executive coaching is something that has traditionally been been done you know kind of personto person it's very hightouch uh as a result it usually it usually only happens at the executive level there's a very very small number of people uh that uh that organizations are willing to uh to support there's all these kind of lowv value added activities that that even though I like our expense reporting system it's actually a lot better than you know anything else that I've used in the past there's still opportunities for improvement net net they were able to uh to to to get to a point where they think they can get a 70% improvement in their product team execution that's pretty significant there's a growing expense eating into your company's profits it's your cloud computing bill you may have gotten a deal to start but now the spend is skyhigh and increasing every year what if you could cut your cloud bill in half and improve performance at the same time well if you act by May 31st Oracle Cloud Infrastructure can help you do just that oci is the next generation cloud designed for every workload where you can run any application including any AI projects faster and more securely for less in fact Oracle has a special promotion where you can cut your cloud bill in half when you switch to OCI the savings are real on average OCI costs 50% less for compute 70% less for storage and 80% less for networking join Sky Dance Animation and today's innovative AI tech companies who upgraded to OCI and save offer only for new US customers with a minimum financial commitment see if you qualify for half off at oracle.comai that's oracle.comonai eye n ai all run together oracle.comonai so go ahead and introduce yourself Jeffrey okay um thanks Craig so uh I'm Jeffrey Hammond and I am a global ISV product strategist at AWS i work primarily with software company customers and you know I've had a long career in uh in the software development space i started out as a developer uh for a number of years and then spent more than a decade uh building uh software development products primarily in product management product marketing roles uh from from small startups uh to large uh high growth and then acquisition uh in my case by uh by IBM uh after that spent a number of different years working with a lot of different software companies as they built their products and worked through uh multiple technology disruptions um mobile uh technology as an example uh cloud technology and now here we are again uh with disruption uh in the AI space and so that's really what I do as a product strategist I work with our software company customers to help them understand the opportunities that they have and then how Amazon can can help them take advantage of those opportunities and accelerate uh the way that they they they seize them uh to to drive profitable growth right yeah i mean you work with software vendors not not with uh people who are integrating a the AWS uh or building on AWS yeah yeah amazon has five different segments uh that we that that we organize customers by there's there's enterprise makes sense small medium business startup uh digital native businesses and then the fifth one is is ISV and the reason that we do that is because as you're probably well aware ISV software companies have particular challenges when it comes to building products they live and die by the success of the software that they build and because they are serving their own customers those customers often have specific requirements on them uh that that that create demands uh on the products that they build so with respect to to AI you're not just building a service that you're deploying internally inside the firewall to your own employees or maybe to uh consumers uh you're building products that you might have to deploy to thousands of businesses that all have their own unique demands uh with respect to what they want to do uh with those those AI capabilities and so you've got to be able to prepare uh for that when you design your products yeah and and these are independent software vendors that are building products on AWS or with AWS using Redlock or Sage Maker or whatever the That's right yeah or even considering it you know they may not be building on on on AWS yet but uh they think that there's an advantage that they can get by doing that yeah you did a survey with Forester Research um about um how successful ISVS are in uh in working uh with generative AI can you talk a little bit about that yeah yeah happy to um one of the things that that we do when we're when we're thinking about strategy and serving our our customers here is uh we like to dive deep and data is one of the ways that you can dive deep into understanding what's going on uh in an in an industry segment and so we commissioned this survey uh in the uh the fourth quarter of of of 2024 uh and we went out and uh uh Forester uh designed the survey uh ran it uh got over 650 responses uh around the world uh spread across three uh different regions North America AMIA and and Apac so it gives us a good perspective on what's happening around the world uh not just here in in North America and what we asked the forester researchers to do was to ask about implementation challenges um plans uh future uh uh goals what what these um software companies were trying to accomplish it gives us insight into how we should we should help them uh but we also wanted to make sure that that data was more broadly available because um you know it helps inform decision-m uh with our customers as well so it was a lot of fun to take a look at the the the details you know I'm often say with with surveys the data is the data and and it's interpreting the data that uh is is what I like to do and and then help our customers work uh through that yeah uh and there's there's been this rush to implement generative AI um a lot of u uh experimentation a lot of pilot programs uh but they've a lot of people have ended up with premature solutions uh and unproven business models high development costs um so they need to prioritize valuedriven use cases right uh to succeed can you talk about that about about how they do that priorit prioritization yeah yeah so first of all I think it's important to to to understand that that that sort of rush to uh uh to to build isn't necessarily unique uh to generative AI i've seen it in in lots of other major technology disruptions i think about um when the iPhone came out in 2008 we had lots and lots of organizations building apps as quickly as they could without a clear understanding of how they were going to drive value and then you know they kind of learned uh what worked and and and what didn't um and so I think that we're working through that process uh it's one of the the reasons that that I I think it is so important with respect to to to generative opportunities to work backwards from the customer and that's just something that's like second nature at AWS um you know there's a quote from from from Jeff uh Bezos in our in our 2016 shareholder letter that talks about the fact that customers are always beautifully wonderfully dissatisfied even when they are report being happy uh and their business is great and so if you go out and talk to the to a customer and you ask them a question like where where do you have to do things today in this product and it's still a pain a pain wherever you want to call about it and I I'll use expense reporting as a as an example you know I've been doing expense reports for over 30 years uh and I think about how I used to do expense reports when I had to tape receipts uh to pieces of paper and then I had to scan those pieces of paper and then I had to mail them in that was a high toil process but even today you know there's still a lot of toil involved in how I do expense reports um anything that's over $50 I've got to have a receipt for and sometimes that means I've got to go search my email for the receipt that was sent to me by the hotel uh and so there's all these kind of low value added activities that that even though I like our expense reporting system it's actually a lot better than you know anything else that I've used in the past there's still opportunities for improvement yeah and that's really the the starting point of of being successful in driving business value in AI is working backwards from the things that customers want so toil reduction is one of those um there's lots of good examples of of very successful use cases in that world uh uh accounting is a really good example of a business where there is high toil and there's also a shortage of labor if uh if you if you look at the research there are just not enough accountants to fulfill the demand and so you're seeing practically every accounting um software company that that we work with making large investments in generative AI to reduce the toilsome activities and give accountants more leverage healthcare is another example of that i was talking with a a company um last week uh that's a customer of ours and one of the things that they said is whenever um a lab runs a a blood test you know there's a whole bunch of of figures that come back and yeah a doctor or a clinician can take a look at those and spend you know seven or eight minutes kind of looking through the lab and trying to extract that information but what if we can automatically extract the most important information give them the summary at the top and reduce the amount of time that they spend scanning through that report from eight minutes to to to three or two and what happens if we can do that dozens of times a day it's a small example of toil reduction that adds up in the long running in the long run so so very useful use case uh retail um I worked with a a company uh um a couple months ago where they had a case where you know thirdparty selling is is a really big deal in retail now you think of all the third parties that sell on Amazon and um one of the things you have to do to enable that is you have to map products from one catalog to another and that can be a really really tedious process if you've got a thousand products to map so one of the things that they said was well if we could get to 90% accuracy um with with a with a you a generative process we could automate this and so taking that that identification of toil and then starting to work backward from that was one of the things that they did there are other examples content generation that's what we all know of um executable artifact generation if to me the perfect example of that you know code is is what we all think of code is an executable artifact that computers can can run but there are so many other things like an a calendar appointment the ability to automatically create that send it out and synchronize it um a data pipeline snap Logic is a really good example of a company that has a a strong meta model underneath its product and so you can either um understand those pipelines that exist and summarize them so that so that they can be explained or you can create them and there are just dozens of areas in the software space where there are these executable artifacts that are that are ready uh for for a higher level of of abstraction um so these recurring use cases and there are different types of them are the basis for creating a product strategy but it starts with understanding what's giving the customer um heartburn yeah yeah and uh you you were talking about uh the time savings for doctors reviewing uh medical tests uh I've talked to a lot of people a lot of enterprises about uh you know if if you use generative AI to focus on uh time saving on productivity uh it doesn't across a large organization it it certainly is is great for the user for the person in uh you know in in an accounting function or something that that can use Geni to summarize and it speeds saves them a few minutes but those incremental time savings don't necessarily flow to the bottom line you know if I'm an employee I've saved five minutes i'm going to check my personal email or I'm going to do something else and and there's something in the in the notes uh here that that were sent over by your team uh where you talk about finding u real valuedriven use cases uh that are that are innovative not simply timesaving can you can you talk about about how you do that how you uh identify those uh deeper value uh situations yeah yeah i think one way to do that is to start by asking the question where are there opportunities to serve customers or to do things that we have not been able to profitably do before and I'll give you a good example um and and just for listeners when you say customers you're talking about AWS customers you're not No I'm talking about the ISV's customers so again I I'm always working backward from the the ISV and thinking about their customers because if they can serve their customers well they're going to drive profitable growth um so here's a good example um executive coaching executive coaching is something that has traditionally been been done you know kind of personto person it's very hightouch uh as a result it usually it usually only happens at the executive level there's a very very small number of people uh that uh that organizations are willing to uh to to support with that model well what if we could open up you know that sort of career development capability to a much broader set of employees in the organization because we could do it at a much lower cost that's an example of where an innovation being able to take best practices being able to take the identified competencies uh that an organization values and expose them uh through uh a generative approach maybe an agentic approach uh could really unlock uh opportunity that to this point in time is just underserved if you look at what's happening right now in marketing around hyperpersonalization or clientelling you know the reality is is clientelling was only you you know really done with with your top tier customers because it was too expensive uh to do it uh for your your more casual customers well if we can change that equation uh then you can use it to drive uh additional value and and expand the market so so that's where some of the big ideas uh lie uh you know in and and I'm not saying you do those just just uh in instead of the little things because the little things can add up uh I think co-generation is is a is a great example i I shared the stage at reinvent this year with um um uh the group vice president from New Relic Surj Krishnan and one of the things he talked about was that they've been able to drive a 15% increase in developer productivity by deploying um you know uh essentially uh code uh co-pilot uh capability when you're talking about a couple hundred developers 15% is nothing to sneeze at from a from from an expense perspective you've got to do both yeah um you you also want to be careful that you you're doing uh implementing these AI strategies in a a careful way so that you don't lose trust of your uh customer can you can you talk about that yeah yeah uh for sure um this is actually I think one of the takeaways that that was a little bit uh uh surprising uh from the uh from from the survey that um you know I thought that we would see a little bit higher priority placed on customer trust and on data security and privacy uh in the survey results and and I think that that was a little bit of a miss from some of the ISVS because we do see that as important it's it's one of the reasons when um when we built bedrock by design uh the way that that bedrock is is deployed uh it's designed so that the data stays private that it never leaves uh you know the uh the the private uh uh you know environment uh that the customers set up because you know that that's a huge concern from our customers customers and you know again I don't think it's necessarily unique to generative AI i remember 10 or 15 years ago when we first had the opportunity to put source code up on the cloud with something like GitHub and there were a lot of organizations that were like well I don't put want to put my code up on the cloud uh what if I lose control over it that's my core IP and then over time you know we learned to trust it we learned that it was that that you know it could be it could be implemented in a in a private way uh that my code wouldn't get exposed and that trust level got to the point where essentially storing source code in the cloud is just the major way that most companies do it now we have to get to that same level of comfort uh with the data that customers are using because one of the things that we see over and over again is one of the best ways to improve the accuracy of these uh of these generative use cases is to include data uh through you know retrieval augmented generation or c augmented generation uh and so making sure that that data is safe critically important uh so so yeah I I think there's probably some more work to be done there yeah and is the issue that ISVS adopt AI solutions in their uh software stack and overlook uh privacy issues or security issues or is it that they're not communicating them adequately to their customers so that customers trust the the this new AI uh powered solution yeah I think it's part of the learning experience what I'm seeing is like when we work with a customer uh and we go through a P so much of the focus is on just seeing if we can get to the right economies of scale you know where the costs uh are are low enough the accuracy is high enough uh the performance is fast enough and then they start to worry about the additional illities or sometimes what they'll do is is is they'll put this out as a as a tech preview and then the enterprise customers start to ask these questions and then it's like okay you know we better make sure that this is you know that that that we've got these capabilities as well so I think it's it's mainly that um it's it's a uh a priority that we need to be more proactive about so we design it in from the start yeah and AWS when you're working with a software vendor um are you advising them of this or you're recognizing through the survey that this these are things that uh that they need to be paying attention to yeah when when we work with a software vendor we uh definitely uh call this out and it's it's you know part of the the the value uh that you know getting some of our subject matter experts involved in in in the customer when they're building uh provides uh so as an example if we're doing an engagement with um a software company in our generative AI innovation center it's just part of the discussion of working through the process of the use case and then building it out excuse me bless you excuse me comes in threes usually um yeah yeah and I should also say it's one of those things that um that we build uh in to the product strategy as well you know the idea of secure by design so a great example of that is is beyond the design of Bedrock itself and how it treats customer data and information is the capabilities that we layer on top of it something like Bedrock guardrails as an example uh specifically designed uh to reduce uh the instance of hallucinations to reduce the possibility of objectionable content uh sneaking through um you know some of the work that our partners do to be able to potentially identify issues like data loss uh that might be flowing through uh the prompts uh that are coming in uh to an LLM uh so um so so yeah that's where the value of using a cloud platform uh and and helping to accelerate the release uh can pay big dividends because otherwise you got to solve the problem yourself if you're doing it all on prem right um c can you talk then how do you work with an ISV I mean all these guys are uh unless they're geni native they're they're trying to figure out uh you how to use generative AI in their products right these presumably are people that have already built products on AWS platforms uh do they come to you do you reach out to them if they come to you is there a process that you go through with them uh you know a checklist or Yeah how how do you how does that work yeah yeah absolutely so first of all it it starts at at the account team level um I just came back from a a week and a half in in Australia and New Zealand uh meeting with with customers and I was talking to some of our uh account teams out there and one of the things they mentioned to me was that every single AM that that's responsible for working with ISPs uh is currently going through the certification process to be an AI practitioner uh so it's the same external certification that that our that our customers get so so they're the ones that that have those discussions when uh the customer has questions then they bring in the subject matter experts that are aligned with the particular services they also uh then will uh identify um opportunities to help accelerate th those those and so I already mentioned the generative AI innovation center that's one way that we would take an opportunity and uh and and um define it uh and then um move to a a proof of concept uh there are other things that we do there's a program that I actually co-run uh it's it's called the roadmap acceleration uh program and uh we do uh workshops with an ISV where we'll say let's take a particular use case that you think is high value and we'll go through the five questions that we use internally at uh Amazon which are part of our working backward process um from those five questions um we'll go ahead and draft a PRF press release frequently asked questions document this is the way that we build our products um as part of that then we also um have have reinforced that that standard working backward process with some specific questions around building generative AI functionality like as an example why is generative AI the best way to to to to meet this customer's need because it isn't all the time and I think that that's one of the things that that the Forester survey kind of showed that there was a little bit of hype here and it's like I need Genai uh it's my solution let's go find the What you really want to do is say what's the problem what's the toil what's the dark data what's the uh uh what's the uh uh the content uh what's the area that we've never served before and then is Genai the right solution and what specifically in Genai is it generating content is it automating tasks uh is it um you know being able to use natural language so that a larger user uh set can can use the product um and then you know that that question about data is critically important because one of the things that we see from a pricing perspective if you want to drive business value as a software company if you've got unique data that is hard to acquire uh if you've got um uh control of the customer through a user interface or a hero product uh if you are um making a creative user or a or a user that's in short supply customer if you're making them more effective you're giving them leverage those things are more valuable and allow you to um to look at uh userbased pricing models instead of consumptionbased models they let you look at potentially outcomebased models uh they let you charge a higher price premium so a great example of that uh one of my my favorite um uh software companies out there is Canva um if you look at their hero product there are just dozens of little places inside their product where they've implemented generative capabilities which collectively speeds the whole um user uh uh user experience for the creative and the net result of that is that they've been able to steadily uh increase the what they charge for their product because the value that it provides is apparent yeah it's the the pricing models that's something I've seen are also changing i know in uh customer service uh the old model is you charged by headcount uh and with the integration of of generative uh chat bots and seamless handoff and all that stuff these companies are starting uh to charge by outcome 100% yeah which is wonderful for the uh for their customers rather than paying a fixed uh headcount cost um c can you talk about uh you know these as these companies are shifting uh to uh hopefully sustainable growth and uh uh profitable sustainable growth are there steps that they should uh is there a checklist that you go down and and also before we get to that you mentioned a certification what certification was that that you were talking about uh it's the AWS certified AI professional i see is that for ISVS or is that for It's general it's for anybody um you know it's similar to what we have as our certified cloud practitioner which you know basically every uh uh Amazon employee when I joined a number of years ago that was customerf facing uh was required to get but it's the same thing that our our customers uh uh you know take as well from a training perspective and and this training is required or it's just service that Amazon offers customers no it's a service that we offer our customers to help them get up to speed to develop their own skills but you know it's it's kind of like physician heal thyself the best way to be able to advise you know customers is to make sure that all of our folks have that same level of expertise and that's actually something that uh I I I've actually found really um really um um invigorating here at Amazon that that that you know the expectation that that that that the sellers are going to have the same level of skill and competency that we would normally expect out of software architects or technical you know uh uh sales support um is one of the ways that we that we help identify those opportunities so that we can progress them yeah you mentioned uh the generative AI innovation center can you explain a little more what that is yeah that is an invest a set of investment uh investments that we made at a corporate level to put together a set of specialists that are designed to accelerate the big ideas uh that our customers have so it's not unique uh to ISV but we have a number of ISVS that have taken full advantage of that um I'll give you one example uh uh deputy which is a uh an up andcoming ISV out of Australia that uh you know our team has has worked with extensively in their case the use case that they wanted to focus on was what I would call an optimization use case they wanted to get more effective in how they built their own products which are for small businesses uh workforce management that sort of thing and so you know the the Genai innovation center worked with them to really push hard on uh toil reduction for their development teams and their product teams so code generation but also collaboration uh within the product teams uh how they prototype uh and uh netn net they were able to uh to to to get to a point where they think they can get a 70% improve improvement in their product team execution that's pretty significant from an external perspective uh BMC is a is a company that that that worked with the the Genai innovation center uh specifically around the price and performance of the models that they were working with and the use cases that they had developed because you know they wanted to get to this point where the cost of goods the the cogs uh for delivering this inference was low enough that they could you know make money off of it and so in that case the uh the the the the engagement with the innovation center was was really focused on performance and and uh and cost reduction and uh in that case I think they reduced the costs uh on an order of um of 40% or more I think so so yeah so you know those are just examples of um of the types of engagements that happen at at that level um in my case with uh you know the work that I do with with our individual AI uh uh um ISVS there's a kind of a stepwise process uh that that that we go through um we start with this idea of of the um the high priority use case in some cases it's an internal use case uh they want to increase and improve their sales effectiveness i did one where uh churn reduction identifying churn reduction early on uh in the renewal process was critically important because the customer had a churn issue uh with their existing product uh and so we worked on that in other cases it's product embedded so we want to you know focus on one of these toil reduction use cases um I I I think of uh um a case around uh environmental health and safety and there's a lot of opportunity there through processing video as an example where you can identify uh safety risks uh or where you can uh uh look at the result of an inspection and start to look at failure trends and identify you know what might need to be done to reduce the number of failed inspections around a particular uh uh class of asset so you can start with that use case and then um start to build out uh what has to be done to make that use case successful and I think I've I've a couple times I've mentioned uh the idea of accuracy and performance and cost those are kind of like the modern iron triangle uh for for generative AI because you have to start with that accuracy thing how accurate do we need to be for this use case to be successful that's that's kind whatever level that is is is is kind of like non-negotiable and once you've got that accuracy goal goal then you can say okay well how fast does it need to be and how much are we willing to pay to hit the performance and accuracy targets that we need to hit what you may find at that state is you simply can't do it cost-effectively today because the inference is too expensive or the models take too long because we have to use a reasoning model to get to the level of accuracy that we want and if that if that if you if that's what happens then you you put it aside and say okay we can't do this today but you know in the last two and a half years we've already seen the inference costs come down something like on the order of 90% so while we can't do it today maybe we can do it 12 months from now so we can revisit at that point but if you pass that that accuracy performance cost triangle then you can move on to the next step and you know in that that product mapping uh example I used earlier they set a goal of 90% accuracy and it took them three different tries with different models and different approaches to using that model to hit that accuracy target but once they did then they could see what their cost was and and and say can we can we make this work in our product and if you're embedding it into your hero product which they were we said "Well the cost is relatively minor because this only happens every so often." Uh and so we'll just put it in the cost of what we do and it'll be an additional differentiation and we'll um you know we'll push it to production um you do have to ask that that pricing power question that's really step four and and figure out the right pricing model and and their um probably the best work that I've seen on figuring out the right pricing model is is what BCG has done uh guys by the name of John Peneda and Jacob Kico uh what they have have observed is that um if you ask questions like what's the unique val d uh data that we bring to the use case and how defensible is that um if we can expose that value in our existing hero products instead of creating new separate standalone products like co-pilots or that sort of thing which we then go have to go out and have to sell as new products or additional products um if you can um create use cases that that create um customer leverage as opposed to cost reduction you can generally charge a higher price a more premium price so when I work with with with our our software company uh uh customers I'm always asking these questions because that helps us get to the point where we say "Yeah um we can do this and we can use it to drive profitable growth." So it's not rocket science you know but but but it but it works yeah this all of this u the generative AI innovation center is that available to all customers or even prospective customers or do you have to have a certain level of u billing at AWS yeah so to you do need to work through an account manager to start that process and they'll work with the requests and you know obviously there's finite capacity so we we prioritize requests uh based on on on the opportunity if it's a huge big idea the the differentiation is is apparent uh the opportunity uh is is is massive you know they're going to work with with that that software company yeah yeah uh what is or maybe you covered it and just didn't name it what's the roadmap acceleration program yeah that is uh an ISV unique program that we run and there the only requirement is that the product decision maker CPO VP of product um is willing to spend the time uh to go through the use cases with us so very low bar of entry uh you know we start with that that 2 to three hour intake process and then we go through the PR fact writing process we get we involve them in that that usually is is something that's a combination of email and meetings so maybe another hour or two and then from there we go into uh a couple day PC process so as an example you know in Australia uh we use the Melbourne Builder Lab which is you know we also have one of those in New York uh we bring their developers together with our prototyping teams and we use that PRFAC to start to to drive the idea and we see how far we can get we test the validity so that's an example of a of a of a program that's kind of in between something like the Genai Innovation Center and something that's that's more tailored around an individual customer like an immersion day that um uh that that that the bedrock team would come in and spend time or or a or a subject matter expert so we really try to meet the customer where they want to uh where they want to meet us yeah isvs um when talking about deploying general AI infrastructure over the next four to 12 months or I shouldn't say ISVS maybe it's all respondents to the Forester survey that 46% uh mentioned leveraging cloud-based Gen AI platforms and only 30% expressed interest in in building infrastructure genai infrastructure that is u inference that's basically inference right you're talking about uh leveraging API inference well there's also training as well so first of all that that stat is all software is software company only the the survey that that the Forester folks did only looked at companies that said our business model is building software products for businesses so ISVS um so so you know we have a lot of software company customers that choose to train their own models on services like SageMaker um you know inference is is obviously where they make the money after they've done that uh so you know that's that's probably the uh the the primary uh focus and I think a lot of it has to do with just the desire to to keep up i mean I think of what was it uh second or third week of January where in in the course of a couple days we had deepseek uh and then we had Quinn and the entire uh financial market evaporated for a couple days uh and then by the end of that week we had DeepSeek support in inside Bedrock and you know the other cloud vendors uh you know had very rapid uptake too um that that matters because you know this this idea of I've got a model and now I'm done and I can move forward with it you know that that's that's not reality uh you know you see the software companies already that are pushing hard on claude 37 uh as an example and and and we're talking about end of lifing I think claude 30 and it's only what a year old you know that that level of speed is just um is hard especially you know when you know it can take a little bit of wow a little while when you're a software company to validate a new model and assess how well it's going to work uh for you uh and so with the rate of speed of all these models coming out um model evaluation has become one of those factors that can really accelerate product strategy so things like uh um you know prompt evaluation uh or or uh prompt optimization uh the ability to uh you know use uh FME val and SageMaker to very very quickly test new models uh is one of those things that that ISVS value highly because you know a couple percentage point reduction in your COGS or a couple percentage points improvement in your accuracy could be the difference between we can't make this use case uh work now and now we've crossed the barrier so let's go ahead and implement it because we know we can do it profitably yeah yeah and when I was saying only 30 30% expressed interest in building Gen AI infrastructure I assume that meant taking an open-source model and training it uh what does that mean no I think that means on on their own premises you know in their own environments in their own data centers right i see yeah yeah but in terms of u uh fine-tuning existing models I mean you mentioned Deepseek's available on Bedrock now right yep um if someone wants to use Deepseek uh and and they want to uh fine-tune it uh do they do all of that on Bedrock and do you guys offer support for that yeah on the models that we do support uh fine-tuning for some models um I'm not sure if we actually support deepseek fine tuning or not i'd have to I'd have to to look at that and and and give you the update on that but yes where we can we support that that that capability and actually that's one of the things that that software companies are often uh interested in doing because they do have uh proprietary domains proprietary data um you know domain specific terminology and that that uh fine-tuning can yield a few extra points uh of accuracy and it's worth the additional cost uh to be able to support that you know one of the uh the issues with with with tuning is that you then have a a model that is is is somewhat unique and so you need to be able to support that and so you know often that'll get uh tied to provision throughput in bedrock uh to be able to uh to execute that so you've just got to make sure that that that um that accuracy performance price um triangle uh is is reflected when you're when you're doing that evaluation yeah um from where you sit you can see uh enterprises and ISVS uh in particular in this case adopting AI as I said at the beginning I know there's all kinds of experimentation and pilot programs but there have not been I mean the the diffusion through uh the product space there are a lot of new products but has not been as quick as one would expect I I mean everybody now has a little chatbot on their on their product but it it the products themselves haven't necessarily evolved do you where do you see that process do you think that things were changing and still are changing so fast that a lot of people started experimenting and then just backed off and said "Well let's wait and see how this settles out before I dive in again." Um I actually think it's a little bit of a different challenge uh and and I think there was some interesting data in the forester survey when we looked at some of the issues around data challenges because as we see data is one of those things that that really dramatically improves accuracy i think some of these early experiments um exposed some of the the the challenges uh in their existing infrastructure so for example among the the respondents that had implemented at least one um uh generative AI use case or or or product um silo data came back as a real challenge uh to uh to their efforts now when you look at the data from those that had done more than that and were expanding uh the the use cases that they were building so they gotten kind of they they they you know had are a little bit more aggressive silo data wasn't as big a challenge but um integrating with existing systems then became a a challenge and so I think part of the reason that you're seeing all this focus right now on agentic frameworks and uh agentic technology is because of that ladder issue so you know you've got to solve these challenges before you can take the next steps in unlocking uh uh the value uh of of of toil reduction you know you don't get toil uh reduced without having tools that agents can use to actually act on the user's behalf uh and so you know that that that blocking and tackling has to be done uh to unlock that next level of value yeah yeah and it's being done i mean there there Yeah there's I mean AWS is is uh where where are you guys in uh in a agents and agentic workflows yeah uh so we've got uh Bedrock agents uh which has uh support for multi-agentic communication um boy it's hard to it's hard to believe that that you know MCP uh model contract protocol has only been around since November it hasn't even been six months right yeah and no one no one really was paying attention to it until you know January or so i mean it took a little while for people to understand what it was yeah yeah you know when I started paying attention to it when I saw the um um the the the the growl team on on on the Java VM implementing an MCP server and it's like okay so now we're opening this up to Java programs uh and when you you know you started to see MCP servers for for databases and it's like okay this is you know I know people describe it as USBC for AI um my my kind of early description was it's kind of like ODBC uh for for for for generative AI and getting access to the data and being able to use it um is is is critical and so you know when I started to see you know GitHub repositories that showed how Nova uh was integrating with MCP it was like okay you know this is this is becoming uh what what you know when I was an analyst we would have called a de facto standard right i think we're getting there very very quickly there's still more work to be done uh and and you know that's actually one of the concerns that I have it feels like that there are so many agentic frameworks out there there's a risk of what I would call islands of automation so I've got an agent that's working in um Agent Force and I've got an agent that's working um you know that maybe uh is is part of uh Boommy or or HubSpot how do I start to put these things together it seems like as an industry we have to do a little bit more work around making sure that that these agents can work with each other regardless of where they might be deployed yeah yeah yeah that's uh that's fascinating and I guess that's one of the challenges is which of those frameworks do you commit to right yeah i think the big you know the big thing from our perspective is is is um making the um the the the the calculation that there's probably not going to be a single framework that an enterprise is going to use they will be you they will end up with multiples and federation is probably an approach that that benefits everyone and and so you know I think that's that's you know the strategy that uh that that that makes a lot of sense going forward because otherwise I don't see how you you you rein in the fragmentation that is already out there yeah and and the the link between the federated states would be NP uh MCP or or is something I think I think there's a part of of a of certainly a part that it plays um I think um what Google's done with a A2A agent to agent is is pretty interesting as well um certainly some uh um you know there's certainly uh a lot of of um of opportunity for innovation uh here still yeah yeah so uh have I is there anything that that we haven't talked about that you're uh doing that you'd like the listeners to hear about uh no it's amazing we've we've done what we time has gone by so fast uh I want to be respectful uh of your time i guess maybe what I would just say is is is um this this um this disruption I think at least in my opinion again I've been doing this for for more than 30 years um is potentially the biggest disruption to how um humans work with computers that that I've seen i mean if I think about the the last 50 years of history from the mainframe on um we've always had to expect the the user to respond and adapt to the system you know because we throw up a form uh or we uh uh we give them a command line and they've got to know what to poke the system with to get it to do something and you know to me the the power of what's happening right now is is the evolution of intentional um interfaces that I express what I want the system to do for me with the expectation that the system is going to respond and adapt to me and you already see some some beginnings of the power of that like um one of the things that uh DUVA uh reported when they when they started to implement um uh code generation co-pilots and and doc you know were were automating documentation is It reduced the onboarding time for new developers by 30% that's pretty cool um I was working with a a company in the healthcare space that was talking about their own own onboarding experience and one of the things that they said was instead of having to throw up a bunch of forms that the user has to fill out we can ask the the new customer what do you want to accomplish um do you want to run a marathon yeah I want to run a marathon i want to lose 10 pounds i want to keep my my uh uh you know my my lipids uh under control and then the system can react with personalized capabilities based on what the customer wants it makes me so excited for for what uh what we can do with this technology especially as we continue to drive the cost down to make these sorts of use cases possible that's right uh yeah it's an incredibly exciting time um okay I'm going to end it there there's a growing expense eating into your company's profits it's your cloud computing bill you may have gotten the deal to start but now the spend is sky high and increasing every year what if you could cut your cloud bill in half and improve performance at the same time well if you act by May 31st 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