Emergence has always operated this way: form a conviction before the market does, and hold it. We backed Salesforce when enterprise software wasn’t supposed to scale. We became Zoom’s first institutional investor when video-first work was still a fringe idea. Our insight preceded market validation by years.

In 2017, we put that same instinct to work on a question that felt early at the time: what if the most valuable layer in enterprise software wasn’t the software itself, but the human intelligence running through it?

Founder and General Partner Gordon Ritter called the thesis Coaching Networks. The idea was that the best systems wouldn’t just automate work; they’d learn from your best people and spread that judgment across the organization. Nearly a decade later, the technology is ready, the market has arrived, and the thesis is being cited at the highest levels of the industry. Gordon traced its evolution in Fortune earlier this month, and last week published “Above the Model,” the fullest expression of where it’s landed.

The durable advantage in enterprise AI isn’t the foundation model. It’s the proprietary learning loop a company builds on top of it. Generic intelligence commoditizes. The specific way your organization thinks and adapts does not. It’s a view Emergence has championed longer than anyone. We’re glad the conversation has arrived.

Read Gordon's full post →

Where’s AI really headed?

Everyone has theories. We went a step further, partnering with Ashby, Carta, Pave, Stackpack, and Standard Metrics to ground our thesis in data from thousands of real companies, spanning fundraising, financial performance, software spend, compensation, and hiring.

Read the 2026 Beyond Benchmarks Report

From the Bench: New Ideas & Insights

Biology Just Entered Its Read-Write Era
For sixty years, our understanding of biology was read-only. We could sequence the genome, trace a disease to a single misplaced letter, and eventually edit one typo at a time. What we couldn’t do was write new, functional biology from scratch. That changed with Evo, the AI model built by Eric Nguyen and his co-founders at Radical Numerics.

Emergence is leading Radical Numerics’ $50 million seed round. Gordon Ritter wrote about why and what it means for the next decade of medicine.

Capital Follows Conviction
Three Emergence portfolio companies closed significant rounds this month. Bland, the voice AI company that has processed over 175 million calls and handles more than 3.5 million per week, closed a $50 million Series C. Genspark, the agentic workplace platform, raised a $100 million Series B extension at a $2.6 billion valuation. And Prosper AI, which automates the end-to-end patient journey across scheduling, insurance verification, and billing, raised a $30 million Series A, following 5x revenue growth in six months. Congratulations to these incredible founders and teams.

Emergence in the Wild: Catch us in the news or at events

Inside the First AI-Native Services Summit
What happens when many of the founders defining AI-Native Services gather in one room?

Earlier this month, we brought together leaders from across the category for a packed, invitation-only summit to compare notes on pricing, distribution, scaling, and the emerging playbook for AI-native businesses. The conversations were candid, practical, and shaped by operators building at the frontier. Attendees, keep your reflections coming.

Many of the lessons from the summit will help inform future updates to the AI-Native Services Playbook. And stay tuned: we’ll be having a New York-based summit later this summer. Register your interest here.

Explore the Playbook →

Emergence GPs Named to Business Insider’s Investors to Know in Robotics and Physical AI
General Partners Kevin Spain and Jake Saper were included in Business Insider’s list of 22 investors to know in robotics and physical AI. We see enormous potential in this category, and we’re proud to have two members of the Emergence team recognized among the investors helping to shape it.

Jake Saper at VC Unlocked: AI Edition
Jake joined the inaugural cohort of VC Unlocked: AI Edition this month as a speaker, leading a session on AI-native services for 24 investors across 10 countries. His session, “The Next Shift: AI-Native Services,” was voted the program’s favorite by a wide margin, and the AINS framework was cited most frequently as the most useful takeaway from the entire program.

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The efficiency story everyone’s telling about AI is backwards.

Most founders assume that building with AI means doing more with less. Faster product cycles. Smaller teams. Lower burn. But six months ago, we looked at actual operating data from 50K+ companies and found something different. Non-AI companies generate 39% more revenue per employee than AI companies.

That’s the opposite of what most people expect. And it means your model for sequencing growth and efficiency is probably upside down.

Why we’re looking at this closely

Twenty years ago, we watched Salesforce grow from 6 people to $7B in revenue. We saw the same dynamic play out: founders who built for outcomes, not velocity, sustained both growth and capital efficiency longer. We’re seeing the exact same pattern now with AI-native companies. That’s why we went deep into the data in partnership with Ashby, Carta, Pave, Stackpack, and Standard Metrics.

Here’s what we found

The best-performing companies don’t trade growth for efficiency. They sequence them.

Companies that were top-quartile R&D spenders in 2024 accelerated revenue growth from 34% to 60% in 2025. Companies that were top-quartile S&M spenders accelerated from 41% to 62%. But (and this is critical) R&D pays off with a 12-month lag. S&M pays off now.

Most founders get this backwards. They invest in S&M first to hit quarterly targets, then run out of runway before R&D compounds. The winners do the opposite: they front-load R&D when they have runway, then scale S&M to capture the output.

Zoom took 8 years to $1B ARR. Together AI did it in under 3 years. Genspark is on track to do it in under 2. The difference is deliberate sequencing.

What you should do differently

Stop planning around 18-month raise cycles. The data shows Series A to Series B now takes 2.2 years. Your financial plan should match reality.

More importantly: treat your R&D and S&M sequencing as a deliberate choice. Decide upfront where you’re investing and when you expect return. Plan backwards from that.

The full report walks through five concrete moves that we’re seeing best-in-class founders execute, with the frameworks to model them yourself.

The bigger picture

We’re in the middle of a shift from software-license economics to outcomes economics. Stop optimizing for customer acquisition or feature velocity. Optimize for measurable outcomes customers care about. That’s a different hiring model. A different burn model. And a different partner you need in VCs.

If you’re building in that direction, and you should be, download the report. There’s a lot more here.

Read Beyond Benchmarks 2026 →

- Joe Floyd and Andrew Crinnion

There’s a seductive version of the AI-native services story making its way around venture and private equity right now. Pick a fragmented services category. Roll up a few legacy firms. Layer AI on top. Declare it “a transformation.” But General Partner Lotti Siniscalco argues this approach often creates the very constraints AI-native services are supposed to eliminate.

Acquired ARR can leak, legacy pricing structures show their flaws, and traditional workflows quietly reinforce traditional habits. But perhaps most importantly, the identity of a services business is shaped by the people delivering the work, and that identity is very hard to reassemble after the fact.

Lotti makes the case that the strongest AI-native services companies are not created through acquisition. They are built organically from the ground up, with AI-native practitioners, AI-native workflows, and cultures designed around a fundamentally different operating model from day one. Sequence matters more than speed. Build the system first, earn the trust, and then scale from a position of gravity.

Is Your Company Actually AI-Native?

Not every company using AI is building an AI-native services business. We created a short assessment to help founders benchmark their maturity and identify the gaps that matter most.

Take the AI-Native Services Assessment

From the Bench: New Ideas & Insights

OpenAI Bets on Deployment
In May, OpenAI launched DeployCo.The initiative pairs frontier models with the implementation expertise enterprises actually need to move AI from pilot into production: workflow redesign, systems integration, and accountable outcomes.

We’re proud to partner with them on the journey. Read more about our investment as a founding partner here.

Pace Scales AI Infrastructure for Insurance Operations
Emergence portfolio company Pace announced new funding to expand its AI-powered infrastructure platform for global insurance workflows, a category where operational complexity and manual processes still dominate. Rather than treating AI as a lightweight automation layer, Pace is focused on embedding intelligence directly into the systems that power underwriting and back-office operations at scale. The company’s approach reflects a broader shift happening across enterprise workflows: AI moving from assistive tooling into core operational infrastructure.

Harper Highlighted as a New Generation of AI-Native Founders Emerges
A recent Crunchbase News feature examining funding trends for Black founders highlights the companies continuing to break through in a highly competitive market, including insurance broker Harper. Harper’s momentum reflects a broader shift toward AI-native businesses that rethink industries operationally, embedding intelligence directly into how work gets done rather than layering software onto legacy systems.

What Makes AI-Native Services Defensible?
One of the biggest questions surrounding AI-native services is whether these companies can build real moats or if they’re simply modern services firms with better tooling. Lotti Siniscalco argues that the defensibility comes from ownership of the outcome. When companies deliver the work directly, they also capture operational data, feedback loops, and workflow intelligence. Over time, that compounds into an advantage that standalone software vendors can’t replicate.

Listen to the audio →

Should SaaS Companies Become Services Businesses?
Jake Saper explores a question that would have sounded almost heretical a few years ago: if AI can now deliver the work itself, should SaaS companies stop selling tools and start owning outcomes? The article argues that AI-native services may become the strongest defense against shrinking seat counts, commoditizing product features, and growing pressure to prove ROI. Instead of competing with foundation models directly, these companies sit on top of them, taking responsibility for the result rather than the software alone. It’s an idea that’s moving beyond theory. Gainsight’s recent pivot toward AI-native services suggests that even established SaaS companies are beginning to reconsider whether selling software alone is enough.

Read the post →

Emergence in the Wild: Catch us in the news or at events

Jake Saper Named to the Midas Brink List
Jake Saper was recognized on Forbes’s 2026 Midas Brink List, which highlights investors backing the next generation of breakout technology companies before they become household names. The recognition reflects Jake’s long history of supporting founders across AI-native services, infrastructure, and enterprise software.

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For years, software has been sold the same way. You sell a tool. The customer hires people to use it. Everyone hopes the combination produces the outcome they actually wanted.

That model is starting to break and the pressure is coming from multiple directions at once. The buyers who champion software renewals are being laid off. Foundation models are compressing product differentiation faster than roadmaps can keep up. And CFOs are done paying for tools that require additional headcount to produce results.

In a new article for our AI-native services living playbook, Jake Saper lays out a question that’s moving from hypothetical to urgent: if AI now makes it possible to deliver the full scope of work, should software companies become services businesses?

Not by layering on professional services. By becoming something different entirely: an AI-native services company that owns the outcome end to end, uses its own software to deliver it, and takes full responsibility for the result. The customer never has to sit in the seat.

This escape hatch to a new reality is possible. But it requires a genuine shift in what you sell, how you price it, and what kind of company you’re willing to become.

This month, we’re digging into that transition: when it makes sense, what actually changes operationally, and why waiting may be the biggest risk.

From the Bench: New Ideas & Insights

The Rise of Long-Horizon AI Workloads
Emergence Investor Rishub Nahar explores a shift that’s reshaping the AI stack: the move from chat-based interactions to long-running, background tasks that consume far more context and compute. As these “long-horizon” workloads become dominant, they’re exposing new bottlenecks and unlocking a fresh set of infrastructure opportunities, from memory architecture to inference economics to context management. The takeaway: the next wave of AI won’t just change applications. It will require a fundamentally different infrastructure layer to support it.

Read the post →

From Talking About AI to Building With It
Jake Saper makes a clear distinction between companies pulling ahead and those falling behind: velocity. The difference isn’t who’s thinking about AI. It’s who’s using it. Teams that are building agents, testing ideas in real time, and using their own products daily are moving at a fundamentally different pace. The shift happens when AI moves from something abstract to something you use. That’s where momentum and advantage starts.

Read the post →

Where Conviction Starts
Gordon Ritter reflects on returning to the Princeton boathouse and the lessons that have shaped how he thinks about teams, effort, and long-term decisions.

In a moment when so much feels new, the fundamentals haven’t changed. Early choices should optimize for learning, not prestige. “Fear mode” is often where real conviction begins. The context may evolve, but judgment is still built the same way: through effort, commitment, and a willingness to bet on yourself.

Read the post →

Emergence in the Wild: Catch us in the news or at events

Jake Saper on the AI-Native Services Circuit
Jake Saper has been making the rounds to pressure-test and expand the AI-native services thesis across a series of recent podcast appearances. On The Full Ratchet, he unpacked concepts like “mirage PMF,” what LLMs will and won’t take on, and why outcomes are replacing software as the core unit of value. On What Stays Human, the conversation focused on how AI is reshaping work, decision-making, and where humans remain essential. And on The Information’s TITV, he zoomed out to connect the dots on how this shift is redefining enterprise software more broadly.

Rewriting the Sales Playbook for the AI Era
In a new piece for Fast Company, Joe Floyd explores how AI is reshaping sales from end to end. The shift isn’t just about automating outreach. It’s about rethinking how teams identify intent, engage prospects, and move deals forward. High-performing teams are moving away from volume-driven tactics toward systems that prioritize signal, timing, and relevance. As AI changes how buyers behave, the sales playbook is being rebuilt in real time.

Four Emergence Companies Named to the AI 50
Forbes released its latest AI 50 list, spotlighting the companies pushing the boundaries of what AI can do, and we’re proud to see four Emergence portfolio companies included: Genspark, Mercor, Physical Intelligence, and Together AI. Each reflects a different edge of the AI landscape, from infrastructure to applications to embodied intelligence. All share a focus on turning rapid technical progress into real-world impact. Recognition like this is a signal of where the next generation of category-defining companies is taking shape.

Arcee Pushes the Frontier of U.S.-Built Open Models
VentureBeat spotlighted our portfolio company Arcee and its latest release, Trinity Large Thinking, one of the most powerful open models developed by a U.S.-based team. The article highlights a broader shift in the global AI landscape, as open model leadership becomes increasingly competitive and strategically important. Arcee’s approach focuses on performance, transparency, and accessibility, aiming to give developers and enterprises more control over how AI is deployed.

Yaz El Baba on The Information’s TITV
Yaz El Baba joined The Information’s TITV to discuss where AI investing is heading and what’s actually driving momentum in the market right now. The conversation touches on how capital is concentrating around companies that translate technical progress into real products, and why the bar for differentiation continues to rise as the space matures. A useful window into how investors are thinking as the AI market moves from exploration to execution.

Federato Brings AI to Smarter Risk Decisions
A recent feature from Digital Insurance highlights our portfolio company Federato for rethinking how insurers evaluate and price risk. Rather than relying on static models or manual judgment, Federato uses AI to help underwriters prioritize and manage portfolios dynamically, improving both growth and profitability. The platform brings real-time context into decisions that have traditionally been slow and fragmented.

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Most AI companies are selling the boat. A smaller group is selling the catch. We’ve committed to becoming early experts in how the fishermen are building these businesses, and we’ve just published the playbook for how it’s done.

While the world watches AI models, we believe the most interesting thing happening in enterprise AI is a new business model. A radical idea spreading company by company: stop selling software, start owning outcomes. The category has been real for a while. But it hasn’t had a name, a framework, and a set of principles for how to build it right. Until now.

Today, we’re launching the Emergence AI-Native Services hub, along with the most complete version of our playbook yet. It’s a distillation of everything we’ve learned so far. What works, what breaks, and where this concept is already proving itself in production. This is a living document that we’ll update every six months, because the category is moving faster than anyone expected.

And the proof keeps coming. Just a few weeks ago, we announced our investment in Hanover Park, an AI-native fund administrator. Fund administration has historically been manual, fragmented, and slow. A category that technology touched but never transformed. Hanover Park rebuilt it around AI from first principles. Timelines collapsed. Accuracy improved. Customer expectations shifted permanently. When the service gets that much better, the old baseline stops being acceptable.

AI is changing who owns workflows. And when ownership shifts, the line between software and services disappears. Dive into our AI-native services hub, read our thesis, and explore the playbook. We can’t wait to hear what you think.

From the Bench: New Ideas & Insights

How Do You Measure AI Adoption?
Jake Saper highlights a KPI that’s starting to show up in boardrooms: AI enablement spend per employee. It’s a signal of a broader shift. Companies are moving through stages, from basic adoption to deeper integration and, eventually, to measuring whether AI-driven work actually holds up. Most are still early. Very few are asking the harder question of quality. Getting teams to use AI is just the starting point. What matters next is whether that usage turns into durable output.

Read the post →

Go Get Your Hands Dirty
Lotti Siniscalco reflects on what it feels like to be in San Francisco right now as AI moves from hype to something much more real. Having watched past cycles come and go, she says this one is different. The gap between those actively using AI tools and those watching from a distance is widening quickly. Her advice isn’t theoretical. It’s practical. Spend time with the tools. Experiment. Compare notes. The shift becomes clear once you engage directly.

Read the post →

A Backdoor Into Anthropic
Joe Floyd breaks down a question many investors are asking: if Anthropic is still private, is there any way to get exposure?

His answer looks at the side doors. Public companies like Amazon, Alphabet, and Zoom all hold meaningful stakes, offering indirect ways to participate as AI leaders scale.

It’s a reminder that as value concentrates in a handful of AI companies, understanding where that exposure sits becomes just as important as the headline itself.

Read the post →

Bringing Structure to Hiring
Hiring drives enterprise value, yet the process itself has remained surprisingly unstructured. In a recent post, Gordon Ritter reflects on that gap and highlights the launch of Lavalier from Textio. Lavalier applies AI to synthesize interview data, helping teams evaluate candidates against shared signals rather than fragmented notes or instinct. The goal is to bring consistency, context, and evidence into one of the most important decisions companies make.

Read the post →

Emergence in the Wild: Catch us in the news or at events

From Tool to Teammate: Meet Genspark Claw
Portfolio company Genspark has launched Claw, its first “AI employee,” alongside AI Workspace 3.0, marking a shift from AI as a tool to AI as an active participant in getting work done. Claw is designed to operate across workflows, taking on tasks directly rather than just assisting, a model that aligns closely with how AI is moving into production environments. The launch comes as Genspark surpasses $200M in annual run rate and extends its Series B, a round led by Emergence Capital, marking our largest investment to date.

How AI Is Rewiring Marketing
In a recent piece in Fast Company, Joe Floyd notes the shift from AI as a campaign tool to AI as the engine behind how modern marketing actually operates. The article explores how teams are restructuring around AI, embedding it into workflows that touch everything from content creation to performance optimization. As expectations around speed and personalization rise, marketing is becoming less about individual campaigns and more about systems that continuously learn and adapt.

What Investors Don’t Want in AI SaaS Anymore
In a recent piece from TechCrunch, investors share a clear shift in how they evaluate AI companies. The bar is moving beyond surface-level AI features and into how deeply intelligence is embedded into the product. Tools that rely on thin wrappers or incremental improvements are falling out of favor, while companies that rethink workflows and deliver measurable outcomes are standing out.

Bolt and Ironclad Named Among the Most Innovative Companies
Fast Company’s latest Most Innovative Companies list highlights the organizations redefining how work gets done, and we’re excited to see Bolt and Ironclad included. Both companies reflect a broader shift toward systems that embed intelligence directly into workflows, improving speed, accuracy, and decision-making at scale. Recognition like this is a reminder of where innovation is happening: inside the tools and services that power real work. Congratulations to all!

Yaz El Baba Named to The Information’s Next GPs List
Yaz El Baba was recognized on The Information’s 2026 list of Next General Partners, highlighting rising investors shaping the next wave of venture. The list reflects a moment where competition for top AI deals has intensified, and the ability to identify and secure high-growth companies is becoming a defining skill for the next generation of firm leaders. Yaz’s inclusion underscores his work backing AI infrastructure and applied intelligence companies at a time when those bets are increasingly central to the venture landscape.

Together AI Recognized on Wing Venture Capital’s Enterprise Tech 30 2026
This year, Emergence portfolio company Together AI has once again been recognized by Wing VC and Eric Newcomer’s Enterprise Tech 30 list. The list – which includes other industry leaders like Anthropic, OpenAI, SpaceX and more – is selected by 90+ leading investors and corporate development leaders, and celebrates the players with the most potential to shape the future of enterprise technology. Together AI and Co-Founder Vipul Ved Prakash are building the essential infrastructure needed to make verticalized AI safer and more affordable. Congratulations to the team on another outstanding recognition!

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Today, AI is being judged on results. Intelligence alone isn’t enough. What matters now is how it’s embedded into real systems that deliver measurable outcomes.

That focus showed up clearly in several recent partnerships.

We co-led Rowspace’s Series A alongside Sequoia, backing a team tackling a foundational challenge in finance: transforming decades of fragmented firm data into infrastructure that AI can reliably reason over. Institutional knowledge becomes structured, auditable, and compounding.

We also led Adaption’s $50M seed round, supporting founders building adaptive intelligence that adjusts dynamically to workload and context. As enterprises move from pilots into scaled deployments, we need a new type of foundational AI to support the efficiency and reliability required to become true infrastructure.

In the physical world, we partnered with Bedrock Robotics in its $270M financing to bring autonomy to construction. By embedding intelligence directly into heavy equipment, Bedrock deploys autonomous excavators on active job sites, using AI to move more earth with fewer delays while reducing operator fatigue and keeping projects on schedule.

And in commercial insurance, we led Harper’s Series A, a leading example of AI-native services. Harper is not selling tools into a legacy brokerage ecosystem. It owns the service layer end-to-end, embedding AI into underwriting and matching workflows so that every interaction strengthens the system.

Different industries, yet a shared pattern.

Each of these companies integrates intelligence into the systems that drive real economic outcomes. As AI moves deeper into production environments, advantage accrues to those who understand the new paradigm and can own the workflow, structure the data, and align pricing with results.

This is the phase we are investing in.

From the Bench: New Ideas & Insights

Which Moats Survive AI?
One of Jake Saper’s latest posts struck a nerve, racking up outsized engagement across founders and investors. With public software down sharply over the past year, he argues the real reset isn’t just valuations. It’s defensibility. As agents take on more of the work, workflow and seat-based stickiness look fragile. The durable moats in an AI era tie to outcomes, full context, brand, and network effects that compound with use. If you’re building software today, it’s a useful stress test: what actually holds when the platform shifts?

Read the post →

SaaSpocalypse? Not For Everyone.
Joe Floyd’s latest post tackles the “AI is eating software” narrative head-on. After two years of watching companies adapt inside the Emergence portfolio, he sees a clear divide: some teams leaned in early and are riding the AI wave. Others hesitated and are struggling to catch up.

The difference hasn’t been luck. It’s been decisive CEOs, relentlessly curious CTOs, and strategy built around where models are headed, not where they are today. The market is rewarding conviction.

Read the post →

The Dream Job Series: Fear, Empathy and Trust
Lotti Siniscalco’s latest Dream Job spotlight features Shanthi Shanmugam, CEO of Casap, a payments infrastructure company that’s grown 10x in a year. Casap is scaling fast and hiring ambitious, low-ego leaders across engineering, GTM, and operations, including a Head of Engineering. If you’re looking for a role that stretches you while building something meaningful, this is worth a look.

Read the post →

Emergence in the Wild: Catch us in the news or at events

The Fintech Wrap Up Podcast: AI and the Next Fintech Cycle
Lotti Siniscalco joined the Fintech Wrap Up Podcast with Casap CEO Shanthi Shanmugam to unpack what’s really happening in fintech right now. They discussed how AI is reshaping product quality and investor standards, why some companies are raising while others stall, and what founders should focus on to build through the next cycle.

Arcee AI on the Open Model Race
In a recent feature in Forbes, reporter Anna Tong examines the growing dominance of Chinese open AI models and what it means for the global ecosystem. Emergence portfolio company Arcee AI argues that leadership in open-weight AI matters, not just for benchmarks, but for who sets the standards around transparency, governance, and performance. As adoption shifts overseas, Arcee is betting that high-performance, U.S.-built open models remain strategically critical.

Rebuilding Commercial Insurance From the Ground Up
Commercial insurance hasn’t changed much in decades. Portfolio company Harper is redesigning brokerage as an AI-native service, embedding intelligence directly into underwriting, routing, and carrier matching workflows. TechCrunch outlines how owning the full service layer allows Harper to move businesses from application to coverage in hours, not weeks, while improving submission quality for carriers. Insurance is becoming a proving ground for what AI-native services can do when they control the workflow end to end.

AI for Institutional Judgment
At the 2:54:27 mark of the latest TBPN broadcast, Michael Manapat, co-founder of Rowspace, breaks down how asset managers can leverage decades of proprietary data to power AI-driven decision-making.

Rather than layering models over messy documents, Rowspace focuses on structuring fragmented firm history into infrastructure that AI can reliably reason over. The result is not just better retrieval, but institutional judgment that compounds over time.

Rethinking How Models Learn
As enterprises move AI from pilots into production, static models are starting to show their limits. Portfolio company Adaption Labs is building systems designed to adapt continuously in real-world environments rather than remain fixed after deployment. In Fortune, co-founders Sara Hooker and Sudip Roy outline a vision for intelligence that adjusts to context, workload, and user intent, bringing adaptability to the core of how AI is built and deployed.

Yaz El Baba Named a Rising Star in Venture
Yaz El Baba was recognized on Business Insider’s 2026 Rising Stars of Venture list, highlighting emerging investors shaping the next generation of startups.

The recognition reflects Yaz’s work backing AI-native companies and founders building at the edge of infrastructure and applied intelligence. As capital rotates toward durability and technical depth, it’s encouraging to see that kind of conviction spotlighted.

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The Team Is the Signal

The most important investments we make are in our people. And we do so guided by our firm’s values. This month, we shared updates to the Emergence team that reflect how our values show up in practice: Lotti Siniscalco stepping into the role of General Partner, Kyle Murphy being promoted to Principal, and Thomas Kim becoming Head of Investment Operations.

They operate in different lanes, but have each shaped how Emergence works and represent its core values in a unique way. Lotti has built a track record of deep market work and long-term partnerships with founders, often backing teams others overlook and focusing on how people grow under pressure. Kyle has earned a reputation for showing up when things get messy and following through as an extension of the founding team. Thomas brings a systems mindset to the firm, quietly tightening feedback loops and infrastructure that reinforces good decision-making over time.

The backdrop for these team changes is a market that’s changing at a faster pace than ever before. AI products are being tested in real environments, under real constraints, with less patience for iteration theater. What used to be forgiven as “early” now shows up as fragility. In moments like this, teams and operating discipline matter more than ever.

We congratulate Lotti, Kyle, and Thomas on these well-earned next chapters. Read our full announcement to learn more about their roles and how they exemplify Emergence values.

From the Bench: New Ideas & Insights

Playing on Hard Mode
Venture loves the myth of meritocracy. Lotti Siniscalco’s latest essay cuts through it. Drawing from her own journey as a first-generation immigrant, Lotti writes about founders who build without a safety net, navigating invisible systems while trying to create something meaningful. These are the teams playing on “hard mode,” where failure carries higher stakes and resilience isn’t optional. It’s a clear articulation of why Lotti is drawn to founders with something to prove. And why immigrant founders, especially in AI, often see opportunities others miss.

Read the post →

The Rise of “Build With”
The old software binary is breaking down. It’s no longer just build or buy. In a recent post, Jake Saper lays out why a third model is taking hold in enterprise AI: build with. Companies like Sierra and Distyl are pairing a strong product core with forward-deployed engineers who adapt it to the realities of each customer’s environment. Jake breaks down why this model is emerging now, from shifting budgets to collapsing customization costs to enterprise buyers’ low tolerance for risk. He also makes the tradeoff explicit: build-with can be a durable business model, but only if founders are disciplined about turning custom work into differentiated product.

Read the post →

Valuations Are Not a Marketing Tool
Joe Floyd takes aim at a trend he’s seeing more often: companies engineering “unicorn” headlines by raising most of a round at one price, then tacking on a small check at a dramatically higher valuation. These inflated headline valuations can distort option pricing, create friction in future fundraising, and leave employees with unrealistic expectations about the value of their equity. What feels like a short-term win can quietly create long-term damage.

Read the post →

Emergence in the Wild: Catch us in the news or at events

Arcee Pushes the Frontier of Open Models
Emergence portfolio company Arcee has released Trinity Large, one of the largest open-weight models ever trained and released by a U.S. company. The launch comes at a pivotal moment for open AI, as global adoption and performance leadership in open models have increasingly shifted overseas over the past year. In a TechCrunch exclusive, Julie Bort covers the release and the significant benchmark results behind it, examining what Trinity Large signals for the future of open, transparent AI. In a world dominated by Chinese models, the US can now meaningfully compete, Joe Floyd writes.

Climbing the Enterprise AI Maturity Ladder
What happens after the initial excitement fades and AI meets the realities of scale? Early pilots are easy to launch. Making AI dependable inside a large organization is much harder. In a recent Fast Company piece, Joe Floyd introduces a maturity framework that helps leaders see where their AI efforts truly stand and what’s required to advance. The article focuses on how organizations evolve from experimentation into sustained execution, and why progress depends on capabilities that hold up under real-world complexity.

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The Signals That Matter

The AI hype cycle is ending, but what comes next will be far more interesting. After a year defined by dizzying model releases (Gemini 3 Pro, GPT-5.2, Claude 3.5, Llama 3’s weekly checkpoints), aggressive valuations (sub-$5M ARR companies raising at $1B+), and endless product demos (“agentic” features that amount to chained prompts), enterprise buyers are shifting from curiosity to scrutiny. They’re no longer asking what’s possible. They’re asking what works.

Across our partnership, the same signals are beginning to surface. And collectively, they point to a market on the edge of a real shakeout where depth, specialization, and execution will matter more than speed or scale.

AI is breaking into the physical world.
General Partner Kevin Spain predicts that 2026 will be the first year robotics companies built on top of foundation models achieve meaningful commercial scale. Robots will take on more complex tasks across more environments, and automation will move from software workflows into factories, facilities, farms, and field operations as perception, reasoning, and action loops become viable at scale. General Partner Joe Floyd sees this same pattern extending across the real world, with physical intelligence becoming one of the most important investment areas of the year.

Control of the AI stack is shifting fast.
Joe expects United States labs to respond to the rise of Chinese open-source models with specialized releases designed for real workloads rather than leaderboard performance. Kevin believes Google will reassert itself as the company to beat, based not only on model quality but on its ability to integrate AI across Search, Android, and Workspace.

Entire industries are reaching their AI inflection points.
Lotti Siniscalco sees wealth management becoming the next sector to experience true personalization through AI, something early fintech could not deliver. Kevin anticipates a breakthrough moment in life sciences as AI-designed molecules begin to show real therapeutic promise.

AI-enabled services will face a reality check.
General Partner Jake Saper predicts that 2026 will expose which AI services companies are truly automated. Growth investors will expect evidence that models, not manual labor, are carrying a meaningful share of delivery. Companies that rely heavily on human execution will stall or reset. The shift will push founders to invest in internal automation rather than bespoke customer work.

And in the middle of all this acceleration, the discipline gap is widening.
Lotti warns that founder behavior is starting to echo 2021: inflated ARR definitions, premature follow-on rounds, and limited domain expertise. Her view is clear. Big checks will still be written in 2026, but only where the fundamentals support truly outsized outcomes.

All signs point to the same shift: AI is entering an era defined by depth, not noise. Markets will reward founders who understand systems and specialization. The gap between durable and disposable companies is about to widen fast.

From the Bench: New Ideas & Insights

The Search for “Leonardos” in the Age of AI
In her newly-launched Dream Job Series, Lotti spotlights Italian founder Riccardo Di Molfetta and his stealth applied AI lab building what they call the Universal Creative Machine, a system aimed at superhuman creativity. Lotti reflects on creativity, craftsmanship, and the kind of first-principles builders this moment demands, from artists and designers to ML researchers with extraordinary taste. It’s a window into the frontier where AI meets imagination.

Read the post →

Build the Muscle of Outcomes-Based Value
In his latest post, General Partner Jake Saper challenges founders to experiment with outcomes-based pricing not to boost short-term ARR, but to build discipline. Even tying 10% of a contract to clearly defined customer outcomes forces teams to confront harder questions: What value are we actually delivering? How do customers measure success? And how do we prove we’re the reason they achieved it? Jake highlights Prosper AI as an early example of how this shift reshapes product and go-to-market strategy long before it reshapes revenue.

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Sunsetting the Phone Tree, Finally
Partner Yaz El Baba makes the case for a future where no one has to “press 9 for English” again. Bland, one of Emergence’s newest investments, has launched a system that can ingest a company’s IVR phone tree and instantly recreate it as a human-like conversational agent: no more doom-loops, button-mashing, or shouting “operator” into the void. As Yaz puts it, the thesis was always simple: phone calls shouldn’t suck. Now, with intelligent voice agents, they don’t have to.

Read the post →

Emergence Year in Review

• Closed Emergence Fund VII, strengthening our ability to back AI-native enterprise leaders.

• Invested in Physical Intelligence, helping power the rise of foundation-model robotics.

• Backed Genspark, which has reached $50M ARR and a $1.25B valuation in five months.

• Led Vesence’s seed round, advancing precision AI for the legal profession.

• Supported Layer’s next wave of growth, as it launched infrastructure built for real-time AI systems.

• Led Casap’s Series A, transforming how financial institutions manage first-party fraud and dispute operations.

• Led Cline’s Series A, accelerating the adoption of open-source AI coding agents across the enterprise.

• Backed category-defining founders at Bland, modernizing customer support through intelligent voice agents.

• Amplified Prosper AI’s momentum, as outcomes-based value creation gained national attention.

• Launched the Emergence Fellows Program, expanding access to venture for students at Berkeley and Howard.

Emergence in the Wild: Catch us in the news or at events

How AI Is Rewriting Workforce Strategy
Joe published a new byline in Fast Company examining one of the most urgent questions facing executives today: how AI will reshape workforce strategy. Joe argues that the next era of enterprise advantage won’t come from replacing people, but from redesigning roles, workflows, and team structures around AI-native systems. From new productivity baselines to emerging technical disciplines, he outlines what leaders must do now to stay ahead of the shift.

An Early Emergence Bet Hits Its Next Milestone
Federato announced a $100 million Series D, marking a major step forward for one of the most compelling companies modernizing risk and underwriting. As an early investor, Emergence has seen firsthand how Federato’s RiskOps platform is reshaping how insurers make decisions, using real-time data and AI to bring clarity, speed, and precision to an industry long constrained by legacy systems.

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Over the past year, enterprise buyers have become far more sober about AI. The novelty phase is over. Demos aren’t enough. Leaders want systems that understand their business, operate with precision, and deliver finished work, not just text.

Across the market, the companies pulling ahead aren’t the ones with the cleverest prompts or the flashiest wrappers. They’re the ones building systems around the model: infrastructure that captures context, coordinates actions, and closes the loop between intent and outcome.

That’s the shift General Partner Joe Floyd explores in his exploration of Systems of Context, and it’s a shift we’re seeing reflected in the ecosystem. One striking example: Genspark, which reached $50M ARR and raised a $275M Series B at a $1.25B valuation in its first five months. Their trajectory is less about speed than about architecture, proving that when a product assembles the right context, customers move fast because the system actually does the work.

But Genspark is part of a broader story. AI teams across industries are rethinking what enterprise software should look like when models are just one ingredient. The orchestration layer, the data fabric, the workflow understanding. This is where differentiation is forming.

As Joe writes, the next era of AI won’t be defined by bigger models, but by smarter systems built to understand the user, the task, and the environment they operate in.

Sure, the companies that embrace this shift will help people work faster. But more than anything, they’ll fundamentally change how work gets done. It’s why we invested in Genspark and why we’re leaning deeper into founders building companies where system design defines the product.

Read the full post →

From the Bench: New Ideas & Insights

Backing Physical Intelligence’s Next Chapter
Physical Intelligence (PI) is building the intelligence layer for the physical world: software that gives robots the dexterity and judgment needed for real, messy, human environments. The company has raised $600M led by Capital G, with Emergence participating in the round. PI’s models are already powering tasks like espresso pulls, box assembly, and consistent laundry folding, showing what becomes possible when robotics shifts from hardware-first to intelligence-first design.

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What AI Can’t Touch
AI is creeping into every creative corner, but some experiences still belong only to people. In his latest post, General Partner Jake Saper reflects on why live performance remains one of the few arenas technology can’t fully replicate. After conversations with musicians like Vulfpeck’s Cory Wong, Jake explores how presence, spontaneity, and shared energy form a kind of value that algorithms can’t touch.

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Stop Calling Your Hinge Matches ARR
Founders are stretching ARR definitions to the breaking point and Partner Lotti Siniscalco has thoughts. In her latest post, she breaks down the difference between ARR, CARR, pilots, and pipeline with relationship metaphors that make the distinctions impossible to forget. And she’s not alone: Jake chimed in, calling out the “math crimes” he’s seeing in today’s frothy market, where everything from one-time spend to pure pipeline somehow ends up labeled as ARR.

A timely reminder that precision isn’t optional.

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Culture Outlasts Every Cycle
In a recent conversation with Hi Marley CEO Mike Greene, Founder and General Partner Gordon Ritter returned to a theme he’s seen repeated across decades of market shifts: culture outlives every trend. As Gordon told Greene, Hi Marley’s mission to make insurance more human isn’t just branding. It’s the operating system that attracts great people, earns customer loyalty, and creates an advantage no technology can automate.

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Emergence in the Wild: Catch our partners in the news or at events

The New Reality of AI Valuations
The pace of fundraising in AI isn’t slowing and neither are the expectations. In a recent Business Insider piece on Perplexity’s rapid-fire rounds, General Partner Kevin Spain weighed in on how valuation pressure is reshaping the market. As he noted, when comparable companies raise at increasingly aggressive prices, those benchmarks quickly become the default expectation for founders and investors alike.

Emergence Recognized Among Top-Performing VC Funds Twice
In a recent analysis from Brex’s Shai Goldman, only 86 venture funds out of 2,400 with reported data have ever achieved 5×+ DPI. Shai highlighted the full list ranked by fund size, and Emergence appears on it not once, but twice.

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AI’s magic moment has already arrived for developers. Tools like Cline and Claude Code have changed how software gets built, letting engineers describe what they want and watch as AI iterates, corrects, and reasons in real time.

The rest of the enterprise world is still catching up. Most business users are clicking through configuration screens built for a pre-AI era. The next transformation will bring that same magic to everyone, where configuring a CRM or updating a workflow feels conversational, fast, and code-native.

We’re at a fork in the road. Incumbent platforms are retrofitting AI into decades-old architectures, while a new wave of startups is building AI-native systems from the ground up. Regardless of how they get there, these new platforms will speak the same languages large models understand (think TypeScript, Python, etc) and enable teams to “vibe code” business logic instead of manually stitching it together.

The opportunity is massive. In five years, we’ll be talking to our business systems the way we talk to our coding assistants today. The companies that lean into this shift by starting small, capturing workflow data, and building toward new systems of record will define the future of enterprise software.

In their latest post, Emergence General Partner Gordon Ritter and Principal David Dworksy unpack this coming transformation, from code-native architectures to the rise of truly AI-driven business systems.

Read the full post →

From the Bench: New Ideas & Insights

Precision Over Promises: Backing Vesence
In legal work, almost right isn’t good enough. That’s why General Partner Jake Saper led Emergence’s $9 million seed investment in Vesence, an AI platform built to make legal work bulletproof. Rather than generating new text that might be wrong, Vesence ensures that what’s already written is right, automatically enforcing firm-wide standards, validating references, and eliminating inconsistencies directly inside Word and Outlook. In his latest post, Jake explains why depth, not breadth, defines the next era of professional-grade AI.

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Expanding Opportunity Through the Emergence Fellows Program
Great ideas can come from anywhere, but not everyone gets the same opportunity to pursue them. That’s why we launched the Emergence Fellows Program, designed to empower talented students with scholarships, mentorship, and exposure to venture capital and entrepreneurship.

We’re thrilled to welcome our first cohort: six exceptional students from UC Berkeley and Howard University, two institutions leading the way in expanding access and social mobility. Nearly 500 students applied across both programs, and each of our selected Fellows stood out for their creativity, curiosity, and drive to use innovation as a force for change.

Over the coming months, they’ll participate in mentorship sessions, develop investment theses, and explore new ideas born on their campuses. The program represents one small but meaningful step toward making venture capital, and the opportunities it creates, more accessible to everyone.

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Venture Requires Optimism By Design
A decade ago, Partner Lotti Siniscalco was learning what venture capital is. After a previous ten years in traditional finance, it took time—and a few great mentors—to unlearn risk aversion and embrace the optimism venture requires. Last week, Lotti returned to Stanford GSB as a guest in Professor Ilya Strebulaev’s venture capital class (now co-taught by Emergence founding partner Brian Jacobs) to share how AI is reshaping the industry. In her latest post, Lotti reflects on that full-circle moment and why opening doors for others is the most meaningful part of the journey.

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All You Need Is Context
Foundation models are powerful but generic. Real enterprise value comes from the System of Context: an architecture that gives AI the grounding it needs to act intelligently. By owning and structuring data across six dimensions—subject, task, domain, time, relationships, and tools—AI systems can move beyond text generation to real-world action. The result is what General Partner Joe Floyd calls Value over Model (VoM): the defensible edge created when context becomes the moat.

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Why Companies Fail Post-Series A: Failing to Institutionalize ICP
The fastest way to implode after your Series A? Chasing every buyer who shows interest. In his latest post, General Partner Jake Saper warns that in the AI era, where enthusiasm is high and differentiation is thin, companies that don’t institutionalize their ideal customer profile (ICP) risk scaling chaos instead of traction. Drawing on lessons from 80+ Series A investments, Jake breaks down how to align marketing, sales, and product around your “hell yeah” customers. Spoiler alert: Discipline beats demand every time.

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Emergence in the Wild: Catch our partners at these upcoming events

Santi Subotovsky at TechCrunch Disrupt This Week
Santi is taking the stage multiple times at TechCrunch Disrupt in San Francisco, happening right now. Earlier this week, he joined Zoom founder Eric Yuan to explore what comes after breakout success. He then hosted a breakout session with Melissa Wong, Zipline’s CEO and cofounder, on the founder journey. Yesterday he served as a judge for the Startup Battlefield competition, where the world’s top early-stage startups competed for global recognition.

Lotti Siniscalco at Slush 2025
Lotti will join Seedcamp and USV on stage at Slush in Helsinki this November for a panel on “What Your Fund Size Says About You and Your Strategy.” The discussion will explore how fund size shapes investment approach, portfolio construction, and long-term vision. Learn more here.

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Across the venture ecosystem, a subtle but significant power shift is underway. Founders are no longer just grateful for capital. They are deliberate, discerning, and determined to build on their own terms.

At Primary’s NYC Summit earlier this month, Serena Williams and Beth Ferreira of Serena Ventures captured the moment clearly, noting that “founders are getting choosier.” They observed that even as capital becomes more available, today’s entrepreneurs are evaluating investors on more than dollars, prioritizing partners who bring strategic value, aligned values, and a willingness to roll up their sleeves.

The market is validating this mindset. In September, Germany advanced a landmark startup-finance reform bill designed to simplify fundraising and strengthen founder equity positions. Plus, Fenwick’s Q2 Venture Beacon reported a marked rise in founder-friendly terms, from greater governance protections to stronger control over future dilution.

These signals reflect a new equilibrium: one in which exceptional founders have the leverage to choose partners who do more than write checks—partners who accelerate vision into reality.

That philosophy has long guided the companies in our portfolio.

• Zoom exemplifies the power of founder conviction. Eric Yuan turned down early acquisition offers to stay focused on building the most reliable video platform on the market, a choice that positioned Zoom to become a global-recognized verb.
  

• Gusto has shown how independence can scale. CEO Josh Reeves has consistently prioritized culture and long-term impact over short-term liquidity, resulting in one of the most trusted SMB brands in fintech.

• Together AI CEO Vipul Ved Prakash has been deliberate about building with the right long-term partners to ensure Together can scale responsibly and remain independent. By choosing investors aligned with his vision for open and efficient AI infrastructure, Vipul has positioned Together as a leader in helping enterprises train and deploy models on their own terms.
  

Founders:
The current market gives entrepreneurs both permission and incentive to be selective. Choose investors who share your ambition, respect your roadmap, and will help you build enduring companies, not just the next funding milestone.

From the Bench: New Ideas & Insights

Protecting the Human Algorithm
In his latest post, Gordon Ritter argues that in the age of AI, a company’s true moat isn’t just its data. It’s the human algorithm: the instincts, processes, and collaboration patterns that turn raw data into insight. Foundation models can replicate outputs, but if you expose the way your teams work, you risk giving away your competitive edge. Gordon makes the case for safeguarding not just data, but the intellectual property embedded in how your best people operate.
Read the post →

Prosper AI: Turning Hold Music into Healthcare Access
Jake Saper highlights Prosper AI, a new Emergence investment transforming one of healthcare’s most frustrating pain points: endless hold music, prior authorizations, and administrative back-and-forth. Prosper’s voice-AI agents streamline these workflows so patients get care faster and billing teams can focus on higher-value work. Already, the company has quadrupled revenue in a single quarter and is winning praise as the most important innovation since the EHR.
Read the full post →

Breaking the Data Monopolies
Lotti Siniscalco argues that decades-old data gatekeepers, from credit bureaus to electronic health records, have slowed innovation by locking up critical datasets. The rise of AI-powered workflows flips the script: startups can now capture fresh, high-resolution data at the point of use, creating living datasets that improve with every interaction. The real opportunity isn’t prying open legacy silos. It’s building the next generation of datasets from the ground up.
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Stop Running Free Pilots
Jake Saper’s latest post takes aim at one of the biggest leaks in enterprise sales: unstructured, free pilots. After reviewing POC outcomes across the Emergence portfolio, the data is clear: priced and structured pilots close 3x more deals. Jake shares five best practices for turning POCs into a true enterprise motion: define success criteria upfront, time-box ruthlessly, pre-commit next steps, get access to decision-makers, and document like a contract. The message is simple: your pilot isn’t a favor. It’s the foundation of your deal.
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Gordon Ritter on the Yesterday Podcast: Conviction, Courage and Climbing
On the latest episode of the Yesterday Podcast, Gordon reflects on the moments that shaped Emergence: his father’s courage covering the Freedom Riders, the day he signed Salesforce as a partner despite intense pressure to walk away, and the 15-month grind to raise Emergence’s first fund. He even shares lessons from climbing Denali, and why backing founders willing to step onto the ridgeline matters more than ever.
Listen to the full episode →

Emergence in the Wild: Catch our partners at these upcoming events

Santi Subotovsky at TechCrunch Disrupt
Santi will take the stage multiple times at TechCrunch Disrupt in San Francisco. On October 27, he’ll join Zoom founder Eric Yuan to explore what comes after breakout success, then host a breakout session with Melissa Wong, Zipline’s CEO and cofounder, on the founder journey. On October 28, he’ll serve as a judge for the Startup Battlefield competition, where the world’s top early-stage startups compete for global recognition. Register here to attend.

Lotti Siniscalco at Slush 2025
Lotti will join Seedcamp and USV on stage at Slush in Helsinki this November for a panel on “What Your Fund Size Says About You and Your Strategy.” The discussion will explore how fund size shapes investment approach, portfolio construction, and long-term vision. Learn more here.

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AI startups are everywhere, and, increasingly, there’s a grand schism of opinions separating them: lofty promises on one side, doomsday predictions on the other. It’s easy to lose sight of the real impact of the technology amidst this noise.

The simple truth is this: good AI makes daily life easier. And two Emergence portfolio companies, Casap and Cline, are doing just that—building AI tools to tackle the roadblocks many of us have experienced. Casap aims to make payment disputes less of a pain for both consumers and financial institutions, while Cline is increasing transparency in service billing to democratize coding tools.

This month, both companies announced Series A funding rounds led by Emergence.

On the surface, fintech and developer tooling are very different markets. But these companies have a similar goal—they’re using AI to increase consumer trust in systems that sorely lack it, making life markedly easier for its users in the process.

Casap: Taking the pain out of credit card disputes

No one wants to spend hours on the phone disputing a fraudulent credit card charge—including the bank. Today, dispute resolution is a sluggish, manual process that can often take up to 90 days. The reason for the lengthy timeline? Disputes aren’t always filed truthfully. First-party fraud, when users defraud their institutions, has grown 2.5x year-over-year and now represents over 35% of all losses—a staggering $100B+ problem.

Casap is rebuilding the broken dispute process using intelligent automation. But converting financial institutions into paying customers requires deep empathy and extensive compliance knowledge. Casap founders Shanthi Shanmugam (Robinhood, Meta) and Saisi Peter (Chime, Instacart, Meta) have both, which is one of many reasons we led Casap’s $25M Series A. Shanthi and Saisi have managed to transform dispute operations from a lengthy, risk-prone process into one that is trustworthy, data-driven, and efficient.

The results are impressive: Casap customers have cut first-party fraud losses by up to 51% while processing disputes 50% faster, dramatically improving bank member satisfaction.

Cline: Proving that developer-first principles can scale

Developers love AI coding assistants, but they’re increasingly frustrated by opaque pricing. When Anthropic quietly added usage limits to Claude Code in July, users were dismayed, thinking their usage had been inaccurately tracked.

These sudden pricing changes and hidden restrictions are emblematic of the challenges Saoud Rizwan aims to solve with Cline, an open source AI coding agent that gives developers full transparency and control. Saoud and his team at Cline are proving that developer-first principles can scale into enterprise-ready infrastructure. Cline is a welcome entrant for many developers.

Saoud first developed Cline during an Anthropic hackathon in 2024. Less than a year later, the platform has 2.7M installs, 50K GitHub stars, and has been adopted by enterprise teams at Samsung and SAP.

Cline is transparent by design: Developers pay AI tool providers—like Google or OpenAI—for the cost of running models, and Cline shows them a full breakdown of the cost for each. Because Cline is open source, developers can see their usage and exactly how they’re being billed, so they have more freedom to choose whichever model they want for a specific task.

Beyond model transparency and open source infrastructure, Cline was built for agentic workflows. After reading a developer’s codebase, it asks clarifying questions, plans multi-step actions, and navigates real-world development environments. Saoud and his team designed these agents to approach a problem like a real engineer would.

We led Cline’s $27M in Series A funding because their approach isn’t just about giving developers a cheaper option—it’s about giving them trust and control in high-stakes coding environments.

“Cline is open source, so you can kind of peek into the guts of the harness and kind of see how the product is interacting with the model, which is incredibly important for having control over price transparency,” Saoud told Forbes.

From the bench: New ideas & insights

When AI apps start to look like infrastructure
Read the post →

Yaz El-Baba discusses why the line between apps and infrastructure is disappearing. He dives into why winning AI teams today aren’t just using models, they’re building infrastructure to adapt, optimize, and stay ahead of the AI frontier.

AI is reshaping the B2B GTM motion
Read the post →

Jake Saper shares insights from our Beyond Benchmarks report on how AI-native workflows are changing sales orgs from shrinking SDR teams to the rise of forward-deployed engineers.

Should immigrant founders build in the U.S.?
Read the post →

Lotti Siniscalco reflects on why AI founders targeting American customers should consider building here, and the personal tradeoffs that come with that choice.

Emergence in the wild: Catch our partners at these upcoming events

• Santi Subotovsky is wearing many hats at TechCrunch Disrupt in San Francisco. On October 27, he’ll take the stage with Zoom founder Eric Yuan to explore what comes after breakout success. He’ll also lead a breakout session with Melissa Wong, Zipline’s CEO and cofounder, on the founder journey. On October 28, he’ll judge the Startup Battlefield competition, where the world’s top early-stage startups compete for global recognition. Register here to attend.

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It’s been a high-energy July across the Emergence portfolio, and the right teams building in the right markets are delivering big.

We recently welcomed Layer to the Emergence family. The company is reinventing accounting for small businesses with an AI-powered platform that’s fast to deploy, easy to use, and deeply embedded in the tools SMBs already rely on. Founders Justin Meretab and Daniel O’Neel bring experience from Square and Wealthfront, and a clear product vision to make high-quality bookkeeping effortless. With over 30,000 customers already using Layer, they’re well on their way to becoming the go-to accounting solution for the modern SMB stack.

Unify also announced their $40M Series B and surpassed $5.6M in ARR. By automating intent signals, outreach, and message sequencing, their software is quickly becoming the GTM engine of choice for AI-native companies like Cursor, Perplexity, and Together.ai. Co-founder Austin Hughes and his team are proving that the next generation of sales tooling will be powered by intelligent automation.

Meanwhile, the Artificial Intelligence Underwriting Company (AIUC) emerged from stealth with a $15M seed round to build the insurance and certification infrastructure for autonomous AI agents. As enterprises increasingly look to deploy agentic AI, AIUC’s audit and liability coverage model, anchored by their AIUC-1 framework, could do for AI what SOC 2 did for cloud security.

We’re proud to back all of these ambitious teams and their missions to build category-defining companies.

From the Bench: New Ideas & Insights

Disrupting Data Monopolies to Liberate the Next Generation of Data
Read the post →

Lotti Siniscalco distills why AI-native workflows are finally giving startups the tools to challenge legacy data giants, and how to build defensible moats in the process. “The opportunity isn’t to replicate their closed systems with updated infrastructure,” Lotti writes. “It’s to build embedded, AI-powered, high-value workflows that generate aggregated compounding insights.”

Charging for Intelligence: How to Price AI Software
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At a recent San Francisco event, Jake Saper joined pricing experts Madhavan Ramanujam and Josh Bloom to explore how AI is reshaping software pricing. As Jake notes, “Buyers never wanted to buy software—they wanted solutions.” With AI increasingly delivering those solutions autonomously, pricing models are shifting away from usage-based fees toward models that charge for outcomes and value delivered.

Beyond Benchmarks: The New Language of Startup Performance
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Earlier this summer, we released our 2025 Beyond Benchmarks report. In this post, Joe Floyd reveals the report’s key insights, including what top-performing SaaS companies are doing differently in 2025 and the Emergence Core Four. “We’re operating in a new paradigm where AI-native architecture, product-led growth, and strategic efficiency are setting the pace,” writes Joe.

The Rise of the Forward-Deployed Engineer
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Gordon Ritter and Kyle Murphy spotlight a new role that’s reshaping how enterprise AI is implemented and scaled: the forward-deployed engineer. As Gordon and Kyle see it, AI models are the gold and FDEs are the miners. Without FDEs, you can’t extract the gold from the mine.

Signal Boost: Emergence and our portfolio companies in the news

• This spotlight from Business Insider highlights Emergence’s 22-year partner retention streak and how other VC firms can rethink their talent models for longevity.

• CNBC named several Emergence-backed startups to its Top Fintech Companies of 2025 list, including Chainalysis, BILL, Gusto, and Hi Marley.

• Jake Saper was featured in this Business Insider article about Windsurf’s rise and fall, and why product-led growth without pricing clarity is a cautionary tale.

Emergence in the wild: Catch our partners at these upcoming events

• Yaz El-Baba is attending Ai4, the largest AI event in North America, on August 11 in Las Vegas, where he’ll be moderating a panel called "Leveraging AI to Drive Innovation and Growth in SaaS.” Register here to attend.

• Santi Subotovsky is attending TechCrunch Disrupt, on October 27 in San Francisco, where he and Zoom founder Eric Yuan will engage in a fireside chat about their perspectives on scaling companies, preparing for public markets, and steering through pivotal transitions. Register here to attend.

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Hello readers,

In today’s market, SaaS founders are navigating more change and more pressure than ever before. AI is no longer a future bet; it’s a present-day differentiator. Go-to-market strategies are evolving in real time and efficiency is now expected.

With so many shifting signals, the old benchmarks don’t cut it.

That’s why we created Beyond Benchmarks, a report series from Emergence in partnership with Benchmarkit based on data from 560+ B2B SaaS companies. Our latest research is more than a performance snapshot. It’s a look at how the most durable, forward-thinking startups are building in 2025.

What you’ll find inside:

• 📈 AI-native startups are growing 4x faster than traditional SaaS (100% vs. 23% median ARR growth)
  

• 💰 67% of growth at $100M ARR comes from customer expansion, not new acquisition
  

• 📊 The Emergence Core Four metrics that consistently correlate with long-term success: ARR Growth, CAC Payback, NDR, and Burn Multiple
  

• ⚠️ Why GenAI adoption in sales isn’t translating to results and what’s missing
  

This report is designed to give founders, operators, and investors a sharper lens on what “good” looks like now and what it will take to win in the next chapter of SaaS.

📥 Download Beyond Benchmarks

Thanks for being part of the Emergence community. We’re excited to share this with you.
Want more context? Read our perspective on what the data reveals → LINK.

— Joe Floyd, General Partner at Emergence Capital

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For several years at Emergence, we've been using what we initially called our "Delta Metric" to evaluate how effectively AI software learns from users and evolves over time. For the first time, we are sharing this metric publicly. We call it the AI Emergence Rate.

The AI Emergence Rate measures the impact of AI software through the incorporation of proprietary business data and processes, leading to ever-improving outcomes for the software’s users.

In a world where B2B software must improve every day, the AI Emergence Rate metric is what separates stagnant AI software startups from those that continuously optimize their solutions. The result is better outcomes for customers and better chances of startup’s success in an incredibly competitive AI software landscape.

Why We Built the Emergence Rate

Our journey to the Emergence Rate began with our AI Coaching Networks thesis in 2017. Over years of diligence, we observed how the best enterprise AI software startups responded to the constantly evolving needs of their customers.

By today’s measures, even the best startups of the last two decades operated relatively statically. This is simply a reflection of real-world constraints: the number of engineers they can employ, the time it takes to turn feature requests into code, the technological constraints of this era of software, and their customers’ discomfort with too many big changes confusing their users.

In the Client-Server Era, a slick demo and out-of-the-box performance typically won prospects over. Companies paid big upfront license fees for on-prem installations. Meaningful upgrades occurred, it seemed, whenever the Olympic Games came back around. Over time, they struggled to keep up with their customers’ evolving needs. Then a new generation of SaaS startups, led by Salesforce.com, emerged to address new challenges, and customers migrated to them.

The cycle has now repeated with the generative AI breakthrough. The new era of AI software has unleashed unprecedented capabilities that can help startups break this cycle and add value for customers over the long haul. The new leaders will be AI software startups that continuously increase the value of their customers’ proprietary assets–most importantly, the ingenuity of their workforce.

Here is how both sides of the software marketplace–startups and their customers–can think about the Emergence Rate framework, and measure the revolutionary impact of AI on their businesses:

• For Startups: A high Emergence Rate–the ability for AI models to automatically refine and improve themselves–distinguishes market winners from those who will stagnate. Startups with a high Emergence Rate deliver products that evolve with their customers, commanding premium prices, and avoiding the churn common among early adopters of AI software.

• For Customers: AI Software with a high Emergence Rate becomes increasingly valuable to their business over time, unlike traditional software, which depreciates. This self-improving AI soon becomes the indispensable heart of the company, worth paying for indefinitely, and providing compounding advantages that create massive strategic differentiation in the market.

How We Define the Emergence Rate: Three Key Metrics

Through our portfolio work, we've refined the Emergence Rate into three essential dimensions:

1. Rate of Change: How frequently does an AI system assess variants based on granular user activity? The faster an AI system integrates unique behaviors, the higher its Emergence Rate.
   
   Today's most advanced AI software implements continuous learning loops, whether through ambient observations (e.g., RPA) or user collaborations within agentic workflows. The best AI software captures and applies insights in real-time without requiring manual updates, as models dynamically change with every new piece of user interaction that’s generated.
   
   In many ways, this is what Scale AI and Surge AI provide for foundation models–filling in large-model deficiencies with the unique abilities of humans (the “HF” in RLHF). Startups focusing on proprietary commercial markets have the advantage of workflow relationships with their customers’ users. The large models won’t have access to this “firewalled” user activity. Startups that carefully cultivate this software relationship at the center of user activity, protecting customers’ treasured IP, will become indispensable.
   ‍

2. Quality of Change: Not all variations improve outcomes—and quality matters more than volume. One poor recommendation can undermine trust in an entire system, even if preceded by many good ones.
   
   At Emergence, we've observed that outcome improvements can be identified through rigorous variant testing. Our portfolio companies typically analyze hundreds of interactions, group users into performance tiers, and identify successful patterns. With AI, we now have granular data to test, using interstitial feedback as proxies where feedback loops are long (as in sales cycles).
   
   For example, when a patient receives a more personalized note from a healthcare provider following a visit, they may be more likely to take the recommended actions, which could improve patient adherence.
   
   From an engineering lens, an AI may be able to observe the qualities of a successful pull request (e.g., minimal discussion, no downstream errors, no rollback) and tailor its style and tests to adhere as closely as possible to those attributes.
   ‍‍

3. Breadth of Adoption: Are insights from top performers disseminated organization-wide? This requires excellent user experience design. Once a successful behavior is identified, the system can either automate the process or guide all users toward it, through dynamic UX.
   
   The goal isn't to create a "mushy middle" of average performance, but to maintain human innovation while elevating the quality of the average outcome. This could mean anything from increasing the rate of successful closes, to raising average ACV, to improving cohort retention rates. Any business scenario can benefit from this cycle.
   
   Finally, keeping human users in the loop throughout enables them to adopt recommendations and experiment with variations, preventing dependency on what we call "Crutch Software"— systems that do the work, but don't help users learn or improve.

How Companies Can Measure Their Own Emergence Rate

Based on broad diligence and portfolio experience, here is a five point framework for a startup to measure its Emergence Rate:

1. Track variant identification: Document how many unique user behaviors the software identifies within a given timeframe. The more granular the detection, the better.
   

2. Implement outcome-based metrics: Define clear success metrics for the specific industry or function the software is built for. In sales, this might be deal closure rates; in healthcare, patient outcomes; in life science, clinical trial patient recruitment.
   

3. Assess variant quality: Measure how much performance variance can be attributed to specific behaviors the AI has identified. A number of our AI companies bootstrapped variant quality by first assessing who on the team produced the variant. If that person is a high performer in their area, their actions become a proxy for variant quality.
   

4. Monitor UX evolution: Count the number of automatic UX or agentic workflow changes that occur reflecting learned behaviors. These should increase over time as the system identifies successful patterns.
   

5. Measure adoption speed: Track how quickly users adopt newly recommended behaviors, and whether those behaviors persist over time, or inexplicably fall away.

The most successful companies in our portfolio collect historical user data, analyze patterns across performance tiers, and focus particularly on raising the bar by capturing and sharing the brilliance of outliers across the organization, so that all may benefit.

The Value of Proprietary Data

Every kind of business, no matter its size or activity, is built on unique intellectual property, industry-specific knowledge/processes, and proprietary datasets. This proprietary “data” are what AI software startups can use to help those businesses overcome what we call the AI Plateau.

AI software startups with a high Emergence Rate populate their platforms with these proprietary data in a way that competitors cannot access. This allows a customer to benefit from increases in the number of successful outcomes at a rate not possible in previous eras of software. In turn, as the AI software learns from human-generated insights that are both rare and valuable, an AI software startup’s Emergence Rate increases, allowing it to better process new data. We call this “The Perpetual Algorithm.”

The advantage for an AI software startup lies in becoming the perpetual algorithm for its domain—continuously learning from user patterns and serving those insights back to its customers in a virtuous, ever-improving cycle.

The Future of Adaptive Intelligence

Humans are natural "a-ha engines." Our minds have nearly 100 billion interconnected neurons and store our unique lived experiences, enabling boundless creativity, adaptability, and innovation. We constantly seek breakthroughs that take us beyond the status quo. Improving processes–from the printing press to the tractor to the spreadsheet–is a core way that we express creativity and ingenuity.

The best AI software harnesses this innate human behavior by identifying when users have discovered (or stumbled upon) new successful approaches, validating their impacts on the business, and distributing these insights organization-wide.

Looking ahead, we know AI software with a high Emergence Rate is essential for enterprise success. The most forward-thinking AI software buyers have moved beyond startup-maintained deployments toward constant renewal. Their software evolves continuously based on real-time activity, maintaining their competitive edge by harnessing their proprietary, uniquely-human innovations to improve the entire enterprise. The best startups will be at the center of this new rate of innovation.

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In 2024 we launched Beyond Benchmarks, quantifying the most critical performance metrics across the B2B SaaS industry. This year we’re doing it again!

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We launched our firm at the dawn of a platform shift—when workplace technology was moving from on-premise to the cloud. Our goal was to identify and support the bold founders leading this transformation. 

That’s exactly what we did.

We partnered with visionaries like Marc Benioff at Salesforce, who pioneered cloud-based business software; Peter Gassner at Veeva, who redefined technology for life sciences; and Eric Yuan at Zoom, who revolutionized how we communicate.

Looking back over our first twenty years, one thing is clear: partnership with Emergence bends the odds of success for B2B founders. More than 1-in-10 of our early stage investments have gone public. From the earliest days of a company’s journey, we’ve been there—not just as investors, but as true partners—helping founders turn emerging ideas into iconic businesses.

Today, we find ourselves amidst another platform shift as AI transforms how work gets done. Leaning on twenty years of experience helping B2B companies succeed in moments exactly like this one, we are thrilled to announce Emergence VII—a $1 billion commitment to supporting the next generation of founders on their journey from emerging to iconic.

A Track Record of Bending the Odds

Our unique approach to partnership helps us improve the odds of making it from emerging to iconic. To mark our twentieth anniversary and the launch of Fund VII, we crunched the numbers to measure the quantitative impact of our work.

Here’s what we’ve found:

• Over 9-in-10 of our early stage investments have gone on to raise successful follow on rounds.

• Over 1-in-5 of our early stage investments have surpassed $1B in valuation.

• Over 1-in-10 of our early-stage investments have gone public.

• We’ve generated over $8B in realized returns on less than $2B invested, funding leading nonprofits working on cancer research, climate, and more.

The data affirms what we’ve long believed—Emergence bends the odds of success for founders. And when they win, their impact extends far beyond the business world, funding some of the most important causes of our time.

The Emergence Difference

These results don’t happen by accident. What sets Emergence apart is not just the companies we invest in, but the way we invest in them. Our approach to partnership is intentional, hands-on, and built for every stage of the company-building journey.

We invest exclusively in founders shaping the way the world works.

We are laser-focused on B2B software, bringing deep, thesis-driven expertise and an elite network of operators to help founders navigate every phase of growth. Our playbook spans every B2B go-to-market approach, turning market insight into execution advantage.

We go all in on every investment to bend the odds of success.

Each of our Partners makes just one investment per year—a deliberate choice that allows us to go deep with every founder. When you work with us, you get the full firm behind you. Every Partner contributes to diligence, strategy, and execution, ensuring you benefit from our collective experience and relationships.

We are built for enduring partnership.

We’ve built something rare in venture: a firm with zero Partner turnover and a culture of developing talent from within. That means the team you start with is the team that will see you through. From your first round to IPO and beyond, we’re in it for the long haul.

Our Founders Building the Future of AI

The best proof of our impact comes from the founders we back. 

As AI reshapes how work gets done, we are privileged to partner with the visionaries defining this next era of workplace technology—not just as investors, but as dedicated partners in their journey.

At Together.ai, Vipul is enabling enterprises of all shapes and sizes to train and optimize their own proprietary models tailored to their workflows. Our interest stemmed from deep thesis work on the potential for open source LLMs, and we were the first to have conviction in his Series A. But our partnership went far beyond the check—we introduced him to Zoom, which became Together’s first enterprise customer, and helped bring in a key engineering leader to strengthen the team.

At Bolt.new, Eric is revolutionizing software development by creating an AI-powered platform for anyone to build and launch full-stack web applications without writing a single line of code. We quickly became a key thought partner, working alongside him on product strategy, go-to-market execution, and talent acquisition. Eric and his team know they can rely on us not just for strategic guidance but for deep operational support at every stage of growth.

At Unify, Austin is transforming how revenue teams operate, using AI to streamline and optimize go-to-market motions. Emergence backed Austin from day one, and our role has been hands-on ever since—connecting him with some of Unify’s largest customers, helping him build his early team, and shaping strategic decisions that accelerated their explosive growth.

At Bland, Isaiah is empowering companies to build voice AI agents tailored to their unique goals, from replicating the tone of their brand to creating multi-language support for global audiences. Before we formally partnered, we worked with Isaiah to help him refine his market perspective and position Bland for long-term success. 

These aren’t just companies—they are founders on a mission to reshape how we work. And they aren’t just investments—they are partnerships built on trust, deep engagement, and a shared vision. As we enter the AI era, we remain committed to bending the odds in favor of the next generation of iconic B2B companies and the leaders behind them.

Building Something Iconic?

We are still in the earliest days of AI and the generational platform shift it represents. It’s hard to imagine a more exciting time to build technology that transforms the way the world works.

While AI breakthroughs are rewriting what’s possible, the fundamentals of building a great B2B company remain the same. The playbooks for scaling, selling, and winning in B2B software are well-worn—and we know them inside and out.

That’s where Emergence comes in. 

For over twenty years, we’ve backed some of the most iconic B2B founders, helping them navigate industry shifts, scale their businesses, and redefine their categories. We’ve developed a partnership model that’s an “n of one” in venture—designed to help founders bend the odds of success from emerging to iconic.

We’ve seen this movie before. We helped the most iconic founders move business from on-premise to the cloud. Now, with Emergence VII, we’re ready to do it again in the Intelligence Age.

If you’re building at the intersection of AI and work, we want to meet you.

🎙️Podcast Update

GP Jake Saper recently appeared on 20VC:

GP Joseph Floyd on Venture With Grace:

GP, Co-founder Gordon Ritter & Partner Yaz El-Baba, on Sourcery, with Molly Shea:

We’ll see you soon with more news!

—Team Emergence

Greetings from Emergence Capital! Today we’re sharing this recent post by Senior Associate Kyle Murphy, which unpacks the new categories of opportunity that increased data portability in fintech have created.

In case you missed it, we’re also sharing news of a few recent promotions at Emergence, and the value they bring to our team and portfolio:

• Yaz El-Baba is now a Partner

• Tammy Han is now Talent Partner

Congratulations to them both (and to Kyle for being promoted to Senior Associate), and thanks as always for being part of the Emergence community!

DATA PORTABILITY KEY TAKEAWAYS

• Historically, increasing financial data portability has helped drive a new wave of generational fintech companies.For example, Plaid made it easier to share banking data, which was a key enabler for companies like Chime, Robinhood, and Brex.

• Agentic AI can now access and process data at near-infinite scale. Data that typically required an army of humans to access and enter manually is now unlocked with AI.

• Agentic AI has the potential to bring data portability to almost any dataset. This could unleash a new wave of infrastructure and application layer companies across categories including accounting, procurement, and risk operations. Companies like Adaptive in construction account, and Anon in agentic data extraction, have already taken advantage of this.

Financial data is more accessible – and portable – than ever before. Consumers can easily share their transaction and balance data to apply for a Buy Now Pay Later loan on sites like Affirm or Klarna. Businesses can easily share their ACH and balance information to fund their new Ramp account. Anyone who has linked one account to another using Plaid, Yodlee, or one of their competitors, knows how relatively seamless this process has become.

In the era of AI, financial data portability will only keep accelerating. Improved OCR and agentic-data extraction products are enabling unstructured data to be parsed and analyzed easily; luckily, there is no shortage of this in financial services.

Before the proliferation of financial data aggregators like Plaid, physical document sharing and data entry were the primary ways people shared financial data. When someone applied for a loan from a new institution, they needed to go to their bank to print their bank statement. This lack of financial data portability caused less competition and ultimately worse customer experiences.

Plaid and other digital banking data aggregators like Finicity significantly enhanced financial data portability, especially for consumers. Data portability was one of a few major inputs leading to a new generation of consumer-focused financial services companies. Consumers were tired of their sub-par experiences with traditional banks and brokerages, so they opened accounts at neobanks like Chime or neo-investment platforms like Robinhood. Others wanted or needed to be underwritten with data outside of just their credit score, so they applied for a loan with Upstart.

Meanwhile, the Consumer Financial Protection Bureau’s recent 1033 ruling is a major policy win for the cause of financial data portability. Most financial institutions by law will need to enable consumers to seamlessly share their financial data with the tools they choose to use. Banks that were previously hesitant or lacked the technical ability to support financial data portability will have to get on board. As a result, consumers will be able to more easily explore financial offerings and will become more accustomed to sharing their data.

More companies are applying the “user-permissioned data access” concept to other financial data sources. Companies like Codat and Rutter have made accounting data available via API. Finch has done the same for employment data, and Method has accomplished this in the field of debt data.

AI now has the ability to pull data from almost any source through agentically parsing unstructured data. These “AI-enabled data integrations” like Anon will be able to unlock any user-permissioned data source, even for sites without APIs.

As financial data portability increases, two categories of opportunities are emerging:

1. Existing categories that will face a replacement cycle due to lower data gravity as users can more easily migrate their data to a new solution, reducing incumbent data stickiness.

2. New solutions that previously weren’t possible emerge

Let’s unpack the opportunities in each of these categories below:

01. Existing categories that face a replacement cycle due to lower data gravity

Risk operations tools. Risk teams across onboarding and underwriting have more data sources at their disposal than ever before. As these datasets increase, risk operations tools like Federato in Insurance and Taktile, Greenlite, and Accend in Financial Services will likely become more critical for managing the growing complexity of decision logic across these sources.

Loan servicing. Traditionally, lenders and servicers have a point in time snapshot of a borrower’s financial health during underwriting. The primary data points available after this are if the borrower pays their debt on time. With continuously integrated cash flow, debt, and accounting (if B2B) data, risk teams can get a real-time view into account activity to better understand their portfolio risk and more proactively help borrowers with repayment plans and financial health. Method, Plaid, and Basis already serve user-permissioned data to lenders to help increase this visibility.

Accounting services: Companies have traditionally used legacy ERPs like Quickbooks and NetSuite to manage their finances. Through accounting and bank data integrations, companies can easily sync their data with third-party tools in real time. This makes it easier for a company to use an external vendor to manage certain accounting workflows. For example, Adaptive is using Quickbooks and Plaid integrations to help GCs and construction accounting firms more easily manage their AP/AR and bookkeeping, while Basis uses GL and Plaid integrations to help large accounting firms automate bookkeeping workflows for their clients.

Tax services: Similar to accounting, most of the datasets used for filing taxes are from data sources like ERPs and GLs that traditionally had low financial data portability. Consequently, tax firms would spend lots of manual hours ingesting this data. AI can now parse this data, which creates opportunities for new, tech-enabled firms like Haven or platforms like Black Ore that enable CPAs to process more tax clients.

02. New, previously impossible solutions emerge

Continuous accounting close - Today, companies close their books every month partially because of the manual, high-latency work required to pull financial data. Now that it’s becoming more feasible for them to pull this information in real time, companies can have an accurate view of their books in real time, achieving “continuous close.” Companies like Numeric, Stacks, and others have taken advantage of these improved connections.

Synthesis of alternative data for underwriting. Traditional credit scores do not always capture your full financial picture. This causes lenders to reject applicants that they could accept if they had additional data. As mentioned above, some lenders have implemented raw alternative data like cashflow data into their underwriting processes. However, most lenders want an out-of-the-box, standardized solution. Now that new sources like banking data have become available and are increasingly relied on, there’s an opportunity for vendors like Prism Data, Heron Data, and Nova Credit to help lenders more easily make sense of these new datasets.

Real-time vendor aggregation and pricing visibility for procurement - Now that AI can parse invoices, contracts, and different online sites, it could be possible to get a real time view into all vendors and their associated pricing. This may make it easier to aggregate the supply side of a marketplace and help overcome cold-start problems. KeyChain has employed this AI-enabled approach to help retailers identify the best possible manufacturing partners, and Parspec has done this in lighting and electrical construction materials.

Agentic data extraction and integrations - AI agents can already replicate many human tasks, and this will only accelerate as inference improves. Soon, agents will be able to access any dataset a human would at an infinite scale, even if it needs to be human-permissioned. There are likely many use cases of this data, especially in financial services, that would support a new company to be created. Companies like Deck, Extend, Integuru, Anon, and Blue Sierra are using AI to parse data that was hard to scalably access and process.

At Emergence, we’ve been spending a lot of time thinking about how increasing financial data portability will impact fintech and B2B software and the infrastructure needed to support this.

With growing AI breakthroughs and regulatory tailwinds, financial data portability will only accelerate. Historically, an increase in data portability has helped drive a new wave of generational companies, and we’re confident that this will continue. We’re excited to see which companies of today will paint the future the way that Plaid, Chime, and Robinhood did when they started 10-15 years ago.

These rapid developments present some new questions we are working through:

1. What other categories will be enabled due to increased data portability?

2. How will data permissions work in a world of AI-enhanced data portability?

3. How can regulators promote innovation while mitigating harm to consumers?

Are you a founder building in this space? Or are you considering doing so? We’d love to hear from you! Feel free to reach out to kyle@emcap.com.

Greetings! We’re sharing a new article by General Partner Jake Saper and Associate Rishub Nahar, offering tactical advice to founders building AI-enabled services. We hope it’s both valuable and thought provoking.

While SaaS startup founders have playbooks and mentors to guide them, AI-enabled services founders are charting new territory. These pioneers combine AI with human expertise to deliver faster, better, and cheaper outcomes than legacy service providers. Take Mechanical Orchard – they're using AI to accomplish in months what traditionally takes years: moving mainframes to the cloud.

We wrote about this emerging model last year. Since then, we've seen an explosion of companies adopting it, many with compelling early traction. But here's the catch: founders are trying to force-fit traditional SaaS strategies onto these service-based businesses. There’s some key similarities, but also some critical differences.

Having invested in and studied this emerging space, we've identified 5 essential lessons for building an iconic AI-enabled service company. Consider this a contribution to the new playbook.

1. Bring on a domain expert early – they are even more critical than before

In traditional SaaS, you're selling a product. In AI-enabled services, you're selling yourself. As such, domain credibility isn't just important - it's existential. Early customers need to believe in your ability to deliver results, and established credibility goes a long way towards building that trust. Domain authority also unlocks access to high-quality talent channels, which enables the rapid staffing that you may need while your AI is still maturing.

That said, technical leadership is equally vital to prevent over-reliance on manual services revenue. A domain expert paired with an applied AI expert makes for the ideal founding team – balancing credibility with a relentless push for automation.

2. Beware Mirage PMF

PMF is a different beast in AI-enabled services. Strong revenue growth and NDR can mask a lack of true AI enablement. We call it "Mirage PMF".

Real PMF in AI-enabled services requires proving you can scale non-linearly relative to your costs. To get there, your AI must drive measurable improvements in cost, quality, or speed—or ideally, all three.

3. Develop partnerships early on – they can be a key growth accelerator

Partnerships with incumbents can be a major accelerant early on for AI-enabled services. Incumbents offer immediate market credibility, established distribution, and access to proprietary datasets, which can be crucial in the early days while your data corpus is small.

To take advantage of these benefits, service startups are exploring partnership models that go well beyond the traditional revenue-share approaches of SaaS. Some startups are exploring equity share relationships with incumbents, while others are fully acquiring existing service providers. Each of these approaches have pros/cons worth considering critically.

4. Leverage new pricing models – they can help unlock higher contract values

AI-enabled services come with different pricing models than classic SaaS. If used effectively, they can help you unlock higher contract values because you’re delivering both technology and labor. Two major models exist for how to price these contracts; you need to understand the benefits and risks of each:

• Labor-Based: Price based on the number of labor hours used to service the contract. This guarantees margin early on, but risks cannibalizing growth as automation increases.

• Outcome-Based: Price based on the value of final output delivered. This aligns incentives and scales margins long-term, but may hurt profitability in the early days when AI capabilities are still nascent.

We’ve found that it is generally best for AI-enabled service vendors to start with the market norm, which is typically a labor-based approach while they are learning how to efficiently deliver their service. At the same time, they should set clear timelines to transition to an outcome-based model.

Regardless of pricing model however, founders should look for opportunities to embed recurring revenue so that they can continue to monetize clients even when their initial projects are completed. Palantir is the best example of this at scale. They deeply embed themselves in initial engagements with heavily customized software and then support that software via ongoing contracts.

5. It’s the demo, stupid!

In SaaS, demos are used to showcase product value. It’s a tried and true method that works, particularly in an era where buyers are eager to see some AI magic 🪄

In AI-enabled services, the art of the demo has been forgotten. Instead, founders default to pitch decks and talk tracks. This is understandable since the AI product isn’t used directly by the customer; the service provider wields the magic internally to deliver their outcomes.

But it’s a huge mistake. Without a demo, it can be hard for a buyer to visualize what makes you different from every other service provider with a similar pitch. And if you’re a startup competing with established/trusted services brands, it’s even more critical to showcase your magic. Don’t hide it behind the curtain.

It can feel wasteful to build a customer facing demo for a product they’ll never directly use. But it’ll be the best GTM investment you’ll make. I’ve seen AI-enabled services companies halve their sales cycles just by adding in a demo.

So stop telling prospects about your AI. Show ‘em.

If you’re exploring AI-enabled services, we’d love to learn alongside you. Share your thoughts, pushback, or examples – we’re all figuring out this new model together.

Special thanks to Arjun Chopra, Medha Agarwal, Wayne Hu, James Currier, Zachary Bratun-Glennon, Wenz Xing, Nic Poulos and Kent Goldman for their great feedback and thoughts on this emerging model.

We normally reserve our newsletter for updates on Emergence and our portfolio companies, but today we have a favor to ask. We're starting a new report on the state of Go-To-Market strategy and tactics at B2B SaaS startups.

We want to equip founders and operators with research and insights that will help them make better decisions. If you’re in charge of GTM at your company, we’d appreciate if you’d share your insights with us for “Beyond Bumped Emails.”

If this isn’t your role, we’d be grateful if you would forward this email to anyone you know who handles GTM — CROs, CMOs, and early stage CEOs or VPs responsible for sales, marketing, or revenue. We’re wrapping up responses this week, so please don’t delay.

Thank you for your help, and keep an eye out for next regular issue of our newsletter, coming soon.

Wishing you and yours a wonderful holiday season,

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