OpenAI and acquisitions like Stripe-OpenRouter highlight the importance of distribution and payment infrastructure, while Ramp's data underscores the need for FinOps and governance in enterprise AI.

## Transcript

And, you know, we just have to trace where the heartbeat is coming from. And it seems like that right now the heartbeat is around routing.
All that and more on this week's Mixture of Experts. Each week Emily brings together a panel of technologists
through the week's news. On this week's episode, we have Kaoutar El Maghraoui, principal research scientist, Aaron Baughman, IBM fellow and Mihai Criveti CTO watsonx Orchestrate.
Today we're going to talk a little about Stripe buying OpenRouter. And then finally we'll talk a little bit about AI for legislation in Congress.
about one big announcement coming out of IBM itself. that IBM was going to launch a big partnership with OpenAI.
It caught my eye just because not too long ago, IBM had also closed a very large partnership with Anthropic as well. And specifically, the partnership pertains to essentially training up large numbers
AI practice within IBM consulting. You know, I'm kind of interested in how
because normally I think we've thought about like, and it's just going to be, you know, the sharks and the Jets. is going to merge where it's not going to be so black and white, and in fact,
to integrate these systems that are kind of much more ecumenical with these models. about your take about this partnership and where you think it all might go.
You know, to have OpenAI's AI capabilities, plus IBM's and our ability to put them within a Fortune 500 company and beyond.
we're going to have forward deployed units and forward deployed that can go out and meet clients where they are, right.
You know, and this is a big signal that we IBM You know, we're actually working and we're going to become You know, we're betting
that the enterprise AI value, it's shifting upwards from owning the model And even you could argue operationalizing, you know, these types of models. Is that, you know, both Anthropic and OpenAI, they they somewhat own the market.
And and because we're we have a partnership with both. it's quite important that we're putting together Anthropic. Where we have Anthropic is really geared towards IBM software engineering.
You know, you know where we have IBM Bob built around that ecosystem And going out and having these forward deployed units and engineers,
AI integrator where we have the options right, of both of the leaders Anthropic and OpenAI.
type series models, which I think would become even more important. And the Granite models are the much smaller ones, right,
So they're smaller, they are cheaper. Whereas you could argue that the OpenAI ones, they would handle the more complex and type of task planning.
And so and so by having, you know, these model gateways, I know Mihai works works on a lot, gives us the ability to go Yeah.
And I think, you know, I think one way of looking at it is almost like everybody's competing with everybody across every single layer at the moment. And not too long ago both launched this own deployment company,
They also signed a partnership with McKinsey, Aaron, as you mentioned, and also has its own consulting arm, which is now partnership with OpenAI.
sort of like everybody is going in every single direction. Because it feels like they are sort of almost like choosing every single route and sort of seeing what sticks
of working with clients to kind of integrate this technology. This is where consulting organizations come in and have the ability to really integrate specific models for a specific model providers
into use cases, which are company specific, client specific. The best model for what? Different large language models and models providers offer multiple models
with different cost, different latencies in time to first token, different performance as in the outcome. Which is the best model for coding?
Which is the best model for information retrieval or summarization? Different models model providers have different geographic availability, as an organization as models in your geography.
If you don't have OpenAI in your London data center, And even there you have limits with these models, which is what most companies tend to find out the moment they scale AI
hey, can I just give AI to my 100,000 You literally can't buy it to go to these organizations. And you're like, hey, this thing only does 50, 150 to 100 requests per second,
And no matter how much money you throw at it, you realize So then you either do model routing or you go to multiple providers
and you find out, hey, I need to prompt this model differently. So this is where again, that relationship and partnership with system integrators, with the forward deployed engineers and so on comes in to be able to help you
consume the right model, for the right task, in the right way, in a way and prompted the right way to get optimal results. It's very costly. Right?
For over 100,000 employees each to begin to use these types of models. And I think to his point, you know, there's no no free lunch, right? Where you have to route the appropriate problem to the appropriate model.
where you want to have the best capabilities Because because if you think it's like ten bucks or $10 US dollars per employee
and you have hundreds of thousands of employees per month, It costs token costs, which may not be sustainable for a business. And so by having these partnerships, you're not not only get access
to some of their leading edge models, but you presumably also get, you know, economies of scale pricing, right, which does help so that you can equip your forward deployed engineers and forward deployed units.
So I do think it's a competitive advantage for both IBM and also for for OpenAI to saturate the market with their capabilities. which is kind of the distribution and the legacy plumbing
that we have, you know, at IBM, which is very strong. If you look at what's happening in the Silicon Valley, on the MMLU or the HumanEval benchmark wins the Fortune 500.
But in the real world, 80% of enterprise value is locked inside legacy mainframe COBOL databases. systems, HIPAA compliance vaults, etc.
OpenAI and Anthropic needs IBM's distribution and consulting pipes far more than IBM needs their specific model checkpoints. IBM, I don't think we need to spend 20 billion training Frontier
Foundation models to compete with maybe Microsoft, Google or Meta. neutral enterprise orchestrator. Through the watsonx Govern, IBM provides the audit
and the regulatory compliance layer while letting clients kind of hot swap models based on cost, speed, and capability. I mean, there is no model that fits everything here, so it depends.
The best model depends on so many other other dimensions here. And you have to tune it to your enterprise, to your data, etc.. So I think it's a very important to have that layer, that orchestration
and those pipes, as are so important with the regulatory compliance, with the audits, with the governance, which is really important AI is not a shortage of intelligence models.
It's really change management, system integration and also dealing And I think IBM consulting is kind of weaponizing OpenAI and Anthropic models to accelerate that enterprise digital transformation,
kind of converting legacy code bases and automating banking workflows at scale. And I think the question being asked is it's almost a little bit like what. And I think, you know, you've seen this debate.
We've talked about this a lot in MoE on the compute side where it's like, okay, maybe it's really the compute that's going to matter. when a world where there's lots and lots of models
and there's there's lots and lots of options, then ultimately the kind of winning factor is going to be the sort of trusted consultant. Yeah, yeah, I really do like this constellation of partnerships.
It's IBM Plus Anthropic plus OpenAI. Because the Anthropic deal and partnership was announced around October 2025.
enterprise software development and integration. And that's that's how IBM Bob somewhat emerged. You know, that's about equipping and training thousands of consultants
right, and meet clients. So we're taking the full stack of the business with these two partnerships. And I'm really excited about the future and what we're going to do together
Well, that's a great segue to the next topic I want to cover. news broke this week that Stripe, the payment processor,
Router for more than $7 billion. And again, if any indication of how crazy the market is right now, going around the internet, which is that 7 billion is more than five times
the $1.3 billion valuation that OpenRouter had just 82 days ago. And I think for a lot of folks who spend their time focusing on the models and hearing about what OpenAI and Anthropic are doing,
OpenRouter itself may be kind of an unfamiliar name. And so I guess for folks who are maybe less familiar with what Open like why it's such a big deal and why it could be possibly worth $7
MCP gateway, Agentic gateway and gave it away for free, I'm kind of A little bit depressed?
the future is multi-modal, multi-model, multi-agent, multi framework, multi harness, multi-cloud multi everything. There is no one model which is good and you can't even get
So with platforms such as OpenRouter, you have the ability to access to a single platform, which gives you access
not only to the models, but you can do things like, for example, semantic caching, or you can help optimize some of that work. And some of the future that we will see in this space is integration into unified
gateways, gateways that have the ability to access not only models, but evolves controls with semantic routing, the ability to route the right request
to the right model, MLOps and cost management, the ability to say, and spend with things like virtual API keys
so you can lose it, or somebody can steal it, but you're actually So a lot of these capabilities are must haves in enterprise.
They are the capability which allows an organization to distribute these models MCP servers, tools and so on safely and observability data from those interactions.
Why Stripe? We've been hearing, of course, a lot Do you know Stripe is you know, I use them to pay for subscriptions
Yeah, that's a very good question. The tollbooth is where the money is. Every six months, models get smarter and way cheaper.
Building a SOTA model is a tough, expensive a tiny toll on every single request, no matter who wins the benchmark race.
It's kind of a payment headache. Old software kind of charged, say, like a $30 a month per user on a credit card. It's millions of unpredictable micro tokens flying around like 24/7.
lets them handle metering and payments right where the code runs. And if you look at AI agent, they need their own wallets down the road. They can sign contracts or swipe physical cards, and the agent
for example, for simple lookups and a $0.50 model for deep thinking. Stripe wants kind of to be the Visa card for these autonomous software.
The the big clouds aren't, you know, Amazon, Microsoft and Google want you kind of stuck on their servers. Stripe doesn't care where your code runs, whether it's on AWS, on an old laptop
They just, you know, they just take their small cut of the transaction. I think the routing strategy allows them to be very flexible here and then kind of have these fallback mechanisms switch between various models
control that orchestration routing layer, which is becoming super important here. It feels like we're we're back in the same place Well, you know, people don't care about the model so much anymore.
You know, it seems like, well, It's going to be the router and the gateway. we're almost looking at a post model market now.
is the things in and around and outside the AI to like the AI model to wit and Like is the future OpenRouter buys a consulting firm
You know I'm kind of really interested in kind of all of these weird configurations we're about to see as the core thing we've been paying attention to. what else is the sort of economic niche that you can stick into?
I mean, this this market is ebbing and flowing. And it seems like that right now the heartbeat is around routing.
Being able to route traffic to the specific place of which it, you know, But I suspect that this routing, it's going to become commoditized
You know, it's a very it's going to become a very crowded capability space. You know, I quickly looked and I mean, there's many competitors to There's LiteLLM Cloudflare, Portkey,
Right. So. Just just the amount of space, you know, that's where all of these little companies
And to me Stripe bought or rather. You know they saw that over the last what you know, six months
or so that the token growth by five x from 5 trillion to 25 trillion. to position themselves as the infrastructure for AI agents
and e-commerce just like they are today with, you know, just commerce in general. agents and models are going to be the drivers of commerce. But there is risk in this transaction. Right.
so and so do I think that this is going to be the underpinning? I think it, you know, of the economic drivers and decision space. But you know, you know, after it becomes commoditized there will be something else,
So, you know, will consulting groups want to buy, you know, OpenRouter, you know, type-ish or build their own organic today. Yes. right where they want to focus in their capital.
have a large number of users, which was part of the acquisition. Like it's I don't know the exact numbers, but something different. across free enterprise, whatever else, 150,000 active.
but this is not just an open source project somebody buys. they're buying the wallet, they're buying those paying customers What's what's really interesting is that OpenRouter has like 50 to 100 employees.
I mean, it's pretty small, but when you look at their market valuation, I mean, you're looking at maybe 70 million per employee, right? towards their valuation, of which they are 70 million per person.
And so, yeah, I mean, to his point, this is a, you know, a bet on growth. And if that trend line continues right. You know, I think Stripe might have done a good job, you know.
But again, commoditization is always the enemy of these types of deals. Yeah, I also like maybe to bring here a nice analogy, So I think Stripe didn't buy this for just the AI hype, especially the OpenRouter.
like while model makers kind of bleed cash competing on benchmarks. I think Stripe sits at the bridge and collects $0.02 on every car
So and I think it's a very interesting of those transactions happening, you know, all of these tokens being consumed.
You know, the routing right now is becoming very important place. I know it's a sore topic because you released one of these gateways
for free to the public, but what is the open side of this? Like, do we feel like, you know, that there will be more kind of open gateways that kind of are operating outside of the businesses?
Yeah, I think there's already more than 100 of these AI gateways in some shape or form out on the market today in the open source space. You know, I think you mentioned like 5 or 6 of them, but
Enterprises and users and so on need to have a way to manage a combination of agents, MCP servers and models And I think a lot of what this space is evolving towards
is that of agent management platforms or agent control planes, where of the capabilities enterprises need, whether they're using open source
or commercial software to provide governance, trust, observability, run and manage capabilities for their entire AI fleet.
Which, as you may know, is one of the most widely adopted, you know, sort of business credit card payment management sort of platforms. And so as a result, they have a really good bit of traction on understanding how
businesses down to the very micro level are using things like AI. And so they released the post, which is basically they're kind of August 2026 review of what's happening in the AI space and some really interesting stats here.
to build on some of the themes that we've been talking about today, source AI adoption is is continuing to rise over time.
You know, this is still, you know, a jump from 4.5% to 6.1%. So not a huge amount of the overall market, but they are seeing this kind of
really measurable rise in people using model serving inference platforms How far do you think that's going to go? Yeah the Ramp and looking at the 2026 Ramp AI index because it gives you a nice,
and what the businesses are doing because it's, you know, it analyzes about 70,000 businesses and what I found. it looks as though AI adoption, it's it's really uneven, right.
Because if you look at like the top 1% right of these AI adopters, they spend about, let's say $7,400 per month on AI, you know, per employee, whereas the top 10%, it drops down to $650, right.
And then the median or the number of that site right in the middle of all And this is, you know, whenever I quote monetary values, it's USD.
So that spread really shows you this huge spread right across companies. But there's a ceiling to the growth, right?
It's as if companies are saying, hey, let's give everyone access to AI. But we we have to meter the use, right, right, right. We need to cap the use in some way if it's getting out of control, especially
as we get these digital workers right online. And what I find interesting, right, is that the top 1%, right.
It's starting to approach the cost of AI per employee, started to approach the actual salary of the said engineers that are using these tools. So so now the question becomes, well, if there's if,
if we're paying as much for AI tools as we are a person, right. Do we need to focus in on productivity versus revenue. And so businesses I think will eventually have to grapple with that, right.
Assuming the economics and costs stay where they are at the moment. I feel kind of the, the end of this AI formal tax. Because what happens in 2023, 2025
And they're kind of the fear of falling behind. But in 2026, the era of the unmetered experimentation is kind of officially And CFOs now demand kind of measurable unit economic payback.
So either an AI tool demonstrably kind of reduces the headcount For example, PRs merge per engineer or, you know, drives, you know, kind of net So so I think what Ramp's data is exposing here is the kind of the vulnerability
of this ten wrapper startups, the which point solutions that merely summarize kind of PDFs or generate marketing copy. These are seeing kind of catastrophic catastrophic churn because foundation
model updates and native OS browser features do these things for free. So I think the true cost metric here is how do we measure So evaluating AI spend on a per seat basis,
And you know, I think the strategy has to shift here to evaluate AI on cost per resolved, maybe support tickets, for example, drop in from $18 human cost to maybe like a few cents
dropping like from say $400 to $12. So the high spend per employee is actually kind of an indicator.
if it's if it doesn't correlate with the multifold productivity gains. And I think that's where I think we need to start focusing on it. with these spends per employee in terms of productivity gains.
Otherwise it's it's not going to be useful just just to have these spends. But I also feel things are kind of segmenting initially. Let's give everybody access to these AI tools and and LLMs etc..
the software developers, they're actually using them substantially versus maybe other teams that might be maybe using them less so. for departments, you know, who really is benefiting the most
and getting the ROI versus other teams that might probably don't need as much. And I think I mean, underlying that is sort of the idea I don't know, like I guess there's there's one way
who say, hey, this might be actually kind of a bubble or maybe actually. dollar spent, it may end up being much smaller than we think.
I think the way I would look at it is that, you know, I've had have shifted from help us use AI or,
We've done a POC with AI to help. We've got agents written in LangChain, general GPT-3, we've got agents
We've got agents everywhere. We've got models. The problem we have is one. where they are built, or how AI is being spent,
you know, asking ChatGPT for advice on 1 or 2 things. It's these hundreds of agents, the developers and everybody else who are,
going into one of these agents and consuming them at scale, and the most expensive models and so on and so forth. Regulators are asking, hey, you know, there's a thing called the EU AI Act.
one of those big fines, for example, or the cost of regulating those models? the higher total cost of ownership is not just the token
It's everything else to manage it the people, the resources, And then you've got the cost runaway. Where today organizations don't really have a good mechanism to manage
things like budgeting, even tokens, let alone tying that into quotas, enforcement,
So this is one of the areas I'm most excited about. what we're building with watsonx Orchestrate. Product I'm I'm technically responsible for to try to get alignment
between return on investment, model cost. guardrails, security controls, everything else in between. So I will say AI is easy and this list shows us is true.
predictable costs and having predictable outcome. And a good return on investment is proving extremely difficult in these early days.
because as AI is getting cheaper and more capable, But just in this report, you know, you can see that
just over the last year token, you know, spend has increased 13x. You know companies can afford to do more. But then the overruns happen because you have all of these agents
that are compounding the problem, that we don't know what exactly they're doing. So so it's quite an interesting conundrum, you know, that I think the field is
And I think maybe this is kind of similar to what happened with the cloud computing when cloud computing matured between 2012 and 2015, EC2 instances, and they started introducing FinOps.
So the Ramp data here shows enterprise AI is also entering this FinOps era and really pushing the companies kind of they need to start thinking about how do we spend smarter, not less so because now we're entering
this maturity age and now the economics, the reality is hitting us harder. So I think a mental model here of how do we monitor these things, make sure that we're spending where things need to be spent properly.
So like if you look at engineering teams, to grow exponentially without pushback. Tools like Cursor, Claude Code, specialized code review models, etc.
from novelty to be an indispensable developer infrastructure. I think it's justifiable, but maybe in other areas not as much.
This is just a fun final item that I wanted to touch on. And I guess maybe, maybe I'll give you the last word on this Politico, which is a publication that covers DC and DC politics,
had a really interesting article that the Congressional Office of Legislative Council in the House of Representatives is awash in AI-driven slop, quote unquote, bills.
I think one of my favorite analyzes that anyone has ever done was observing really exploded in the Houses of Parliament in the UK.
And I guess we've all used AI occasionally to take certain shortcuts. AI to draft legislation. And I'll give you the last word here for the episode.
to introduce a load bearing bill with I think it's no surprise that's just like we're seeing an explosion in papers,
publications, homework, emails, We're seeing the same thing everywhere, including legislation. It's educating people that AI will make mistakes,
You're still responsible for the end result and giving them a way to do it. That is, I would say secure and governed, not just everyone going to, for instance, ChatGPT or online or somewhere, or,
you know, just posting the information there and posting back whatever they see. So I think legislation is no different than any other field. And rather than trying to fight it, there needs to be clear guidelines
what's safe, what isn't safe, and empowering folks to use it in a smart way as opposed to having AI write a law.
Kaoutar, Erin, thanks for joining us on the show. If you enjoyed what you heard, and we'll see you all next week on Mixture of Experts.