Alex Porter

From Birth Doula to the Pentagon—Alex Porter on the Nonlinear Path to AI Infrastructure

Bio

Alex Porter is the CEO of Mod Tech Labs, providing the control plane for secure AI orchestration across critical infrastructure. Operating beneath the application layer, Alex builds the engines that automate compute and simplify legacy integration for the Defense Industrial Base and media titans like Paramount and Sony. As a dual-use innovation partner for OEMs like NVIDIA, Intel, and AMD, she navigates the high-stakes intersection of hardware and compliant intelligence. A recognized innovator and leader in technology, Alex is leading the future of "computational authority," helping organizations and leaders secure their operations within the modern "Operating System" of trust.

Intro

The conversation with Alex Porter, CEO and co-founder of ModTech Labs, elucidates the profound journey that underscores the notion that there exists no such thing as an overnight success. The episode delineates the intricate evolution of Alex's career, from her early endeavors as a birth doula to her current role in developing advanced AI orchestration platforms. We explore the significant challenges and triumphs she has encountered over the past six years, emphasizing the critical importance of resilience and strategic foresight in the startup environment. Throughout our dialogue, Alex articulates how her company is addressing the pressing need for computational efficiency within both the defense sector and the entertainment industry, thereby demonstrating the multifaceted applications of her technology. By leveraging her diverse experiences, she offers a compelling narrative on the necessity of grit and adaptability in the pursuit of innovation.

Conversation

The conversation with Alex Porter, co-founder and CEO of ModTech Labs, unveils a compelling narrative of resilience and innovation in the realm of technology and entrepreneurship. From her unorthodox beginnings as a doula to her current role in developing advanced AI orchestration platforms, Alex's journey epitomizes the notion that success is rarely instantaneous but rather the result of sustained effort and strategic evolution over time. In our dialogue, Alex articulates the intricate transition from her initial ventures in virtual reality to her focus on creating deep infrastructure software that empowers organizations to maximize their existing resources. She shares insights into the significance of communication, the importance of understanding stakeholder needs, and the challenges inherent in adapting to a rapidly evolving technological landscape. One salient takeaway from our discussion is Alex's assertion that true innovation stems from recognizing and harnessing the potential within one's existing capabilities rather than seeking entirely new solutions.

Takeaways

• The journey to success is often lengthy, taking approximately ten years to manifest fully, indicating that consistent effort over time yields results.
• Alex Porter emphasizes that her company's evolution is not merely a pivot, but rather a natural progression that has always been inherent in their foundational work.
• A critical aspect of business lies in effective communication, particularly when discussing sensitive topics, which is vital for building strong relationships.
• Utilizing existing infrastructure efficiently, instead of acquiring additional resources, can lead to substantial cost savings and improved operational efficiency for organizations.
• The implementation of a comprehensive AI orchestration platform can significantly enhance the efficiency of compute resources, enabling organizations to optimize their infrastructure.
• In the rapidly evolving tech landscape, companies must adapt their messaging to different stakeholders, ensuring clarity and relevance in their communications.

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Hello. Please meet today's guest, Alex Porter.

There's no such thing as an overnight success. Right. It takes roughly 10 years to get there. So we're six into this company. So maybe in the next four years we'll be an overnight success.

I don't know.

What do a berth doula, a VR studio and a secure AI control plane for the Pentagon have in common? They're all chapters in Alex Porter's story.

Alex is the CEO and co founder of ModTech Labs, and her path to building deep infrastructure software for the defense industrial base and Hollywood studios like Paramount and Sony is anything but straight.

From helping families prepare for childbirth to designing in AutoCAD, to launching VR training tools, Alex kept following the thread until the compute layer that was always running underneath her work became the whole business.

In this conversation, we get into what it actually means to build an AI orchestration platform, how she's helping organizations squeeze dramatically more out of the infrastructure they already own, why she refuses to call her company's evolution a pivot and what grit looks like when you're six years into what might be a ten year overnight success. Well, hello Alex, and welcome to the podcast. Thanks for joining.

Absolutely. Thank you for having me.

Let's start simply. Tell us all where you're from originally and where you live now.

Well, technically, originally, I was born and raised in Arkansas until I was about 10, but then I moved to Austin, Texas. So I lived in Austin middle school, high school, college, got married here, and we are back Austin adjacent.

We've lived in many other cities as a family, including upstate New York, California and Canada.

You have the tiniest possible Southern accent in your, in your speech, but it's really, really tiny.

I tried really hard to get rid of it in middle school because I did not move. I moved to a big city with a very thick country accent. But it's always got a little, always had a little hint of it.

Of course, if I, I have to make sure I, I don't make you say y' all or anything like that,.

Because then I would just Absolutely gonna say y' all every day, all day.

Okay.

We're gonna be talking about your current startup, but go back in time a little bit and, and tell us sort of before this startup, where, what sort of places did you work that, you know, got you the experience to then. Well, you know, take the big leap.

Yes. So my, my journey is a wything one I'll do. I'll give you the, the Cliff Notes version. My original sort of career path.

I was a nanny And I was a doula. So I worked in people's homes with their most precious thing, their children.

As a doula, which is like a birth attendant, not in charge of medical things.

I helped parents sort of prepare for the birth of their children and gave them information because I was, you know, well, well studied on many things. And this is a wide variety of types of, you know, births and choices. And you know, every family has their own journey.

But what that really taught me fundamentally is how to communicate with people about very important, very sensitive things.

And at its core, business, no matter whether you're serving someone and their family directly or you are serving a business, you know, a corporation, you are building relationships and you're communicating clearly. So that is what I really learned in my early career. Then I decided to go to college.

I originally was studying child development, realized that was not necessarily the most effective long term path for me and I pivoted into interior design with a minor in construction technology. So my background is actually the it's and it is a Bachelor of science. So it's akin to architecture rather than, you know, home decorating. Right.

Those are two flavors that all sort of mix together in interior design.

And fundamentally, you know, what I learned a lot about was everything from, you know, aesthetics to color theory to space planning to 3D software design. So AutoCAD and Revit. And after college I did some freelance work in design and wasn't necessarily fulfilled with that path.

And my husband, who I got married to after college and I decided to start our own business after we had our first child. And his background was games and movies and he was always working in studios helping them build out automation, optimization and workflows.

So we were helping build launch titles that actually were on the Vibrift and PS VR when they launched. And from there we ran that business together for almost five years before we started ModTech Labs, which is our current company.

And we learned a lot within that business around how to how scalable content was or wasn't, how challenging it was to change sort of genres and create these bespoke projects and ultimately pivoted and shifted that toward a product focused company, which is Montech Labs, where we're really working at the compute level to help make infrastructure faster, delivering that with a security first mindset and enabling a wide variety of workflows.

Well, you Use the word pivot both in the context of your personal choices, your career choices, and then of course the way those of us in the startup world think of it as a strategic change of direction for a business.

And maybe you could say a little bit more about what it was originally, because you described it as automated 3D file conversion, I think is what you explained to me. Tell us what that is and then how that could have possibly connected to what you're doing now.

Sure. So I'll start a little bit back at our previous company so that the, what we called an XR Studio.

So it was augmented reality, virtual reality and the like, really helping to create content. So it was everything from training to education.

And we worked for large corporations, helping them build out these one off sort of project based experiential tools is how we looked at it. So one example we worked for a large wheelchair manufacturer called Quantum Rehabilitation and we helped them develop something called iDrive VR.

So it actually uses the, the technical real world physics from a power wheelchair and it brings that into the VR world so users can train on the physics in a virtual setting and allow them to understand how the controls work more effectively.

So there are a lot of different types of wheelchair, wheelchair controls, which could be a joystick, a sip and puff, a head drive, excuse me, et cetera. And so that is an example. So we took a lot of real world content.

So the physics, the, the design of the chair, we put it into virtual settings and then we allowed the user to control the experience in a gamified manner so that they could learn how to effectively use their drive controls. So with that really helped us learn a lot of things about how to take and make content sort of at scale. Right. So content is all the visuals. Right.

It could be the virtual wheelchair, it could be the virtual scene. There's a lot of different pieces and parts that have to go into that.

So taking those visuals and making them easier to, to build, easier to deploy and making sure that they were going to play back well. So you know, the specs for your computer versus your cell phone versus a headset, they're all very different.

If you've ever seen any of the behind the scenes stuff from the Mandalorian, for instance, you will have, you know, understood that they're. They're running what is called a game engine or a real time engine.

Same thing that will play a game on your phone, the same thing that will run in a VR headset, or the same thing that will run on a console. If you're playing a game, they run these with the content behind the actors and actresses.

So there's a lot of real time, real world balancing that has to happen to make sure that it plays back, it looks great, and it's really effective for the scene you're trying to build out. And that's where we started with the content piece.

But underneath the content, we've always had a compute layer where we're focused on actually making sure that the content itself is going between your ram, your cpu, your gpu, your, all of your compute pieces, and it's running more efficiently. And so where we have expanded to. I hate, I mean, honestly, I don't even like calling it a pivot because it doesn't feel like a pivot to me.

It feels like it's always been there. It's just the, it's the headline now instead of the byline. Maybe that's a pivot, depending on who you ask.

And so now the story that we're telling, especially during this sort of AI push in the world, right, is use your compute more efficiently. And so we have trained some machine learning algorithms to help do that more effectively.

It still works great if you're doing 3D content, but that's up here at the application layer.

Where we are, where we are more focused and see more of a value proposition going to market is the computation layer to help make sure that companies can use their existing infrastructure and hardware and compute better so that they can build everything, every piece of content, more efficiently.

And how are you including AI in this picture such that you can call it an AI operating system?

It's a, it's one large term for a lot of different types of, you know, computer intelligence. Right.

We've had everything from computer vision, where we're actually able to look at the content within the system and analyze it for a visual output that we need. We have machine learning, which I've mentioned where we're actually training to get a predictable outcome.

We have used CNNs, which are a different flavor. Right. We've used GANs. A lot of folks are familiar with LLMs, large language models like ChatGPT and the like. We do have some technology in there.

We don't, we don't typically go out to the larger foundational models, but if our clients want to plug in their subscription to a ChatGPT or a Gemini, they can do that and add that into the system for us. The models that are within our system have been trained on our system.

And so they'll help guide a user, like a wizard within the system to build workflows, build process, and have observability into what's happening on the compute so side.

So foundational AI has always been really important for us, but it is, it is a wide type of varieties of AI and it really, you know, depends on how the user, the end user, wants to plug in other types of AI to their system that's capable and, and ultimately being able to use your compute more efficiently will allow you to use more AI. Because one of the big issues today is that there are not enough GPUs available, you know, for machine learning.

And that makes it really challenging, especially because a lot of the GPU compute is Nvidia specific because of CUDA. So CUDA is. About 80% of the machine learning models require CUDA to run.

And that means that if you don't have Nvidia GPUs, you're going to hit a blocker and being able to implement a lot of the tools that are out there. One way that we've solved that is we actually have a cross computational model that will allow you to use any type of compute, not just gpu.

So gpu, cpu, npu, lpu, there's a wide variety of compute types and you can do that so that you can use your existing infrastructure more efficiently. That's all it really boils down to in the long run is efficiency.

Use what you already have, don't pay for extra, don't go searching for, you know, compute instances and save yourself a buck and some time.

So what's the, what's the real powerful message hook that you where you're plugging into a, a very significant pain point with these companies?

Yes.

And you say here, this, we understand your pain. It's this. And we have a solution.

And you've talked about compute efficiency, but you've also talk a lot about smoother integration, better utilization of what you already have, which is sort of a way of talking about integration and so, you know, kind of give us the problem solution in sort of the pithiest way that you describe it.

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Absolutely. Being able to run advanced AI and your legacy systems with one primary hub platform where it all plugs in, allowing for a massive gain in efficiency.

We actually have a 2.6x speed up on process initiation itself. And in aggregate we're seeing a 60% increase in efficiency across systems when they deploy mod.

Are you in competition with other small companies or are you really competing against some of the big guys that are identifying this as a widespread problem?

So it depends on which angle you look from.

So for instance, on the OEM side, so Nvidia, amd, Intel, we have partnerships with those companies and they're interested in their end user being able to more effectively use their hardware.

Obviously that's, I don't think that's going to stop anyone from buying the latest chipset, to be, to be honest, because there's still a lot of gains to be had from getting the newest, latest and greatest. But no one has a homogenous infrastructure ecosystem, right? They don't have just Nvidia, they don't have just Intel.

The reality is they have a lot of different types of, of companies and compute in their, in their stacks. We're working a lot with legacy companies, and by legacy I typically mean companies that are running their own infrastructure.

They want to manage it for security purposes, compliance purposes. They've already done that initial investment into the hardware side.

So they want to use it more efficiently rather than having to spend to buy more or less. You know, a lot of them don't even have the ability to go to like public cloud.

It's not possible within the ecosystem that they have to, they have to meet for compliance and standards.

So the, you know, the next piece of that is we definitely, you know, would be seen as competition for what I would consider more traditional sort of workflow tools that are focused on workflow. So if you think about Zapier N8N those are workflow tools, right?

They help you bring together parts of a process and enable sort of automation and you know, computer intelligence behind the scenes to pull those things together. Where, where we sit, we definitely have a workflow creator and a tool. We can also help non code based talent.

So folks that are, that are not coders that want to be able to create these workflows, we. So we call the chain of processes a workflow. Each individual process we call a node.

And so the nodes also have to be available in order to put them into a workflow. So we have a lot of intelligence to help users build those things that the other platforms that I mentioned don't.

Those platforms still rely heavily on people that are automation specialists, that are coders, developers, engineers that can get in there and do those types of processes for folks. We want to hand the keys to the kingdom to people that aren't necessarily that deeply technical.

That being said, you know, there are other places where we compete with some of the sort of more mainstream, you know, tools and functions. But ultimately, you know, at the infrastructure level, some of the comparisons would be airflow.

And this gets very deeply, deeply into the nerdy part of the stack. Airflow is one that is traditional. It is an open source tool.

So a lot of folks have that put in place kubernetes, which is not the same as airflow, but it's part of that stack to orchestrate, manage infrastructure, etc.

We have a better solution to both of those because we are faster, more efficient and there's intelligence and quality assurance built in that those don't have inherently.

So you described the picture you paint about a lot of these companies is that they have lots of different tools have kind of grown up and maybe been purchased by different people within the organization. And then they realize that they have to make these, all these things all work together.

And you even use the term spaghetti code, you know, which is not a, which is not a good thing. That's what people do have. It's just tangled mess. How do you make, make that work better? Because fundamentally they've just got a mess.

I know, I mean you're 100% correct. And that is part of where I think a lot of companies are sort of, you know, hitting, hitting that wall. Right, I've mentioned the wall.

But the reality is, especially when you're trying to add in new technology like AI, there's a ton of barriers to entry. It's not just the Compute, it's how does it actually integrate within your existing system? What systems does it talk to?

What data does it have access to? And what we found is a lot of companies have silos, right?

They have, you know, all of these pieces are completely separate, whether it's, you know, shipping meets, you know, the ERP meets the CRM, right? There's all these like facets of a business that don't necessarily talk to each other.

And so that's where we go back to, you know, the ability to be that hub. We want people to plug in their existing tooling, their existing code snippets, their proprietary stuff into the system that lives on their systems.

So we don't actually maintain control of it if it is an on prem deployment, which means that they get to keep it in their ecosystem, they manage it, they maintain it, they can add all of their pieces and parts in proprietary or not. We as Montech Labs don't have access to their instance of mod when it's deployed on their systems. Right.

So for us, that helps them really maintain that security piece. But we have a ton of ability for them to plug in existing solutions and functions and then add in new things in a more seamless manner.

And because we have that like quality assurance layer, we're also testing and ensuring that things are going to work together more efficiently. We are an API first orientation.

So the way that the workflows actually function, when you create workflows within our system, it creates an entire API endpoint. And so you can point back to it and you can create a much more modernized system than would be traditionally.

But you can do that by pulling in legacy functions.

And I think that, you know, we've looked at it in a way that I think is a bit counterintuitive for a lot of the folks that have been out there building these tools and functions for a long time.

And we've done that because we have this internal knowledge of how these workflows and these systems have, have been functioning inside of legacy industries like the film industry, like the game industry. People think that they're very, that they're trailblazing and that they are out there doing the latest and greatest. And the reality is they're not.

They're still trying to figure out, you know, how they implement these things in a system that was not set up for this originally.

When you talk about these companies having massive compute power right under their noses, that they don't realize, is that really because of the stove piping you're talking about? And over in this stovepipe, they're not fully utilizing all of their compute power. And similarly over here in this stovepipe and this stovepipe.

Or am I coming to the wrong conclusion?

No, that could definitely be a piece of it. It depends on how the company set themselves up for infrastructure.

So one of the layers that we have in our system does allow you to create a compute or network pool. And so this could be everything from, you know, your server racks to your office computers. For us, compute is compute.

It doesn't really matter where it's coming from. It. If it's all on the same network, it can be instanced for processing.

So when you shut down at 5pm because everyone's going home from the day, you can now use those compute nodes as processing nodes because otherwise they would just be sitting there doing nothing. Why not?

So one of the things that we have seen is that companies are interested in figuring out how to allocate compute more efficiently across different departments or potentially they're offering it as a service to other companies. So we do, we have those as well. We call those tenancies.

So, so whoever the platform admin is, whether they're doing that for their own internal departments or whether they're doing that for other companies that they help provide compute for. So in the film industry, for instance, each film is its own company, right?

So Sony or Paramount would have, you know, the top sort of platform admin permissions and then they could allocate based on budgets to each individual film, how much AI credits they could have, how many, you know, compute nodes they can use over the, over the aggregate of their network. So there's a wide variety of ways to sort of slice the compute power much to much to, you know, the way that you suggested.

And then there's also, you know, the other side where it is just simply they don't really even know what they have in, in total.

So trying to put all those things together and effectively understand what compute you really truly have available across these different, types of, you know, compute styles and processing needs is a challenge because there's. They're typically not necessarily all networked or hooked together in one plane.

So you've got some resources that aren't being used over here because there's not transparency or they don't have it logged properly or it's not connected in and the like. So there's multiple ways that they are losing money and efficiency across their compute capabilities.

Companies of all sizes, small and large, do have a tendency to overstudy and over plan startups shouldn't for sure, because time is money and they don't have a lot of either.

There is a sense that, okay, in order to do this new thing, which is going to require this big automation, you know, integration to be set up, it sounds like a really big deal. And so they tend to have lots of meetings and lots of analysis. You rebel against that. You don't think that's right.

How do you sell that to either size company that might bring you in and convince them, well, you don't need to do all this analysis and planning and here's why.

I mean, I think part of it is the show and tell is what I'll say, right? We, we know that it is hard for people to, especially when you get this deeply technical, right?

It's hard for everyone to wrap their brains around it.

And the real folks that we're appealing to, right, with the, the money savings and the efficiency, right, that's going to be more on like a CFO COO level rather than sort of the people that are down at the bottom like running the systems, DevOps and engineers and the like. And so for us, a piece of it is ensuring that we have the right sort of conversation, first of all, with the right stakeholder.

And the value proposition has to be really clear. And I think one really important piece is the observability layer.

I mean, I've mentioned it in this conversation, but what, what that really, that could be a dashboard, right? It could be as simple as a dashboard. We all love a good dashboard where we can like at a glance see the technical details that we need.

But at the 10,000 foot level, we don't need to know all the granular details of every piece of compute if we're at, you know, a CXO level. And so creating the right visuals and the right data for the stakeholder is super important.

And then being able to demonstrate the technical feasibility to the end user, the person who's implementing it, the person who's, whose job it will affect. But I personally believe it will affect all of them in a positive manner.

Meaning if there's less drudgery, less boring things, and ultimately the ability to control a lot more because you have more information at your fingertips and you don't have to go search for it.

We're not managing, you know, we're not taking away the job roles that we are supplementing what they are doing and helping them be more effective with their time.

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Tell us a little bit about your startup. How big is it now?

Sure. So right now we have raised just under 2 million in funding over the last six years. Our team is between two and five people.

We are, we kind of ebb and flow depending on what kind of workloads we have. And we have really right now we're focused on this expansion toward defense. So as a what, what I would consider dual use.

We haven't had application, full application yet in the defense sector, but we've been looking at it since 222 22. So film and television has had, you know, a challenging few years is what I'll say. There's been a lot of condensing in the industry, right.

And part of that is due to the fact that there are now a lot of stakeholders from the tech world that own media companies. So Paramount for instance, and Skydance, Skydance purchased Paramount and that is under the Oracle family. Right. So you know, we see.

And again Warner Brothers is now, I think the Warner Brothers just as of today acquisition has been approved by Paramount. Netflix has always been one of the most technical companies. Disney is kind of, I would say closer to the top.

But really there's like my joke was that there are four and a half media companies, four and a half large media companies. And so you know, we have great relationships and have done work with Paramount, Sony, NBC Universal.

And the reality is there are only like a handful of very large media companies. And so for us it was a big important piece to add another industry and another market.

And where we found a lot of interesting opportunity and traction is in the defense and defense industrial based spaces because they are very similarly looking for on prem deployment because of security and data management. And they are looking for the ability to, to save money and be more efficient.

One of the things that I always like to talk about with Startup founders is the word grit. Because every startup founder, well, I would say, has to have it in order to survive, in order to just do it.

What you've just described in this conversation, going from helping parents get ready for a baby's design and architecture types of things, then the, the whole 3D virtual reality, and, and. And now, you know, this operating system for AI. And of course, I've simplified what you're.

You've told us dramatically, but it just, it just calls out that you've. You're another one of these people that has a lot of grit.

My definition of it uses words like resilience and fortitude and a lot of drive and scrappiness and courage. Courage, probably the most important one. And so when you think about it in those terms, where do you think your grit comes from?

I. I will tell you that it took me a while to find it. I did. I've always actually enjoyed running my own business. So as a nanny, I had my own business.

I never worked through an agency or anything like that. So I was dealing with my own contract, sourcing my own clients, managing my own schedule, all of that stuff. And I enjoyed it.

I enjoyed the, like, solopreneur route. I never, I didn't know that's what it was called, of course, at the time. A little less, you know, flashy at that point, but I liked it a lot.

I did do some corporate jobs and did not love them or thrive in them. They're just a little redundant. I would much rather pair together, you know, 14 jobs across seven days and sit at a single desk for a week.

That is very much always been my MO. Maybe that's just my ADD. I'm not 100% sure, but I'll take it. I'll take it as a superpower.

And ultimately, I think a piece of entrepreneurship is being bullish about the thing that you are building, even if you're still figuring out how to get it there, even if you're still figuring out the messaging, even if you are, you know, not 100% sure if it'll work. Right.

The reality is you need to know that there is an opportunity that you are committed to, to finding and ultimately, you know, taking across the finish line. And I. We've had a lot of interesting challenges across the many years.

You know, this cumulatively between the two companies that I mentioned that are. We're in the tech space, it's been a decade, and it. There's no such thing as an overnight success. Right. It takes roughly 10 years to get there.

So we're six into this company, so maybe in the next four years, we'll be an overnight success. I don't know. But the. I think one of the other pieces is. Everyone always asks, you know, do you want to be.

Do you want to be rich or do you want to be famous? And I'm like, definitely rich. I don't. I don't want to be famous. I don't necessarily want to be a household name.

I don't even necessarily think that our company, per se, should be a household name within its little niches. For us, it's always about how can we make the most impact. That's what I want to do, is make an impact. And you can only do that if you stick to it.

You push hard, you barrel through the hard times. You have, you know, a community and, you know, folks around you that support you in that. I think that's super important.

I would not be where I am today if I did not have that. And ultimately, you know, you start building that ability to. To hear.

No, to hear that it's not the right fit or it's not the right time or it's not the right person. I think that those pieces are super important on the journey.

Do you think that the way in which you're raising your kids is going to develop grit in them as well?

Absolutely, yes. They are already scrappy little humans. They both have had multiple businesses already. I definitely didn't have a business before I was 10. Not really.

I mean, maybe we did like a, you know, lemonade stand or something like that, but they've had multiple businesses. They're both always sort of looking for, you know, how to use their. Their skills and their capabilities in unique ways that.

That provide value and create opportunity for themselves and try to foster that as much as I can. And it's. It's. It's wild, to be honest. Everyone. Everyone's like, you know, oh, you were in childcare. You know exactly what you do.

I'm like, definitely not. Definitely not.

That doesn't mean anything once you have your own children and you get to see what interesting little humans you made because they're so very different. And that, I think, is one of the most amazing parts of.

Of entrepreneurship as well, is I have a lot of flexibility to support my family in unique ways that I think other people don't. So I'm grateful for that as well.

Well, Alex, thank you very much for this conversation. I think this is one of those which will be highly motivational to people that are listening. And I I I hope this was somewhat fun for you as well.

Absolutely. I appreciate you inviting me on.

I enjoy sharing about my non linear journey because I do think that, you know, it helps to know that you don't have to be deeply technical, you don't have to have a, you know, CS degree to be in this world and operating if tech is where you want to go. But there are a lot of ways to use those skills and meaningful ways that that can contribute and create an impact.

Well, hear hear. Thank you for that. And again, I really appreciate your time today.

Absolutely. Thanks.

And now for your toolkit takeaways. Toolkit Item one your pivot might not be a pivot, it might just be the headline. Finally catching up to the byline.

Alex's compute layer was always inside her product. The market just needed time to care about it. Before you call something a pivot, ask yourself, was this always there, quietly doing the work?

Toolkit Item 2 Match your message to your stakeholder. The same product needs two completely different conversations.

One for the CFO who needs to see a 60% efficiency gain on a dashboard, and one for the DevOps engineer who needs to see it work. Know which room you're in before you open your mouth. Toolkit Item 3 Show and Tell beats study and plan every time.

Alex's answer to Analysis Paralysis is a demo. Get the right stakeholder in front of the right visual and let the product make the argument. Meetings don't close deals. Proof does.

Now go take an honest look at what your company already has. Infrastructure, relationships, capabilities and ask yourself what you're leaving on the table before you spend a dollar on something new.

And that's our show with Alex. The show notes contain useful resources and links. Please follow and rate [email protected] designing successful startups.

Also, please share and like us on your social media channels. This is Jothy Rosenberg saying TTFN Tata for now.