2026 is when multiplayer mode comes into gear.
If you have a bunch of agents autonomously working, isn't there potential for a huge multi-agent cascade of failures?
There's a lot of narrative around AI helping automate work and reducing cost.
But I think in instances where AI is actually reinforcing the business model in driving revenue, there's really no limit to the amount that customers may want to adopt that technology.
It's not AI that's the competition.
It's your competitors using AI.
Every year we step back and ask a simple question.
What will builders focus on next?
Our 2026 big ideas.
Bring together the themes our investing teams believe will shape the coming year in tech.
This episode is built around one big idea.
AI is becoming an orchestration layer inside the enterprise, not a collection of standalone tools, a coordinated system of agents that can plan, analyze and execute work across departments and software.
You'll hear four perspectives on what changes when AI starts running the workflow, how organizations extract context, why legacy replacement accelerates, what multiplayer AI looks like in practice and what makes these systems commercially defensible.
To understand the shift, we start with the enterprise-wide view.
Seema Ambal argues that the move from experimentation to coordinated multi-agent systems will force organizations to extract tested knowledge from documents, processes and people's heads, turning it into usable operational context.
Here's Seema.
Hi, I'm Seema Amal, a partner on our apps investing team.
My 2026 big idea is that AI will create a new orchestration layer and new roles, particularly in the Fortune 500.
In 2026, enterprises will shift further from isolated AI tools to multi-agent systems that'll need to behave like coordinated digital teams.
As agents start to manage complex interdependent workflows — like planning, analyzing and executing together — organizations will need to rethink how work is structured and how context flows across these systems.
The Fortune 500 will feel this shift most acutely.
They sit on the deepest reservoirs of siloed data, institutional knowledge and operational complexity, much of which sits in people's brains.
To get this context out of people's brains.
It's some combination of collecting documentation and watching human actions.
What's the documentation?
It could be onboarding videos, written instructions, full documentation that's been written up.
And then the watching human actions is literally watching how humans are clicking around in their browsers, the actions they take, the phone calls they make, et cetera, and then piecing this together as shared context.
What needs to be solved across these agents?
It's providing the feedback across the agents and being able to ultimately determine, in this case, who is a good customer and are we getting the ROI, how we're spending our dollars or our time.
To put that even more concretely customer support needs to be able to say this is a bad customer sales.
You should spend less time prioritizing customer A and go for a customer profile B.
But right now, if we looked at it, the sales agent is operating autonomously, the support agent is operating autonomously and they're probably, if anything, being measured more on efficiency metrics versus holistically looking at what's best for the business.
One of the natural questions that comes out of this is well, if you have a bunch of agents autonomously working, isn't there potential for a huge multi-agent cascade of failures?
Yes, it's possible.
But remember, we're not changing to this overnight.
There could be multi-human cascading failures in any organization.
I think agents have to be treated similarly.
If you think about it this way, there are two checks.
One is there still will be humans in the loop at various points.
That will be one check.
And what will the human do and eventually the agent?
There will be a set of audit procedures and evals.
You know, again, go back to these quantifiable metrics and say, OK.
Our sales agent is doing really well.
We're closing a lot of customers.
Our negotiation agent is bringing in great pricing, but all our customers are turning.
If we measure all those against each other and say we see that one is too high relative to the others and we have these quantifiable metrics, we can go back and change the objective function for any of the agents.
I think every agent will have its own eval function and it will have KPIs, just like humans are measured against right now.
There will have to be logic, that's saying, if A, then B Ultimately right.
Just as organizations work towards some set of overall organizational KPIs, that's how agents will work too.
There's a huge opportunity specifically working with Fortune 500 in the context of this problem.
To date, we've seen Fortune 500 companies be very interested in AI, but it's been, I'd say, more on the experimentation side than deeply implementing AI.
But I think that's about to change.
It's most interesting for the Fortune 500 because they have all of this siloed context across people and processes, as these organizations have gotten built.
A lot of Fortune 500 companies have grown through acquisition.
They have different geographies.
Each of these geographies have different software systems.
They have different people.
They operate differently.
And what does that mean today?
These companies all operate very slowly and bureaucratically.
Implementing new software takes years.
Anything to change takes forever.
Now, if you're able to create this context layer, we're able to take things out of people's heads and create a context layer.
You know, imagine like putting in a new ERP or a new procurement agent becomes much faster.
And then you can actually have these agents work with each other in a way that's much faster than the Asia team and the Europe team needing to set a bunch of meetings and two people needing to continuously talk to each other about closing a contract that spans multiple geographies.
What I'm most excited about is this ability to pull things out of people's heads and then suddenly unlock the real power of agents.
And I think the Fortune 500 has the most siloed and distributed data.
And I think there can be a lot of opportunity for smoother operations.
SEMA gives us the operating model, orchestration, context extraction, and digital teams.
Now let's look at the clearest industry where this becomes unavoidable.
Angela Strange focuses on financial services and insurance, where unified data and parallelized workflows make it possible to replace legacy cores and unlock speed, margin and scale.
Here's Angela.
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 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 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 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 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?
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 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 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 re-architected 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 across your company as quickly as possible is going to make a much bigger difference against your competitor.
That maybe takes two or three 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 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 massive.
So if you're a founder who deeply understands or is deeply curious about any archaic aspects of banking or insurance, the opportunity is now.
You can build your software faster and customers are ready to buy.
Angela makes the case for why this happens now.
Modern platforms unify data and agents can run work in parallel, changing both the customer experience and the economics.
Next is the product implication.
What does this orchestration layer look like inside the software itself?
Alex Immerman describes vertical AI moving into multiplayer mode, where multiple humans and multiple agents collaborate inside a workflow with explicit trust rules and a command center interface that separates what agents can execute from what humans need to review.
Here's Alex.
My big idea for 2026 is vertical.
AI is going to evolve from information retrieval and reasoning to multiplayer mode.
Vertical software is having a moment.
But vertical software was cool before ChatGPT.
Shopify, Viva, Procore, Toast have all scaled to tens or even hundreds of billions of market cap.
Huge companies.
But vertical AI companies, they're growing faster, faster than historical precedents that we saw in SaaS.
One of the cool aspects that we're all talking about with AI is how agents are replacing labor.
It's easier to replace a lawyer than it is to replace a generalist.
Building for a vertical building for a specific type of employee means deep integrations, proprietary data, specialized interfaces that a horizontal as much as I love ChatGBT is not going to be as good at.
We've observed vertical AI evolve across three phases.
First was information retrieval.
You read some documents, you extract information, and you might summarize it.
The second came this year in 2025, reasoning.
Reasoning capabilities have been really impactful for vertical software businesses.
With Hebbia, you're analyzing financial statements and building models.
With Basis, you're able to reconcile trial balances.
And with Elise AI, you're able to diagnose what the maintenance issue is and contact the right vendor.
The problem is that with all complex work, there's collaboration.
Multiplayer mode is required.
2026 is when multiplayer mode comes into gear.
If you want to accomplish not just a discrete task but the full job, you need to be able to collaborate with others.
So multi-human and multi-agent collaboration is on its way.
And with that...
The value of these platforms increases and the switching costs rise, which is really exciting as we think about defensibility of these platforms.
Vertical apps have been criticized that they're not very defensible in this AI era.
Will they stand the test of time?
The best ones absolutely will.
A couple attributes that I look for with vertical apps.
One, brand.
There's a high referenceability in vertical markets.
The customers all go to the same conferences.
They go to dinner together.
And so Elise AI has emerged as the brand in property management.
All the customers, all the large property managers know them when they think of AI.
A second mode is proprietary technology or IP.
Anderle in defense.
Flock safety in public safety.
Waymo or applied intuition in autonomy.
Really difficult to build technology, difficult to replicate.
And then coming back, network effects.
With multiplayer mode, as more agents and more humans find increasing value on the platform, switching costs rise and no one's leaving the platform.
We expect that to merge and be an important part of the 2026 story.
One of the biggest obstacles to getting to multiplayer mode is building trust.
There needs to be AI operating agreements, understandings of when an agent can act on behalf or when they need to flag an issue to their human
Initially they might be able to schedule a meeting for you, but in the future, as they build more and more trust, they can be on the front lines negotiating.
So let's paint that picture.
You're in an M&A transaction.
Your agent has built up trust.
They have the responsibility to go negotiate.
You've set parameters.
So if you're the sell side, you're selling a business.
You set the minimum price that you're willing to come to terms on.
And then the buy side agent, well, they'll set the max they're willing to pay.
And if those two cross, great.
You can get to a high level agreement.
But there's going to be outstanding questions like what's the working capital arrangement at close or how to deal with contingencies or earnouts.
The agent may not have the information to negotiate on their behalf, so that gets flagged up.
And so software won't be just another chat interface, but you can think of it as a command center.
There is a list of activities that are being negotiated on that.
Agents have full ability to go and act.
And then there's a separate section, the flags, where humans need to engage and take action.
I'm really excited to see these new user interfaces.
But what I'm more excited about is where work becomes less about doing and more about reviewing.
Alex shows what AI runs the workflow becomes in practice.
Collaboration, operating agreements, and interfaces designed around review and escalation.
To close, we need the commercial filter.
Which AI systems will actually win and persist?
David Haber argues, the strongest companies are the ones where AI reinforces the business model, driving revenue and outcomes not just cost reduction and building defensibility through workflow ownership and proprietary outcomes data.
Here's David.
Hey, I'm David Haber, general partner here at A16C, and I help co-lead the AI Apps Fund.
My big idea for 2026 is looking for companies where AI reinforces the business model.
You know, I think there's a lot of narrative around AI helping automate work and reducing cost.
But I think in instances where AI is actually reinforcing the business model in driving revenue, there's really no limit to the amount that customers may want to adopt that technology.
And so the market pull, and examples like that are just you know, so much stronger than those where it's just a cost reduction story.
I sit on the board of a company called Eve which operates in the plaintiff law space.
And what's unique about plaintiff law is that those attorneys don't charge by the hour.
They operate on a contingency basis which means that they only get paid if they win.
And so again, while AI is helping automate a lot of the drafting and reasoning work that they do, ultimately it's really about enabling them to take on more clients and make more money.
So it doesn't erode the billable hour, it really reinforces their business model.
And as a result, the market pull for Eve's kind of AI workspace has just been tremendous.
Another example in our portfolio is a company called Salient, which operates in the loan servicing space.
So they're applying voice agents to.
They started in auto lending but they've expanded to a whole ecosystem of kind of consumer lending products where a voice agent can speak in 50 languages fully compliantly track UDAP, do welcome calls and pay reminders.
And obviously, there is a cost reduction story in that, right?
It is helping drive efficiencies in many of these bank and non-bank lenders who have large call centers.
But I think what they found which is so remarkable, is that the voice agents are actually driving better collection rates.
So it's not just a cost reduction story.
It's actually delivering better outcomes for their end customers.
And as a result, it's reinforcing the lender business model.
Ultimately, where did the sources of compounding competitive advantage reside in AI applications?
And I think Eve is a really unique example and case study for this.
Ultimately, the founders of Eve had a vision for owning the end-to-end workflow, from intake to outcome.
And I think deeply, embedding yourself within your customer.
Having them live within the product every day is a source of defensibility.
I think they're also creating a really unique data asset.
Ultimately, by being able to process cases again from intake all the way to outcomes.
That outcomes data is not public.
That is not a source of information that model companies and labs can actually train on in the public internet.
And so...
Ultimately, that outcomes data is used to better inform, smarter intake, so that Eve can tell their customers.
Look, this case has these characteristics to potentially be worth 50000.
This case is potentially worth $5 million.
Here's how you may want to triage your labor and your time.
And ultimately, given this counterparty, what are the characteristics that you may want to put into a demand letter to actually affect better outcomes?
And so I think the more cases that Eaves processes, the smarter and more powerful the platform becomes again, ultimately reinforcing the business model for their clients.
Here's the connective tissue across all four ideas.
SEMA, the shift from isolated tools to coordinated agent teams, and why context becomes the gating factor.
Angela.
Alex.
David.
That's the enterprise orchestration layer, not a chatbot and not a feature, but a new way work flows through the company.
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