How AI Will Reshape The Economy In 2026 (a16z Big Ideas)

Welcome to part two of our 2026 Big Ideas. Ryan Macintosh explores the rise

Ideas. Ryan Macintosh explores the rise of what he calls the electro-industrial stack, a new foundation for how we build and power America's industrial future.

Angela Strange identifies a critical turning point in financial services and insurance where decades old systems are finally ready for reinvention. And Sarah

Wang reveals how a dynamic agent layer is emerging to overtake traditional systems of record fundamentally changing how enterprise software operates. These

aren't just forecasts. their firsthand

perspectives from investors driving change across American dynamism, financial services, and enterprise technology.

My name is Ryan McIntosh. I'm an

investing partner on the American Dynamism team. My big idea for 2026 is

Dynamism team. My big idea for 2026 is that the electro-industrial stack will move the world. The next industrial evolution won't just happen in factories, but inside the machines that power them. This is the rise of the

power them. This is the rise of the electroindustrial stack. Combined tech

electroindustrial stack. Combined tech that powers electric vehicles, drones, data centers, and all of modern manufacturing. I I think there are

manufacturing. I I think there are common uh tropes people report on.

People talk about China's so far ahead uh we can't catch up. And actually, you know, you go back a couple years ago and people were saying, you know, China's very far behind and America's incredibly fast. So, we've seen sort of like a

fast. So, we've seen sort of like a whiplash and now it's the opposite. I

think the reality is that uh you know, the technology that China has, America can do. Uh we're very good at uh

can do. Uh we're very good at uh engineering. We're very good at doing

engineering. We're very good at doing specific things. And in fact even like

specific things. And in fact even like the you know recent stuff around rare earth for example rare earth separation and processing we know how to do this we can do this we can do it incredibly fast. The real challenge is building the

fast. The real challenge is building the ecosystem to do this industrially at scale and doing it at a low cost.

Another example you know people typically talk about is is companies like SpaceX or Android these large businesses that need to move incredibly fast and thus vertically integrate. In

many ways they're vertically integrating by necessity not strategy. There just

isn't an ecosystem of companies that can scale with them. That is not the case in China. There are tier one, two, three

China. There are tier one, two, three suppliers, components, raw materials that exist in those ecosystems as well as the, you know, the institutions and uh uh political bodies that allow them

to move incredibly fast. Those are the things that might take years or decades for us to catch up to China. We can do the technology, but everything else needs to grow with it, or else we're just moving the bottleneck. So if you

want to build uh the electro-industrial stack or the core components that feed into these technologies in the United States uh you need to blend Silicon Valley software talent and culture uh

with industrial veterans. Even companies

like SpaceX, they were pulling propulsion talent from people who worked on you know shuttle program and various old school contractors when Shawwell came from aerospace corporation. Like

there there is a there is a world where you need this actual expertise. You need

to know what's been tried before. There

are smart people ex out there in these other companies, but you need to be able to move a lot faster. There's a lot of advantages of software today. So, you

need to be able to get the software talent that may not exist in these companies previously. You also want to

companies previously. You also want to colllocate engineering and manufacturing concepts like design for manufacturing uh are something that you know when you're tightly integrated on the same footprint or in the same ecosystem uh

you can move a lot faster. And I think also you need to build prestige around the mission. Um, for a lot of sort of

the mission. Um, for a lot of sort of traditional Silicon Valley talent, the smartest people can work on a number of problems and there are a lot of problems that are worthy of working on. Some of

them pay more than others. So, you need to attach sort of a a prestige or a purpose to what you're working on and uh use that to to attract the top talent.

The way that software will affect the physical world is through these sort of embodied electrified components. And

it's not just, you know, not just a humanoid robot or an electric vehicle, but it's the batteries, it's the power electronics, it's the compute, it it's it's the motors. All these things we're going to need to either reshore or

vertically integrate within the companies who are building the end product. These are, you know, very

product. These are, you know, very technical. These require a lot of

technical. These require a lot of expertise. These are very difficult

expertise. These are very difficult problems to solve. But the companies who solve it and the countries who have the talent base to in order to support it are the ones who are going to win in the 21st century. And as software and

21st century. And as software and artificial intelligence get stronger and they start having you know more of a presence in in automation and industrial military owning these supply chains is

going to become even more important. And

I think as we you know look forward 50 100 years owning the supply chains today are going to have a lot of effects of who controls both the sort of economic and military uh powers uh in in the

future.

I'm Angela Strange, a general partner on the AI applications fund. And my big idea for 2026 is there will be a dramatic turning point coming to

financial services and insurance where finally the risk of not replacing legacy systems will exceed the risk of change.

It's already happening. Major

institutions will let long-standing contracts lapse and implement their newer AI native competitors. Why? The

next generation of infrastructure doesn't just add AI. They unify the data from legacy cores, from external systems, from unstructured data into a new system of record, enabling FIS not

only to scale, but to take full advantage of AI. When this happens, there are three major changes that are important for both customers and builders. One, workflows will finally

builders. One, workflows will finally become parallelized. No more bouncing

become parallelized. No more bouncing between screens, cut pasting data. For

instance, your mortgage team could see the 400 plus tasks that are needed to underwrite your loan, do them in parallel, and even have agents do some of the more mundane ones for you to

check later. Second, the categories as

check later. Second, the categories as we know them are going to expand. For

instance, customer data from onboarding, KYC, KYB, transaction monitoring, even how those customers behave with your customer service team could all sit into a single risk platform. brings together

fraud, risk, compliance much more effectively. And then third, most

effectively. And then third, most excitingly for the builders, the new winners here will be 10x bigger. Not

only because those software categories are bigger, but because software is able to consume a lot of the labor that humans didn't want to do anyways or that banks or insurance companies couldn't

hire for fast enough. So, as the saying goes, it's not AI that's the competition, it's your competitors using AI. So the best banks, the best

AI. So the best banks, the best insurance companies will fix their plumbing and enable them to take full advantage and be the most competitive going into the next decade. Companies

have been talking about this for decades. Why is it different now?

decades. Why is it different now?

Primarily three reasons. One, we have to remember that many of these companies still live on mainframes, decades old mainframes, and their systems were

already on the verge of breaking with the scale. Two, now companies see that

the scale. Two, now companies see that they're leaving a lot of revenue on the table by not being able to take advantage of AI. For instance, in insurance, underwriters sometimes can't

even get to the demand that they have because they're not able to process it fast enough. They can't bring in the

fast enough. They can't bring in the documents. They can't scan them. This is

documents. They can't scan them. This is

a huge revenue upside that can be captured if you get the right system and you layer AI on top. Third, there are strong viable options of this next

generation of AI first software built by entrepreneurs who deeply understand your industry, are deeply technical, and have entirely rearchitected your platforms to

one enable you to scale and two be incredibly flexible in terms of how you can add AI on now and in the future. I

see a ton of opportunity here and potentially a dramatic reordering of the winners and losers of incumbent companies based on who become the early

adopters of some of these new platforms. And we're already seeing it. There's

some banks and there's some insurance companies that are starting to get the reputation of being forward thinking, easier to work with, wanting to lean in.

And those companies in some areas like mortgage servicing have been able to turn areas of their business from 5% margin businesses to 50% margin businesses. And you imagine doing that

businesses. And you imagine doing that across your company as quickly as possible. It's going to make a much

possible. It's going to make a much bigger difference against your competitor that maybe takes 2 or 3 years to catch up. One of the reasons as an investor that I get so excited about

infrastructure is that it's beautiful infrastructure that enables beautiful consumer experiences and beautiful business experiences.

For instance, why does your bank market products to you that you already have?

It's because your customer data sits in all of these different sectors. Why

can't customer service agent A answer questions about customer service B if you call in about your banking operations? Now, imagine the future of a

operations? Now, imagine the future of a unified data layer and incredibly smart people supplemented by agents that can understand your needs, help you with any

product you already have, anticipate your needs in the future. That would be a beautiful experience for both customers and businesses. In 2026, we're going to see a dramatic acceleration for

any company that has built a new AI first platform that sells into this large industry. But the opportunity is

large industry. But the opportunity is massive. So if you are a founder who

massive. So if you are a founder who deeply understands or is deeply curious about any archaic aspect of banking or insurance, the opportunity is now. You

can build your software faster and customers are ready to buy.

I'm Sarah Wang, general partner on A16Z Growth, and my big idea for 2026 is that systems of record start to lose their edge. A passive system of record layer

edge. A passive system of record layer stops making sense when agents can independently execute on unsigned intent. I expect to see a new dynamic

intent. I expect to see a new dynamic agent layer that actually makes sense for employees to replace legacy systems of record. This is a very exciting

of record. This is a very exciting development on the long road of inserting intelligence into companies. I

don't say that systems of record are losing privacy lightly at all. I used to work at a firm that almost exclusively invested in ERPs and other systems of

record because of the stickiness of the data gravity. There was a wave of SAS

data gravity. There was a wave of SAS 2.0 that was wellunded and tried and failed to take on the system of record mostly through a better UI. This is the first time that we've seen a genuine

threat to that and that's because the distance between intent and execution is collapsing and that's creating not a 20 to 50% better experience for the user

but how you get to that magical TEDex.

Let's take the concrete example of ITSM IT service management. This has

traditionally been the domain of powerhouse company Service Now. I

chatted with a head of IT recently who told me for the first time in his two decade long career he believed that IT support was fundamentally going to change. It will look completely

change. It will look completely different in 5 years. So why is that? If

you think about the way that the old systems work, how long it takes to do something like request access to new software in the firm and you contrast that with the ITSM agents that are

arriving. They plug into your stack and

arriving. They plug into your stack and this type of request becomes nearly instantaneous. Through advancements in

instantaneous. Through advancements in LLM, you can now extract intent. You can

classify the request type. You can map it to a known workflow, identify user entities, and the request from the user becomes fulfilled in a way that is efficient and accurate. So, we think

there's a couple of valuable layers in this new paradigm. Of course, there's the foundation model layer. We believe

that stays valuable. Um, but it's really the emerging agent layer that sits as close as possible to the user and is collecting data on that user, understanding user preferences that we

think acrru value in the future. Based

on everything that we're seeing in the wild, we believe this is a huge opportunity for new players to come in and win. Why is that? We're in a phase

and win. Why is that? We're in a phase right now where the product is getting better on a weekly, if not daily, basis, and you need teams that move fast. If

you're going to collapse intent and execution, what bridges that is actually having an accurate or reliable solution for your customer. Otherwise, they're

not going to use it. They're not going to trust the agent that you're building.

That's why we're starting to see even agents built on top of classic iconic platforms like Data Dog lose to some of the new AI SRE companies like a a Resolve or a Traversal. We're extremely

excited about this opportunity and 2026 is going to be the year that the dynamic agent layer overtakes the system of record.