I would say there are perhaps the biggest one for me is let's sort of conceptualize this more as an ecosystem play as opposed to a single model or even a single platform right I mean you know whenever I at least for me having grown up at Microsoft having seen whatever four major platform shifts u I sort of fall into that um camp where a platform is defined by fundamentally its ability to create more value about the platform versus what's captured in the platform. And so if you you view what's happening right now, I think this morning's keynote was how can any company whether it's an AI native company or a traditional enterprise company participate as a firstass participant where they can point to AI they created. Right? It's not that they don't use other people's AI. Of course they will. But to me, what's the path? What's the recipe? How do I do it? What does a stack look like? What does the tooling look like? What is valuable? How do you do that? That's it. That's sort of our job to do.

So, so the thing that we wanted to do with the MAI models was to build and as Mustafa talked about first of all a great lineage right starting with pre-training uh with very good data quality uh doing all the abilations making sure because in in some sense it's become even harder to build a clean lineage model just because there's so much stuff out there um that you truly need to ablate out to be able to have a fantastic pre-trained model. In fact, that's one of the challenges of a lot of the open rate models is they look great on one benchmark or two, but they're not great on practice. So, that's why in fact even in our FDES are are pretty gone really excited about these MAI models because how the heck can a small 5D model hill climb? uh and it goes back a little bit to what I think is ultimately the key thing to do which is try to pursue finding that cognitive core. Uh so to me starting with with a clean lineage then creating that ability for companies to be able to use this right not just as a generalist but to create their own specialist by building this hill climbing scaffold around it right so it's not just the model but you have a hill climb scaffold around it then you will start building your rle you will start collecting the traces most importantly you'll have private eval because we know all the eval out there are good, interesting, but they're not really that critical at this point because they all can be maxed. And so the point is each company will have its own private eval. And so that end to end platform story around our models is sort of uh what I think is interesting.

I mean I think the the thing when that I reflect quite a bit, right, which is sort of obviously I got into all this when I got excited by the the scaling laws paper and you know when you know even the open AI partnership came about when those folks said hey we're going to really throw a lot of computer transformers uh and they've helped right the thing that I always look back and say wow these things um do have capability that they're climbing up I mean this you know this crude way of saying it is intelligence is log of comput kind of works. Now what I think we underestimated perhaps is the real world complexity of deploying these so that they actually deliver the value in the real world. Right? So the outcomes as measured by any benchmark is interesting important but the true eval is when people out there are able to do unique things that they only can value and it's very measurable right that I wish we had sort of even like had more in our consciousness right which is as an industry because right now I think when people say wow I don't want a token max it's an artifact of us not having thought ourselves as an industry that we are using tokens to create value every step of the way.

coding is worked so well that we now have to rebuild the IDE right I mean it's kind of nuts to see what we launched is like oh my god I have these 100 agent sessions I the cognitive load it transfers back to me as a human is so excessive that now I need a new UI oh by the way like the the chat as the only artifact was also impossible. So that's why we need a canvas. So it's kind of interesting for all the things about where is software needed or where is UI needed. Uh you kind of need that even for code right in a fully agentic world. But that said, one of the things that we are starting to see, we started seeing with co-work, but even some of the work we we showed with auto um um autopilot, right, on what you see with claws is a good one because if you sort of think about a lot of human capital is doing the glue work, right? If you now can augment that with tokens slash agents that are longunning, durable, right? then your ability to scale even what is still judgment and glue work gets amplified like coding does. Uh so you can like I'm positive that 6 months from now we'll all be saying oh wow like all through night the night there was a bunch of stuff that all these autopilots that I have working on my behalf with my delegated authority so to speak right I can sort of given even my identity did a bunch of work then of course I'll need my new ad to say what did you do like am I did I do this work and so on so I think that that's where compressing of workflows uh complete reading of tasks. Uh that's where I think a lot of the value gets created.

so in some sense you kind of want the harness to define the models the the data uh and the tools and so that you have a loop across those three and so what we are trying to first of all make sure is each of our products that we build right whether it's GitHub copilot or the security co-pilot the the stuff we showed with mdash or even the discovery for science it doesn't matter. All of them are multimodel harnesses um with tools access so that you can do this progressive uh disclosure of tools even so that they're token efficient. Uh and then you're feeding it with very rich context because that's sort of the other hard lesson we've learned in the last two years is oh my god the amount of work you need to do to prep the context layer uh such that your plan can execute in the most efficient way is where the magic is. So we have in our case we have the GitHub harness which essentially we're using across all our products. It's available in foundry and we're open like you can use your llama harness whatever or you can use the um uh you know any open harness or any harness of yours and train with your tools and multiple models and your context and so that's the pitch because right now a lot of dialogue is um hey if I train the harness plus tools and the model together you get evals and what we are proving out is and the best example of that is what we did with mdash right because when it launched um it found bugs or vulnerabilities that were not found by mythos. Uh and so there is existence proof I would claim that you can have a multimodal harness uh that can in fact be more uh performant in the real world.

But the thing that is is it should not be a limiter to other people achieving that same success. Right? That I think is the core difference which is the the network effects this time around around intelligence are such because they learn from data and not really lots of data. It's just the few samples that you have to see to understand what's novel about something. So that's why the game becomes how to protect. So that's why I would say every company having private eval maybe the biggest IP right I think about it like what's that private eval that you can then use even a frontier model to hill climb on and not leak the traces maybe one of the biggest drivers uh of IP like so in other words another asset test is you have an eval that's private you're using a a give model A can you switch it to model B and you climb up. If you can, then you're in control. If you can't, you're not in control. And that's where even the harness decision becomes super important, right? So therefore, having an open harness, letting all models come in, having your evals, your context, your tools help you hill climb, I think is the skills that an AI native startup needs, a SAS company needs or every enterprise needs. Yeah, I think in a very real way you are Microsoft historically as an operating systems company and then become a cloud company. Maybe like the third act is that you're harness or eval whatever whatever the the sort of conglomerate of concepts that you want to put together. Um I I think like enabling every company to have like frontier intelligence or what what I forget the the exact term that you used um is the the mission right that is that is the platform promise that you build with us you will get your intelligence uh for your data.

I'm definitely in the camp that this is going to be about expressing new forms of human agency and ambition even as token capital goes up, right? So let's say a any corporation has lot of tokens and lot of human capital. The question is how do you compound the two? So if you have a like if you take in teams I have a bunch of agents doing work and a bunch of humans doing work and the traces between those that is really important context of how that enterprise is creating value then that goes back to train not a generalist model but to tain the train the company veteran agent uh right that is super valuable again right which is when a company says it should in fact go onto the balance sheet is how I think about it right That's so in fact they may be like human capital was never possible to go put on a balance sheet uh because you then know how to capture the tacet knowledge whereas now I think you can with the agents that have learned through the through time through time through all the traces uh so that's what at least we think will happen.

Now you kind of get to relitigate that vertical stacking, right? So I still think for example that data model that you built underneath every SAS application is super good, right? Like why reinvent it? Like I my general ledger better be a general ledger. I don't need new schema creation. Uh in fact that entity relationship uh is actually pretty good robust thing that I want to feed. And you want to be stable. That's right. Then same thing with business logic, right? If if you look at uh we have this product called PowerBI, right? It was like dashboards galore people created. The beauty underneath that dashboard is a very rich semantic model, right? Someone took the pain to create a dashboard and do all the measures. And you want that that's business logic, right? I want that to be available to me. So I think the challenge of the SAS business model is we packaged one way we now have to learn how to unbundle these things and rebundle in new ways and discover new business models right I mean if you look at it what's happening today with Microsoft 365 is a great example right we have this thing called work IQ in fact like what we're realizing is oh my god like if you look at in fact there's a historical parallel to right we sold first exchange and share SharePoint and uh you know before teams we had a thing called link server and what have you and we thought oh that's all going to move to the cloud but little did we realize that oh the number of people who will use servers in the cloud is 10x 100x right because people were not buying servers they were just buying a subscription. The same thing is now happening with M365 because with Work IQ, we have exposed what is perhaps the most important database in a company that never got used as a database because it was only captive to our apps, right? It was all email operated on it, Teams operated on it, Word, Excel, PowerPoint, SharePoint. But now, like this is one of the coolest things I get to do with work IQ. I go to a GitHub repo and I say, "Hey, I attended a bunch of design meetings last week related to this repo. Can you capture all that and tell me what changes I should make?" I mean, think about that, right? It literally can go look at all those transcripts, come back with a plan to change a code base, right?

the per user pricing is really an artifact of someone creating a budget needing certainty right because it's the most important thing like somebody wants a budget they need a per user and and per user is just a set of entitlements to usage right that's kind of what it is and so the way is the first bundling will be take some usage bundle it into per user stacks and you know then sell subscription so subscriptions I think are going to be there per user is going to be there then the next big thing will be consumption so people will say I want consumption and it's also possible that people will say I don't even want to pay for any of the subscriptions or the consumption. It's outcome. But remember, most people love outcomes until they have an outcome. Because once you have an outcome, it's like giving away royalty, right? I mean, like I I've talked to customers who love, you know, outcome based pricing and I say, "I'm all in until they oh my god, like what are you talking about? You're sharing in my outcome." No, no, no. I want you to go back to per user pricing and I want you to consumption price. Right? So, I think that debate will go on. uh but and all all the all all of these business models have a particular time and a place versus one to rule them all.

I think the the thing in these type of transitions is to have a conceptual model of how work can change to go after outcomes that you could hardly imagine previously. Right? In fact, Kevin Scott has this nice line, right? which is um when you can make the impossible like when you're making hard things easier that's sort of one point of leverage but true ambition is about making the impossible possible. So now the thing that is missing a little bit in all of our organizations is what is that new conceptual model of what can we build what was impossible and what can we build and I'll give you one example of this right which is I take great inspiration from sort of the people who were managing the Azure network and they came to the this was from even last year you know we were scaling you saw that I I talked about sort of how we built in the last 15 months more Azure capacity than we built in the first 15 years. I mean, it's crazy, right? It's pretty wild. And it's the same team. So, they saw that and they said, "But this just ain't going to work if we don't reconceptualize our work." So, they built essentially they said, "Our job is not to do Azure networking. Our job is to build the agentic system does that does Azure networking, right? ... so they were saying look I we don't need headcount we need tokens in order to be able to manage uh our operation that reconceptualization of what their work is right they they basically took their work and made it meta right that meta work is now their new work.