Application Software: Earnings Recap
Which Software Companies Are Athletes?
Software Synthesis analyses the evolution of software companies in the age of AI - from how they're built and scaled, to how they go to market and create enduring value. You can reach me on LinkedIn and X.
Every public software CEO fielded the same set of questions about their right to exist in the age of agents. The discourse on the end of software rages on as startups substitute app software with custom tooling developed internally and labs productise aggressively in all directions.
This AI-pilled worldview would see most established enterprise software companies get wiped out and replaced by a combination of AI vendors and in-house builds.
I’m of course talking my book when I argue for the rotation of market share from incumbents to disruptors, but there’s more nuance to this than my X feed cares for.
Let’s look through recent earnings commentary from application software companies to unpack how they’re faring. I chose MNDY, HUBS, INTU, KVYO, BOX, and SAP as a broad basket covering SMB to enterprise apps.
Application Software
MNDY
FY2027 forecasts were scrapped as a result of higher uncertainty around MDNY’s historical strength in the self-serve SMB segment:
Given the evolving nature of the AI landscape and the choppiness in the no-touch demand environment, we believe it is responsible to keep our near-term communication focused on what we can execute and deliver with high confidence.
MNDY can’t reliably model its own self-serve funnel, which historically represented the majority of new customer acquisition.
The ZIRP-fuelled PLG days ended a while ago, but it’s still striking that MNDY deemed the no/low-touch channel structurally choppy.
Disaggregating the drivers of this choppiness is tricky, but commoditisation of the SMB offering by the AI labs is one plausible driver.
On the other end, $50k+ customers now represent 41% of total ARR and have 91% gross retention, whilst $100K ARR saw record net adds, and the $500K+ cohort grew 74% - the move upmarket seems to be working. It just might not be working enough to offset deceleration downmarket.
Margins came down a bit, reflecting investment in the upmarket GTM and AI, but pricing is also adapting:
So our AI capabilities are foundational in our platform. They’re embedded in our workflows. And based on the feedback we’ve gotten from customers, they really enjoy the predictability of PPU pricing, and they like to consume those capabilities that way. With that said, some of the more compute-intensive workloads that drive outputs with these workloads, we are charging and monetizing that through credits. and our customers like the mix of both of those.
The AI SKUs are: AI agents for workflow automation, Monday Vibe for application development, and Sidekick for information retrieval. Traction is still early.
HUBS
On whether HUBS’ data will be consumed by third-party agents and undermine the role of system of record, CEO Yamini Rangan’s answer focused on business logic and domain context:
Our strategy, as I just articulated, is to be that intelligent system of customer content and we have the data, but more importantly, we have the business context, the industry context and the domain context to deliver it. And that’s why customers come to us. They rely on us for that context. They want to use more of our APIs and partners want to customize and build on top of us. And as AI adoption accelerates, the value of our agentic platform increases.
Followed by (in my view, the weaker argument) determinism:
SaaS platforms are more than data. It is the logic, right? You can certainly get a nondeterministic output for a sales e-mail, but try taking a nondeterministic output for your sales forecast. That is not possible. It’s workflows like forecasting, routing, approvals, permissions, that is logic. It’s not data to be sucked away. And ownership accountability and governance, all of those lives inside applications.
Like MNDY, HUBS prices on seats and credits, with the customer support agent driving ~60% of credits consumed, with prospecting agent, data agent, and intent monitoring each contributing 10-15%.. Intriguingly, the support agent’s stated resolution rate of mid-60s is in the ballpark of private darlings like Sierra and Decagon.
Credits are not yet a material contributor to reported revenue, but the trajectory is clear: this is a FY2026 emerging tailwind that could become a FY2027 growth driver.
Management reiterated the core ICP of companies with 2 to 2,000 employees, with strong traction at the upper and multi-product penetration: deals over $5K MRR grew 33%, deals over $10K MRR grew 41%, and customers with 500+ seats grew fivefold. Multi-hub adoption among new Pro Plus customers hit 62%, with 40% of the installed Pro Plus base (by ARR) now owning 4+ hubs, up 6 points year-over-year.
As a sign of yet another AI-native buying third-party SaaS instead of building in-house, Lovable is a customer of HUBS - here’s Dharmesh Shah’s take:
So just because it’s possible. So we have, as Yamini mentioned, a large engineering team knows what they’re doing, spending 97% of their kind of calories using agent coding tools, they’re not doing it to replace internal platforms. So we think the best companies, both AI and non-AI will not be using Vibe coding to replace core systems. They’ll be doing it to add value to their customers. That’s what Lovable doing. I think that’s what some of the best companies in the world will continue to do.
HUBS is seeing the same declines in organic search as a channel as other software companies and has leaned into AEO.
INTU
Having announced apps on ChatGPT’s app store and a partnership with Anthropic to access Intuit products inside Claude, CEO Sasan Goodarzi addressed the elephant in the room:
And it’s also -- the thing I would point out is it’s why companies like OpenAI, companies like Anthropic look to the partnership with us because at the end of the day, they see and understand that this is a business that comes with a lot of liability and LLMs can’t just create the platform that we’ve created overnight.
Time will tell if relinquishing the interface to the end customer to the labs is a wise decision. The question of the value of incumbent apps where the product is primarily consumed by agents will only get louder in the future.
Taking liability is a recurring argument being touted in defence of vertical AI lately, a point INTU reiterated.
In our category, accuracy, compliance, security, reliability of financial decisions, and the liability that comes with it are critical to our customers. It’s our advantage and it’s why we win.
Management presented a narrative of AI enhancing Human Intelligence, i.e. AI-enabled services.
Our success rests on our powerful combination of proprietary data, domain-specific AI platform capabilities and AI-powered human intelligence, which we’ll refer to as HI.
Our system of intelligence combines AI and HI to deliver done-for-you experiences with accuracy, compliance, security, reliability and data privacy that create a durable competitive advantage. This foundation delivers what matters most to customers when it comes to financial insights, money management, taxes, bookkeeping and accounting, leading to complete confidence in their high stakes financial decisions.
AI-enabled services firms have raised boatloads of funding across law, insurance, accounting, banking and healthcare. The AI+HI traction INTU is seeing is a strong data point for the services-as-software thesis..
INTU’s agents are being rolled out at scale, with 3 million customers using them and repeat engagement of more than 85%.
INTU sees 3 monetisation levers: agents drive higher willingness to pay for subscriptions, agents drive cross-sell, agents drive higher services adoption (HI) - no credit-based consumption as less likely to see compute-intensive workloads relative to MDNY and HUBS.
Mailchimp continues to struggle, speaking to broader SME weakness that the MNDY no-touch performance already hinted at. INTU’s push into mid-market ERP is going much better, with new IES contracts growing nearly 50% quarter-over-quarter.
KVYO
KVYO’s business model is more naturally aligned with consumption.
Revenue scales with active consumer profiles and message/interaction volume, not with the number of humans operating the platform. When AI agents generate more campaigns, send more messages, handle more customer service conversations, and drive more transactions, Klaviyo’s revenue grows automatically because interaction volume increases.
When Marketing Agent enables a small team to run more campaigns without adding headcount, Klaviyo captures the increased message volume regardless of how many humans were involved in creation.
KVYO’s AI uptake might be one of the most underrated stories in public markets right now: for Marketing Agent adopters, more than half of campaigns are now AI-generated, performing as well as or better than manually created campaigns while taking significantly less time. Customer Agent resolution rates increased 20 points since launch, with monthly resolution volume up 50%+ since Black Friday/Cyber Monday. 85%+ all-time repeat engagement across 3 million+ customers using agents.
In keeping with this growth, the company is reframing its category - marketing automation is out, ‘autonomous B2C CRM’ is in.
Our technology marries the customer database we founded Klaviyo on and our robust marketing messaging infrastructure with agents for marketing and customer service that will autonomously create, deliver and optimize customer experiences on behalf of a business. And this agent layer that is designing and delivering experiences to billions of consumers is trained from our deep expertise and the trillions of experiences we’ve delivered for businesses already.
Enterprise traction accelerated meaningfully in FY2025. Customers with $50K+ ARR grew 37% to 3,912, with 349 net new additions in Q4 alone — beating the previous record by 25%. Customers generating $1M+ ARR doubled year-over-year. It’s no surprise KVYO hired ex-WDAY co-CEO Chano Fernandez as their new co-CEO. Another sign of the move upmarket is an Accenture partnership who are building practices around “marketing reinvention and service reinvention” powered by Klaviyo, targeting the largest consumer brands globally.
FY2026 revenue guidance of $1.501-$1.509B (21.5-22.5% growth) represents a meaningful deceleration from 32% in FY2025, but this assumes minimal contribution from AI.
KVYO’s answers on long-term durability were grounded in technical differentation rather than business context:
Our ability to be the agent and the platform of choice finds its roots in how our platform was built. This is our durable advantage. At the core is the database and data infrastructure, specifically built to handle the scale of consumer data and indexing and enriching through machine learning and serving hundreds of thousands of requests per second with millisecond latencies.
It allows Klaviyo to ingest, aggregate and govern first-party data in real time, so every consumer behavior, transaction, preference and consent is available to our users and now to our agents to deliver the best possible consumer experience. That foundation is coupled with our marketing platform, which not only provides high throughput, scalable systems to render messages, deliver them with excellent deliverability and enforce compliance, but also integrates with our customer data infrastructure to make last-mile personalization decisions on content, incentives, timing and channels at the moment we deliver a message or experience to an end consumer. Speed and scale matter.
Like INTU, KVYO supports Claude via MCP and has an app for ChatGPT, but remains confident in its infrastructure ensuring durability.
Lastly, the company is one of the best example of operating leverage and margin expansion through AI, with operating margins expansion 900bps YoY in Q4.
BOX
Aaron Levie is one of the most AI-pilled enterprise software CEOs and he deftly managed to tie file-based systems as the winning agent tool-calling paradigm to Box’s strength in enterprise content management:
Files are quite simply the native unit of work for agents. Agents use files to keep track of their work…
Thus, to have an effective AI agent strategy, companies fundamentally need a content strategy. They need a secure platform to manage critical content and ensure it can connect to all of their people, agents and applications. This is what we’re building at Box with our Intelligent Content Management Platform.
The narrative being proposed is that agent proliferation = inflection in volume of enterprise content that needs to be managed, governed, and secured. Every Claude Cowork session, every OpenClaw task, every custom agent workflow generates files that need to persist beyond the stateless agent session. The agent’s compute environment may disappear, but the documents it produced, the contracts it reviewed, the research it synthesised — all of that must be retained, governed, and discoverable.
The strategic implication is that BOX’s TAM grows with agent adoption rather than being threatened by it. More agents = more files = more content management demand. This is why Levie described Claude Cowork and OpenClaw as “universally good things” for BOX — every agentic knowledge work session creates content that needs a secure home.
BOX is seeing its AI SKUs drive enterprise penetration and expansion. Enterprise Advanced launched approximately one year ago and already accounts for 10% of Box’s revenue. The pricing uplift from Enterprise Plus to Enterprise Advanced has been 30-40% on a per-seat basis — at the high end of the 20-40% range initially anticipated. Total Suites customers now represent 66% of revenue, up from 60% a year ago.
Even so, top-line growth was still in the single-digits at 9%.
On monetisation, BOX sees themselves as serving agent demand through API consumption in a headless-first world, or more seats if human-in-the-loop use cases grow. The consumption model is still immaterial overall, but might be the future when the agent-first economy takes off.
SAP
SAP has leaned into the ‘Business Data Cloud’ with a framing that foundation models lack sufficient business context to be reliable. Citing the example of recently won customers like H&M, CEO Christian Klein emphasised SAP’s position as embedded across mission-critical workflows.
And so when I think about the future of AI and SAP, I’m super happy that I have our ERP. I’m super happy that I have our apps because without those apps, I wouldn’t have the data. And without the data, I wouldn’t have an AI.
Management also stressed their right to win against a backdrop of geopolitical turbulence:
While geopolitical and trade tensions have taken a certain toll on our top line performance in 2025, the growing need for sovereignty and resilience also offers unique opportunities for those vendors that could offer technologies and tools to reduce dependencies from dominant offering. As largest non-U.S. software, SaaS and PaaS vendor, there is no company better positioned than SAP to satisfy this rapidly growing demand.
Two-thirds of deals closed with AI in some shape or form.
Looking across MNDY, HUBS and SAP, there’s an argument for SAP’s position being strongest because of the criticality of the data they sit on: ERP, supply chain, manufacturing.
Conclusions
AI impact on revenue still early, particularly the premise of agentic consumption of APIs or credits
SMB segment showing weakness, indicating both limits of PLG and competitive threat from labs’ prosumer AI assistants
Companies are either leaning on accumulated business logic as a durable asset in a future agent economy where software is headless, or on sheer infrastructure differentiation
Enterprises still value governance and security of AI, as well as counterparties owning liabilities; will incumbents win on this vector?
Partnerships with labs via connectors and MCPs not seen as major threat to enterprise value, even if surface area of product and customer interactions shrinks; no rev-share or data being transferred. How will these lines evolve?