鈥淚 recognized both the massive potential AI brought to the tax world, as well as the risks to firms and clients by making mistakes and hallucinations,鈥� he told 附近上门 News.

The accounting industry has historically been slow to adopt new technologies. As of today, the majority of small to mid-sized accounting firms 鈥� which make up 90% of the market 鈥� remain stuck in a cycle of manual data entry.

Addressing both the opportunities 鈥� and risks 鈥� that came with advances in AI, Haase started building , a tax prep automation startup, on the side in 2023. Rather than targeting the self-prep market, like does, or the mega-enterprise firms that can afford $15,000-per-return software, Juno was built for the underserved SMB accounting firm.

鈥淲e continuously 鈥榙og fed鈥� the early Juno prototypes into the firm to see what worked best, what slowed things down, and to make it the most efficient tax preparation platform as possible,鈥� Haase said.

It took about a year and a half just to build integrations. 鈥淲e had to do a bunch of hacky things to be able to work with the existing tax software,鈥� he explained, 鈥渂ecause your typical tax software is actually around 15 to 20 years old and they don鈥檛 have public APIs.鈥�

By 2024, Juno had launched a co-pilot. Then, in July 2025, it had a tax product. The startup began onboarding other tax firms, growing to nearly 500 customers over the past year. Last year, Haase sold his accounting firm to focus on growing Juno full-time.

Today, he鈥檚 announcing that San Diego-based Juno has raised $12 million in a seed funding round led by , including participation from and .

AI to help humans 鈥榖e the advisers they were trained to be鈥�

What makes Juno different from others in the market, Haase believes, is that it operates on the premise that, at least for the foreseeable future, human tax preparers should be the ones driving the tax-return preparation process.

鈥淎 business or high-net-worth tax return requires hundreds of calculations, edge cases, deductions and more,鈥� said Haase, who holds an MBA from . 鈥淎I simply can鈥檛 do that with the 100% accuracy required not to get audited or charged with tax fraud.鈥�

Describing much of the manual work that most accountants must perform to complete returns as extremely tedious, Haase acknowledges that it鈥檚 also very easy for accountants to make mistakes that could prove very costly.

鈥淚n school, if you get a 93, an A, you get all the credits,鈥� he said. 鈥淏ut on a tax return, if you have a 99%, you fail, and your client could pay the price in penalties.鈥�

In a nutshell, Juno acts as the bridge between a client鈥檚 raw documents and the accountant鈥檚 filing software. It performs tasks like pulling data from IRS forms and even unstructured documents, such as business financial statements. Overall, it automates 90% of data entry across more than 90 document types while also flagging prior-year changes and inconsistencies for human validation.

The result is that a process that typically takes a human two to three hours is shrunk down to seven to 10 minutes, Haase estimates.

鈥淲e do 95% of a tax return in minutes, leaving the accountant to handle the strategic human decisions 鈥� the parts that actually save the client money,鈥� he said.

While he declined to reveal hard revenue figures, Haase said that in just eight months, Juno grew to mid-seven-figure annual recurring revenue.

The startup sells on a per-return basis, starting around $45, dropping to the low $30s for high-volume firms.

‘s recent move into consumer taxes and OpenAI’s hiring of a tax director show that the bigger players are eyeing the tax market. But Haase doesn鈥檛 feel threatened.

鈥淗igh-wealth individuals want assurance. If you鈥檙e paying $40,000 in taxes, you don’t want to 鈥榗ross your fingers with a chatbot,鈥� he said. 鈥淵ou want a human to talk to, someone who understands the context of your life.鈥�

Juno isn’t trying to replace accountants, he added.

鈥淚t’s trying to rescue them from the data-entry basement so they can actually be the advisers they were trained to be,鈥� Haase said.

The startup plans to roll out business returns soon, a move that Haase expects will significantly scale its customer base.

鈥楢 huge, obvious pain point鈥�

, co-founder and managing director of Bonfire Ventures, said he was drawn to invest in Juno because he believes the company is going after 鈥渁 huge, obvious pain point in a category that hasn鈥檛 been meaningfully modernized in a long time.鈥�

鈥淭he workflow pain is real, the labor dynamics make the timing right, and Dave brought exactly the kind of founder-market fit you hope to see,鈥� Andelman told 附近上门 News via email. 鈥淗e lived this problem before he built the company. That always matters.鈥�

The investor believes that tax prep is a category where trust is crucial to product success.

鈥淚f you鈥檙e going to bring AI into that workflow, it has to be transparent, auditable, and built with a human in the loop,鈥� Andelman added. 鈥淭hat鈥檚 what Juno understood early, and I think that鈥檚 a big part of why the product is resonating.鈥�

Fintech startups, particularly those that apply AI to traditionally manual or burdensome processes, have benefited from increased investment in recent quarters. Total global funding to VC-backed financial technology startups totaled $53.8 billion in 2025, per 附近上门 . That鈥檚 a more than 29% increase from 2024鈥檚 total of $41.6 billion raised.

Related 附近上门 query:

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Some are longstanding blue chip tech and pharmaceutical companies. Others are fast-growing venture-backed unicorns. And still others are more recent public market entrants looking to stay competitive in the age of AI.

To get a sense of who鈥檚 buying in bulk, we used 附近上门 data to put together a that acquired three or more seed- or venture-backed startups in the past three years. From there, we picked the most acquisitive names.

The most prolific startup acquirers of the past 3 years

Per 附近上门 data, the most prolific acquirers of seed- and venture-backed startups in recent years are 1, and . Overall, our query showed six companies with six or more known purchases, charted below.

For top-ranked Salesforce, high-volume M&A is nothing new. The San Francisco software giant has purchased at least 91 companies in the past 20 years, per 附近上门 data. Its most recent startup purchases include , a revenue orchestration platform, and , which focuses on agentic AI for e-commerce.

OpenAI, by contrast, has a shorter track record of M&A shopping sprees. The pioneering generative AI company has bought 16 companies in the past three years. Among the most recent was an deal involving open-source AI agent and its creator, . This month, it also snapped up , a creator of open source tools for software developers, and , an open-source tool for testing AI applications.

Snowflake, meanwhile, has 19 acquisitions to date. Most recently, it acquired , a developer of AI observability tools that previously raised more than $460 million in venture funding.

Notably, recent the active acquirers list for recent years looks quite a bit different that the ranking of all-time top M&A dealmakers in the 附近上门 dataset, shown below:

Highest-spending acquirers

The most prolific startup buyers also aren鈥檛 always the biggest check-writers. By the latter metric, the far-and-away leader is , and its $32 billion acquisition of .

For a broader picture view, we used 附近上门 data to put together a list of six companies that made the biggest-ticket funded startup acquisitions of the past three years.

2026 off to a promising start

So far this year, it looks like the pace of startup M&A dealmaking remains fairly robust.

This includes two deals in the multiple billions: 鈥檚 $5.15 billion purchase of and s $2.4 billion acquisition of . The AI sector鈥檚 appetite for acqui-hires and smaller purchases of earlier-stage startups also continues to boost momentum.

We鈥檒l see if it keeps up.

Related 附近上门 list:

Related reading:

• Small And Mid-Sized Startup Purchases Are Still Well Below The 2021 Peak
• 附近上门 Predicts: Why The Race For Talent And Tech Could Accelerate Startup M&A In 2026
• M&A The Highest On Record For Unicorn Exits In 2025

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Last year felt like the Year of the AI Pilot. Companies bought LLM subscriptions, managers checked on employee usage, and coffee chats abounded with the 鈥淎I wrote my memo鈥� motif.

Looking around today, there is . Add to this the recent sell-off in SaaS stocks, and the question is no longer 鈥淎re we using AI?鈥� but rather 鈥淚s this thing working?鈥�

AI is an invention that is in the process of becoming an innovation. An invention is a new capability; it is not an innovation until it has a business model. In that light, experimentation last year was the sensible move.

It is becoming clear now that the form that innovation takes will be AI systems trusted with real decisions 鈥� what Peter Drucker would call executives, and what are today referred to as agentic AI.听

As we turn to the question at hand, Is this thing working?, we can look to one of Drucker鈥檚 intellectual disciples for a framework to take us forward. Andy Grove, the legendary former CEO of , turned Drucker鈥檚 writings into a hard-nosed, pragmatic approach to managing knowledge-worker organizations. His book, 鈥淗igh Output Management,鈥� provides the classic framework for measuring the outputs of middle managers. This is not an easy thing to measure. But Grove is relentless in insisting it can and must be measured.

As we address the question of whether AI agents are delivering tangible value, we have to shift our focus away from activities, anecdotes and initiatives. These are inputs.

Grove argues that organizations must instead focus on outputs. If we try to think like Grove, we would first define the business outcome we wanted to achieve, and then measure our agentic AI only by whether this performance metric is better.

A mathematical approach

As we began working on this several years ago across our software portfolio, I had the great good fortune to meet , a mathematician who was using AI to solve real-world problems in a very similar way. He is also co-founder and CTO of 1, a decade-old, 70-plus-person team of mathematicians and engineers who have completed hundreds of projects for leading companies around the world.

His approach to these has a singular focus: improving core business metrics.

Isazi came to the same idea of measuring outputs, although starting from the field of mathematics rather than organizational behavior. The idea is to approach AI projects as though they are mathematical optimization problems: Define a target measure (such as throughput or working capital), ask what variables influence that metric, and model the mechanism by which the target measure is moved.

Then all initiatives are aligned to this target measure, and success is measured by its improvement. This aligns well with how AI models are built and improved: benchmarks and evals are always the core measure of success. Here, these evals are directly aligned to business metrics.

You must begin with the output you want to measure. And then you watch that output measurement, as a gauge, and see how long it takes until that gauge is reading changes, how much it changes, in what direction, and whether it sustains.

The time it takes to see (and sustain) a material movement is called 鈥淭ime To Production.鈥� Our theory on why so many pilots fail is that companies tend to pick an AI tool and a pilot duration and qualitatively check in with users at the end of that time.

While we at and Isazi appreciate experiments and pilots, we have found that results are best when that process is reversed. We choose the output we want to see improved, vary the AI tools until one moves the dial, and measure the time it takes to change the output positively and in a sustainable way. The shorter the Time To Production, the better.

A real-world example

Let me share an example.

One of Strattam鈥檚 portfolio companies, , is in the business of helping very large multinationals manage their global shipping. A key part of the offering is ensuring that freight bills are complete, match the contract, are approved for payment, and are properly accounted for.

Trax works across all geographies and all shipping modes, with thousands of carriers. Discrepancies between the bill and the shipper contract are common. Handling those 鈥渆xceptions鈥� at scale is a key part of the service, and historically, Trax has had a large in-house team that resolves those.

In 2024, it identified AI鈥檚 ability to resolve some of those exceptions as a key opportunity and developed the AI Audit Optimizer in-house. The output goal was clear: the fraction of exceptions resolved without human intervention.

The first quarter after its release, the Trax AI Audit Optimizer resolved some 826,000 exceptions that otherwise would have required human intervention. That was a good start, but not worth writing home about just yet.

In Q2, however, the system remained stuck at that same level, rather than improving. So Trax rapidly experimented to see what would improve outcomes. In Q3, the company discovered that a human prompt engineer interacting with the system made a big difference. As a result, in Q4, resolved exceptions tripled to 2.5 million.

Now we鈥檙e talking.

With the output gauge firmly in mind, Trax is moving forward by adjusting interaction points of the prompt engineer and the system. It used data from successful and unsuccessful resolutions to retrain the system. The company also set quarterly goals; next quarter, it will aim for the Trax AI Audit Optimizer to resolve more than any previous quarter.

This story shows how studying an output gauge allowed the company to tune and adapt the AI tooling to deliver the outcomes that actually matter. Trax is intent on fixing its customers鈥� problems so it can earn market share. Its use of AI helped it do that, and its output measurements prove the real-world value of the AI innovation.

Measure what matters

Amidst all the hype, we all care that our companies actually adapt, actually deliver customer value, and actually succeed. We know that we cannot keep doing what we are doing as we have been doing it, that our futures may well depend on our ability to adapt. But this is different from actually adapting.

To adapt successfully, resist the urge to buy tools and run pilots and tell anecdotes and report on activities. Those are just inputs. Instead, determine the outcome measurement that matters, and watch it like a hawk to see if AI is delivering cold hard business results. If it鈥檚 not, change your AI until the dial moves. Drawing on the time-tested wisdom of Drucker and Grove in this way, you鈥檒l ensure AI earns its keep at your firm.

co-founded in 2014 and is managing partner. He has served on numerous private and public technology company boards, and currently is a director of , , , , and . Previously, he was a partner and member of the investment committee at . He also worked at and . Morse serves on the board of directors of and as member of the advisory board for the HMTF Center for Private Equity Finance at . He attended , graduating summa cum laude with a B.S.E., and , where he earned his MBA and was an Arjay Miller Scholar. Morse lives in Austin.

contributed to this article. He is chief technology officer at , a Johannesburg-based applied artificial intelligence firm purpose-built to deliver production-grade AI software solutions for clients. Fanucchi has excelled academically in the fields of computer science, mathematics and physics throughout his career.

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In that moment, “you鈥檙e in a very tough spot mentally and emotionally,” Gerstenzang recalled about the experience. 鈥淭o feel taken advantage of 鈥� and then feel that the person you love isn’t being honored the way they should 鈥� it鈥檚 not a good feeling.鈥�

The experience left the serial entrepreneur so disappointed that he felt compelled to offer others in similar situations better options. So in January 2024, he teamed up with to launch New York-based , which describes itself as a “contemporary funeral home without the home.”

When a person is overcome with grief, making so many decisions related to what is often the biggest unplanned purchase of many people鈥檚 lives can be daunting. Meadow aims to make it as simple as possible by allowing families to arrange funerals over the phone or online. The startup also partners with a curated set of venues so funerals can happen, for example, at a wedding venue that鈥檚 only booked on Saturday nights or at a local chapel rather than a funeral home.

鈥淏ecause we鈥檙e software-enabled and not stuck in the way things used to be, we can offer honest pricing and unmatched hospitality,鈥� Gerstenzang told 附近上门 News in an interview.

Meadow recently raised a $9 million Series A funding round led by and following a $2 million seed round in 2024, it told 附近上门 News exclusively. Uniquely, the initial capital for both Meadow and Moxie came from the founders’ own permanent capital firm, a vehicle they use to lead their own seed rounds.

Lower costs, more software

Meadow operates by stripping away the most expensive part of the business: the real estate. By forgoing physical storefronts and using software for administrative tasks, Meadow claims it can offer dramatically lower prices.

The national median cost of a funeral with a viewing and burial in 2023 was $8,300, while the median cost of a funeral with cremation was $6,280, to the .

Meadow says that its services are significantly more affordable. A typical funeral can cost around just $1,300, according to Gerstenzang.

鈥淭here are a lot of markups on coffins [at funeral homes], because of the increased rate of cremation,鈥� he explains. 鈥淪o a lot of funeral homes really want you to do a burial. They want you to do an elaborate service because that’s how they make their money. And there’s a ton of markup embedded in that.鈥�

From fintech to funerals

Gerstenzang is no stranger to scaling complex systems. An alumnus of payments giant , where he led product teams for consumer payments, he and Gilsanz in 2022 also co-founded , which helps nurses open medspas. In founding both companies, Gerstenzang has noticed a pattern: highly regulated markets that impact millions of people but haven’t seen meaningful innovation in decades.

In the funeral industry, he saw a landscape dominated by private-equity rollups. He claims that some large corporations buy up local family funeral homes, keep the original names on the doors to build false trust, and then quietly hike prices.

Meadow鈥檚 business model seems to be resonating. The company grew its revenue 3x from 2024 to 2025 and is on track to triple it again in 2026, according to Gerstenzang. The company worked with more than 400 families in February alone, he said.

After becoming the largest independent funeral home in California, the company recently expanded into Texas and Washington, with Arizona and five other states on the horizon this year.

Today, nearly a third of Meadow鈥檚 business comes from “pre-planning” 鈥� from people who, for example, have just navigated the process of burying their own parents, and want to spare their children the same burden. It also offers both a direct cremation and a funeral, depending on a family鈥檚 wishes.

鈥淲e fundamentally care about the quality of what we do,鈥� Gerstenzang said. 鈥淲e believe we can actually increase quality as we scale because our software allows our team to spend their time working directly with customers, rather than dealing with paperwork the same way it鈥檚 been done for 50 years.鈥�

, founder and general partner at Meadow investor Haystack, noted that that his firm was also among the earliest investors in and .

Backing 鈥榖roken, unsexy鈥� industries

鈥淲e know when there’s a broken, unsexy industry that hasn’t adapted to serve the modern consumer,鈥� he wrote via email. 鈥淢eadow’s combination of software operations with unmatched hospitality is exactly what the deathcare industry needs and what families deserve.鈥�

Related reading:

• Startups Look To Streamline Tasks Around Death And End-of-Life Planning
• More Funded Startups Focus On Dying And Bereavement

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To understand what鈥檚 holding the IPO market back and what could unlock it, 附近上门 News recently spoke with , U.S. IPO services leader at , via email. He discussed how companies are rethinking IPO timing this year, how investor expectations have shifted since the 2021 boom, and why the next wave of large listings could raise the bar for smaller and mid-cap tech companies.

This interview has been edited for brevity and clarity.

附近上门 News: How are companies thinking about timing, pricing and capital needs in this uncertain market?

Bellin: The companies we work with have become significantly more sophisticated in their approach to all three dimensions, and the most important shift we’ve seen is a move away from calendar-driven thinking toward readiness-driven thinking.

On timing, companies are no longer asking “when is the window?” They’re asking, “Are we ready when the window opens?” That’s a meaningful evolution.

After years of intermittent issuance windows, late-stage companies have learned hard lessons about the cost of being caught flat-footed. The companies that priced successfully in 2025 had invested 18 to 24 months in advance in governance upgrades, financial reporting infrastructure, and refinement of their equity story.

That institutional preparation is now table stakes. As we’ve noted in our , market windows can open and close quickly, which makes continuous readiness and flexibility essential, regardless of where macro conditions stand on any given day.

On pricing, there’s been a healthy reset in expectations. The exuberance of 2021, when companies could access the market at growth multiples untethered from near-term fundamentals, is not what we’re operating in today.

Investors today are paying a premium for scaled, cash-generative stories with clear paths to profitability. That means founders and their boards have had harder conversations about the right price relative to where comparable public companies trade, rather than anchoring to the last private-round valuations.

The good news is that median pre-money valuations have begun to rise for the first time since 2021, particularly for AI-enabled businesses and later-stage companies with clear profitability trajectories. The reset isn’t a permanent discount; it’s a quality filter.

On capital needs, we’re seeing more disciplined thinking about sizing. Nearly every company going public targets a raise that covers 18 to 24 months of operations, ideally through to profitability.

What’s changed is that companies are also thinking harder about their post-IPO capital structure: How do the IPO proceeds interact with existing debt, what is the all-in cost of capital as a public company, and how does the public currency (stock) open doors for strategic M&A or talent retention?

The best-prepared companies treat the IPO not just as a fundraiser but as a balance-sheet transformation.

It feels like the IPO market is moving more slowly so far this year than expected. Why do you think that is? Do you expect it will pick up?

There are several factors at play, and it’s worth separating the structural from the situational.

On the situational side, the October-to-November 2025 government shutdown had a materially disruptive effect on the capital markets calendar that is still being felt. The SEC reported that issuers filed more than 900 registration statements during the shutdown, all of which required review and processing once operations resumed. That backlog doesn’t clear overnight.

Companies that had been in process for a Q4 2025 or early Q1 2026 launch found themselves delayed, recalibrating roadshow timing, and in some cases choosing to wait for the market to absorb other supply first. So, some of the slowness we’re seeing in early 2026 is the shadow of that disruption.

On the structural side, macro uncertainty 鈥� including tariff policy, interest rate trajectory, and geopolitical volatility 鈥� has raised the bar for when boards and investors feel confident enough to move forward. Companies are increasingly patient because they have deep pools of private capital supporting them. That optionality is valuable, but it also means that when uncertainty spikes, the default decision is to wait.

That said, we do expect the market to pick up, and we’re cautiously optimistic about the balance of the year. The underlying fundamentals for the IPO market are strong: 2025 demonstrated healthy investor appetite for high-quality offerings, traditional IPOs raised the most proceeds since 2021, and the backlog of IPO-ready companies entering 2026 is among the largest in a decade, with more than 800 unicorns that have now spent additional years strengthening their balance sheets and operating discipline.

As the clears its backlog and macro visibility improves, we expect activity to accelerate, particularly in AI infrastructure, software and specialty risk. The first few deals of any re-opening tend to be conservatively priced to rebuild confidence, and if those hold their post-IPO performance, the door widens for the cohort behind them.

What sorts of companies do you expect to hit the public market this year?

Based on where investor appetite is concentrated, we see the strongest IPO pipeline in several distinct sectors. AI infrastructure, including data centers, power capacity, and chip-adjacent services, leads the pack.

Physical AI: Investor demand for direct exposure to the physical layer of the AI economy is significant, and large-scale, capital-intensive businesses in this space have been able to command premium valuations. The 2025 AI infrastructure IPO set a powerful precedent: Institutional investors proved willing to underwrite capital-intensive, high-growth models when the contracted revenue visibility is strong.

AI-enabled software: This also continues to be a top investor preference. The key distinction from earlier software cycles is that investors are no longer willing to pay high multiples purely on growth. They want to see that AI is genuinely embedded in the product, that net dollar retention is strong, and that the path to margin expansion is credible. Platforms with high switching costs and essential utility are commanding the best multiples.

Insurance and specialty risk: This sector had a strong 2025, and that momentum is continuing into 2026. These businesses tend to offer the cash-flow predictability that institutional investors increasingly prize.

Industrials, aerospace and defense: These are also moving up the IPO pipeline, supported by reshoring policy tailwinds and supply-chain realignment.

How are these listings influencing the strategies of smaller and mid-cap tech companies?

It is real and somewhat sobering. High-profile listings serve as both a benchmark and a warning.

When a well-known, scaled company prices and trades well post-IPO, it recalibrates expectations across the sector, validating the category and giving smaller companies a comparable reference.

But it also raises the implied bar. Investors who have a scaled, cash-generative AI infrastructure company available at a $40 billion to $50 billion valuation will apply that lens to every software or infrastructure company in their pipeline.

Smaller companies are watching their larger peers closely and, in many cases, extending their private timelines. They use the interval to strengthen unit economics, hit profitability milestones, and build out the public company infrastructure (board composition, financial controls, investor relations capability) that institutional investors now expect to see in place on day one.

Given that 2026 has seen a massive surge in venture secondaries, is an IPO still the 鈥淕old Standard鈥� exit? Or is PwC seeing founders use secondaries to delay their IPO even further?

This is one of the most important structural questions in the private markets right now, and the honest answer is nuanced.

The IPO remains the aspirational end-state for most venture-backed companies. It provides the broadest access to capital, the most liquid currency for acquisitions and talent retention, and the clearest signal of institutional legitimacy. In that sense, it retains its status as the gold standard. But what has clearly changed is the sequencing and the role that secondaries play in getting there.

The secondary market has undergone a structural transformation. What was once considered a signal of distress 鈥� such as an insider selling before a company was 鈥渞eady鈥� for the public markets 鈥� has been normalized as a sophisticated liquidity tool.

As noted in our , nearly half of asset managers are already using continuation funds to unlock liquidity, and GP-led secondaries and continuation vehicles are now mainstream instruments. Secondary transaction volume surpassed $60 billion in 2025, and the market is projected to continue growing significantly in 2026. Secondaries are expected to remain the dominant exit route for private equity, with IPOs still accounting for only a limited share of total private equity exits.

For founders specifically, we see secondaries being used for several distinct and legitimate strategic purposes:

First, personal liquidity without forced exit timing. Founders who are a decade or more into building their companies have reasonable personal financial planning needs. Secondaries allow them to diversify without forcing the company into a public exit on a suboptimal timeline.

Second, employee retention. Extended hold periods have put pressure on the equity value of employees who joined years ago and expected a liquidity event. Secondary programs provide a release valve, allowing companies to retain talent they might otherwise lose.

Third, valuation discovery in a more forgiving setting. Private secondary pricing, while increasingly sophisticated, is still conducted without the full scrutiny of a public offering, allowing companies to establish a market-clearing price on their own terms.

What we caution founders about, however, is treating secondary access as a reason to indefinitely postpone the public markets journey. The median time to IPO for companies that went public in 2025 has reached over 11 years, the longest in a decade.

Extended private holding periods can be constructive, but they also delay price discovery, compress LP distributions, and ultimately reduce the competitive tension that keeps acquisition valuations high.

The IPO window is selective but open, and companies with the right fundamentals shouldn’t mistake the availability of secondary liquidity for permission to wait indefinitely.

Is PwC advising late-stage founders to prioritize GAAP profitability over top-line growth to satisfy the current 鈥渇light to quality鈥� among institutional investors?

We’re not advising founders to make a binary choice between growth and profitability, but we are advising them to have a credible, investor-grade answer to both.

The market signal from 2025 and into 2026 has been clear: Institutional investors are no longer willing to pay premium multiples on growth alone. The “Rule of 40,” the principle that a company’s revenue growth rate plus its profit margin should exceed 40%, and which may now be more a rule of 60, has re-emerged as a baseline screening metric for public market investors evaluating software and tech businesses.

Investors are paying a premium for scaled, cash-generative stories with clear paths to profitability. The emphasis is on paths.

GAAP profitability at the IPO date is not a requirement, but an articulated, credible, time-bound roadmap to it absolutely is.

What has changed is the tolerance for ambiguity. In 2021, investors were willing to fund a narrative about future profitability at an indefinite horizon.

Today, they want to see demonstrated progress in unit economics, such as improving gross margins, reducing customer acquisition costs as a percentage of revenue, and expanding net dollar retention, paired with a specific operating-leverage story. When do sales and marketing efficiency improve? When does R&D spend as a percentage of revenue compress? Where does operating margin land at scale? These are questions that founders must be able to answer with precision, not just aspiration.

The GAAP-versus-non-GAAP debate is also something we work through carefully with companies. Adjusted EBITDA and non-GAAP operating income are widely used and accepted, but institutional investors have become more sophisticated in looking through those metrics to understand certain adjustments as a real economic cost, and to evaluate true free cash flow generation.

Companies that present GAAP financials in a clear, transparent, investor-friendly way, rather than burying them under adjustments, tend to build more durable institutional credibility.

Our practical advice to late-stage founders is this: Make sure your growth spending is efficient and that every dollar of investment is generating measurably improving unit economics.

The investors we work with are sophisticated enough to reward capital-efficient growth with premium valuations and to discount growth that appears to require permanently escalating spending to sustain it.

Governance maturity, financial reporting infrastructure, and a compelling, data-supported equity story are as important to IPO success today as the top-line numbers themselves.

Related 附近上门 query:

Related reading:

• IPOs Are Holding Up In 2026, But SaaS Debuts Aren鈥檛 Happening
• 鈥榃hy Not?’ How Sales Automation Unicorn Clay Uses Tender Offers To Reward Employees Without An Exit In Sight
• The IPO Window Won鈥檛 Stay Open On Its Own

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This is a weekly feature that runs down the week鈥檚 top 10 announced funding rounds in the U.S. Check out last week鈥檚 biggest funding deal roundup here.

Busy week, big checks, lots of AI and robotics. That, in ultra-brief synopsis form, characterized the general startup fundraising environment this week. Notably, the two largest global rounds were U.K.-based and Paris-based , which raised $2 billion and $1.03 billion, respectively.

In the U.S., meanwhile, e-commerce platform , AI networking developer and industrial automation startup each picked up $500 million.

1. (tied) , $500M, e-commerce: Quince, an online fashion and home goods retailer with an affordable luxury theme, said it secured $500 million in Series E financing led by . The round sets a $10.1 billion post-money valuation for the 8-year-old, San Francisco-based company.

1. (tied) , $500M, AI infrastructure: AI networking startup Nexthop AI raised $500 million in Series B funding led by , with joining as a major investor alongside other backers. The Santa Clara, California-based company develops switching technology built on open-source operating systems for AI and cloud networking.

1. (tied) , $500M, robotics: spin-out Mind Robotics closed on a $500 million Series A round, co-led by and Andreessen Horowitz. The Palo Alto, California-based company is developing an AI-enabled industrial robotics platform, with a focus on automating industrial and manufacturing tasks at scale.

4. , $450M, robotics: Palo Alto, California-based robotics startup Rhoda AI emerged from stealth with $450 million in Series A funding reportedly led by . The startup trains robots using hundreds of millions of videos to develop intelligent models for operating in complex and changing environments.

5. , $400M, AI software creation: Replit, an agentic AI software creation platform, picked up $400 million in Series D funding at a $9 billion valuation, up from $3 billion just six months ago. led the financing for the Foster City, California-based company, joined by a long list of venture and celebrity investors.

6. (tied) , $200M, AI networking: AI startup Eridu emerged from stealth with over $200 million in a newly announced Series A round led by , , , and . Saratoga, California-based Eridu develops a high-performance network switch for AI data centers.

6. (tied) , $200M, artificial intelligence: Palo Alto, California-based Axiom Math AI, a developer of AI systems that can perform automated verification of computer code, $200 million in Series A funding at a $1.6 billion valuation. led the round, joined by , , and .

8. , $165M, robotics: Sunday, a startup planning a beta launch for a household robot called Memo later this year, raised $165 million in Series B funding. led the financing, which set a $1.15 billion valuation for the Mountain View, California-based company.

9. , $125M, cybersecurity: San Jose, California-based Kai, developer of an agentic AI cybersecurity platform, announced that it secured $125 million in funding led by .

10. , $100M, procurement: Oro Labs, developer of a procurement platform for enterprise customers, raised $100 million in Series C funding. and led the financing, which the company said follows a year of 300% revenue growth.

Global financings

The week鈥檚 largest rounds went to European startups.

, $2B, AI infrastructure: Nscale, an AI infrastructure hyperscaler, secured听 $2 billion in Series C funding. and led the financing, which set a $14.6 billion valuation for the London-based company.

, $1.03B, artificial intelligence: Advanced Machine Intelligence, a startup co-founded by computer science pioneer and former AI chief , said it has raised $1.03 billion to develop 鈥渨orld models,鈥� or AI designed to learn from and interact with the physical world. The funding for the Paris-based company represents the largest seed round ever for a European startup.

Methodology

We tracked the largest announced rounds in the 附近上门 database that were raised by U.S.-based companies for the period of March 7-13. Although most announced rounds are represented in the database, there could be a small time lag as some rounds are reported late in the week.

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One of their largest customers had asked for a specific new feature which would help their workflow, the kind of request that was clearly valuable to the customer but not necessarily important enough to jump it to the top of the roadmap.

As usually happens in enterprise software, the request needed to move through business and product discussions, design work, engineering prioritization, testing cycles and security reviews before anyone could even commit to a timeline.

The customer understood that. But after waiting for some time, it raised a different possibility: Rather than continue waiting for the vendor to ship the feature, it was considering using AI coding tools internally to build something themselves that would solve the problem well enough.

That single comment reflects a broader shift that SaaS companies are only beginning to fully absorb as their stock prices take a hit, precisely because of this sentiment.

For years, a feature request was a request to the vendor. It entered the backlog, competed with other priorities, and if the customer was important enough or the use case broad enough, eventually it would make its way into the product.

That logic is now starting to weaken. If customers can increasingly generate narrow workflows, lightweight internal tools or customized interfaces on their own, the role of the traditional feature begins to change. And once that happens, it is worth asking a deeper question: Will features even exist in the way the software industry has historically understood them?

Features were once the product

For decades, SaaS companies built value through predefined functionality. A roadmap was essentially a sequence of decisions about which features to build, which customer pain points to prioritize, and how quickly the product team could turn demand into software.

In many categories, feature depth and feature velocity became the core of competitive differentiation. The company that could ship faster, cover more use cases, and respond more effectively to customer requests often had the advantage.

That model made sense in a world where software creation was expensive, slow and highly constrained by engineering capacity. A feature had weight because it represented significant investment. It required planning, development, quality assurance, release management and support. Customers understood that process because there was no real alternative. If they needed something badly enough, they could ask for it, pay for customization, or wait.

AI-assisted development begins to change that equation. When internal teams can describe a workflow and generate a usable version of it in days rather than quarters, the meaning of a feature starts to erode 鈥斕齨ot because functionality is no longer important, but because it no longer has to arrive in the same packaged form.

In some cases, customers may not need the vendor to build every layer of functionality for them. They may only need enough access, flexibility and context to shape part of it themselves.

Functionality may become something dynamic

The real question may not be whether AI will help SaaS companies build features faster, although it clearly will. The more important question is whether the concept of a feature as a fixed unit of product development starts to fade.

For many years, teams gathered requests, translated them into product requirements, scheduled them into roadmaps, and released them as standardized functionality for a broad user base. That process may increasingly look inefficient in a world where software can be generated more dynamically.

In an AI-native environment, the customer may not ask for a feature in the traditional sense at all. They may simply describe the workflow they need, the output they want, the approvals required, the data sources involved, and the rules that should govern the process. The platform could then generate that capability inside the product environment rather than waiting for a formal release cycle. In that scenario, functionality becomes more fluid.

That would represent a meaningful shift in how enterprise software is defined. The feature would no longer be the product鈥檚 smallest strategic building block. Instead, the platform would provide an environment in which functionality can be created, modified and governed with greater flexibility.

This matters because it changes where value sits. If the workflow can be generated on demand, then the defensibility does not lie in the isolated feature itself. Rather, it lies in the system that makes that generation possible in a secure, reliable and scalable way.

The platform becomes the real moat

This is also why AI is unlikely to simply make serious SaaS platforms irrelevant. Even when a workflow can be generated quickly, it still needs to operate inside a much larger enterprise reality. It must connect to structured data, respect access controls, interact with existing systems, produce auditable outputs, comply with security policies, and function with a level of reliability that internal experiments rarely match on their own. These are not minor details. In many enterprise environments, they are the actual product.

is a strategic adviser to tech companies and investors, specializing in strategy, growth and M&A, a guest contributor to 附近上门 News, and a seasoned lecturer. Learn more about his advisory services, lectures and courses at . for further insights and discussions.

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This is a weekly feature that runs down the week鈥檚 top 10 announced funding rounds in the U.S. Check out last week鈥檚 biggest funding deal roundup here.

The first week of March was a relatively brisk period for large startup funding rounds, led by three deals of $500 million or more in the space tech and AI infrastructure sectors. In addition, we saw some good-sized deals around healthcare, neuroscience and enterprise software.

1. , $550M, space tech: Sierra Space, a space and defense tech company that designs and manufactures satellites, spacecraft and space subsystems, secured $550 million in equity funding led by . The financing sets an $8 billion valuation for the 5-year-old, Louisville, Colorado-based company.

2. (tied) , $500M, AI infrastructure: Ayar Labs, a producer of co-packaged optics for use in AI infrastructure, landed $500 million in Series E funding led by . The financing sets a $3.75 billion valuation for the 11-year-old, San Jose, California-based company.

2. (tied) , $500M, space tech: Long Beach, California-based Vast, a startup developing next-generation space stations, announced it has raised $500 million in fresh funding. The financing includes $300 million in Series A equity and $200 million in debt, with as lead investor.

4. , $250M, care platform: Findhelp, developer of a platform to coordinate care across health systems, governments, benefits providers and other entities, secured $250 million in investment from 鈥檚 . Founded in 2010, Austin-based Findhelp describes its mission as connecting people to help and support systems.

5. , $230M, neurotech: Alameda, California-based Science Corp., a biotech startup focused on brain-computer interface technologies, announced it has closed on a $230 million Series C fundraise. , , , and were among the investors participating in the syndicated round.

6. , $180M, e-commerce: Cart.com, provider of an e-commerce platform and logistics services for brands to sell across digital channels, picked up $180 million in growth equity investment. led the financing for the Houston-based company.

7. , $150M, mental health care: Grow Therapy, a New York-based platform for providing mental health care, raised $150 million in Series D funding led by and .

8. , $105M, neuroscience: Cambridge, Massachusetts-based Cognito Therapeutics, a developer of therapies for neurodegenerative diseases, secured $105 million in Series C funding. , and led the financing.

9. , $80M, engineering software: Nominal, a self-described provider of tools for engineers to test and operate critical technology, picked up $80 million in new funding. led the financing, which set a $1 billion valuation for the Austin-based company.

10. , $65M, health software: New York-based Sage, provider of a software platform for senior living and skilled nursing, raised $65 million in Series C funding led by .

Methodology

We tracked the largest announced rounds in the 附近上门 database that were raised by U.S.-based companies for the period of Feb. 28-March 6. Although most announced rounds are represented in the database, there could be a small time lag as some rounds are reported late in the week.

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If a venture-backed company has gone more than four years between funding rounds, the forecast generally looks dim. It becomes increasingly unlikely that it will secure another good-sized financing or a sizable exit.

Four-year funding gaps are especially top of mind these days, as it鈥檚 been that long since U.S. venture investment hit its all-time peak. During the boom that lasted from 2020 to early 2022, software companies in particular routinely raised megarounds at rich valuations.

That, as we know, resulted in some strong exits, a lot of mediocre outcomes, and quite a lot that haven鈥檛 flourished.

Stranded software unicorns

For many, flush times came to an abrupt end. Per 附近上门 data, more than 150 boom-era U.S. software and software-related companies with $100 million or more in equity funding have not raised capital in over four years, remain private and have not been acquired.1

Collectively, they were a well-funded bunch. Companies in the cohort that raised their last round during the peak听2 pulled in over $51 billion in aggregate funding, per 附近上门 data.

The list also contains a number of companies that were fairly high-profile startups several years ago. Examples include:

: The equity and fund management software platform raised close to $1.2 billion in total funding but hasn鈥檛 reported a new round since 2021.

: The NFT marketplace operator raised over $427 million in equity funding but closed its last round just over four years ago.

: The developer of the popular scheduling app secured $350 million in 2021 and hasn鈥檛 raised a round since. Since Calendly was mostly self-funded for its first seven years of existence, however, we鈥檇 guess it鈥檚 not a company that鈥檚 likely to be in financial distress.

Using 附近上门 data, we put together a longer sample featuring 10 companies.

Where are they now?

The ranks of companies that haven鈥檛 raised for years include a mix of those that are still active, have shuttered or are quietly winding down. For software startups in particular, many can continue eking along with a skeleton staff and a sparsely supported offering without formally shutting down. Or, they might be doing fine, given the capital they raised at the peak.

Given these are private companies, we can鈥檛 peek under the hood regarding details of their financial condition. All we can say is they haven鈥檛 disclosed a new round for some time.

Related 附近上门 query:

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• Global Venture Funding And Unicorn Creation In 2021 Shattered All Records听

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A partner at (Google Ventures), Sakach has helped lead the firm’s investments in high-profile startups such as , , , and .

Unsurprisingly, considering her involvement in so many significant fintech deals, Sakach followed a fairly traditional path into finance. She started in the technology, media and telecom investment banking group at before moving into investing roles.

Sakach began her investing career at , focusing mainly on software and fintech businesses across buyouts and minority investments. Over time, she transitioned more toward growth and venture investing, joining in 2021.

In May 2024, Sakach landed at GV, where she now focuses on growth-stage companies, and in her view, her fintech background gives her an introspective lens to examine different verticals.

鈥淎cross my investments, the common thread is solving large structural problems with technology and data advantages,鈥� she said.

附近上门 News recently spoke with Sakach to find out more about her investment thesis, her thoughts on what defines winning fintech and AI companies, how AI is affecting traditional software businesses, and how she determines what truly is a large opportunity.

This interview was edited for brevity and clarity.

附近上门 News: Do you consider yourself a fintech investor or more of a generalist?

Sakach: I consider myself an investor first. Some venture investors define themselves by sector, but I鈥檝e always wanted to be the best investor possible, regardless of category.

I鈥檝e worked across stages and strategies 鈥� from banking to buyouts to growth equity to venture.

Those experiences are interconnected. For example, banking exposes you to companies at every lifecycle stage, buyouts focus on mature businesses, and venture focuses on emerging leaders.

At GV, we invest in hyper-scaling businesses early in their lifecycle that we believe could become public companies.

What does it take to build a successful fintech company today?

I think a lot about compounding businesses 鈥� companies that naturally grow in value as customers use them over time.

The best fintech companies share several characteristics: trust-based customer relationships because once customers trust a financial platform switching becomes difficult; expansion economics because over time, companies can upsell and cross-sell additional products; and a core infrastructure role, which allows them to become embedded in essential financial workflows.

For example, compounds through customer engagement and product expansion. continues to grow as a core infrastructure provider for global payments.

Even today, modern payment service providers still handle a minority of global payment volume, which highlights how much growth opportunity remains.

How do you evaluate fintech opportunities now compared to a few years ago?

Today, companies tend to fall into two categories: very early, highly novel ideas, often AI-driven, or late-stage compounding businesses with strong retention and expansion dynamics.

Execution quality is critical. Many fintech successes come from doing the fundamentals exceptionally well.

There鈥檚 also a large opportunity in automation within financial institutions 鈥� AI-driven efficiency improvements inside banks and financial operations.

How is AI affecting traditional software businesses?

AI has reduced technology as a durable moat. Many software products can now be rebuilt quickly. As a result, defensibility is shifting toward proprietary data, distribution channels, customer relationships and talent and research capabilities.

Companies that succeed will preserve or expand their distribution advantage, rebuild their product stack for an AI-native world, and learn from proprietary usage data faster than competitors.

The dividing line is roughly pre- and post-ChatGPT. Companies built before must replatform. Companies built after must start with the right architecture.

What excites you most about AI鈥檚 long-term impact?

I think about two categories of impact: cost reduction and expansion of possibilities. The most exciting outcomes come from expanding what鈥檚 possible, not just reducing costs. AI can increase access, scale services, and grow total output. For example, healthcare automation doesn鈥檛 just reduce expenses 鈥� it enables providers to serve more patients.

I focus on opportunities that expand outcomes dramatically rather than simply making existing processes cheaper.

Are current AI valuations sustainable?

The key difference between today and 2021 is the presence of a true platform shift. In 2021, capital surged and there was no comparable technological shift. Today, AI represents a foundational technology transition. So, capital is flowing toward transformative opportunities.

Another major change is structural. Venture capital has grown dramatically as an asset class.

Large funds must deploy capital, which increases competition and deal sizes. The critical question is not valuation alone. It鈥檚 whether investors are backing category-defining opportunities.

How do you determine what qualifies as a truly large opportunity?

You cannot make a small idea large simply by investing more capital. Investors evaluate things like market scale, structural tailwinds, timing (asking 鈥渨hy now?鈥�), team capability and potential for industrywide change.

We鈥檙e looking for ideas that can reshape entire systems if they succeed. Those opportunities are relatively rare, which is why selectivity matters so much.

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