Congratulations to the enterprise AI company that just raised another nine-figure round to solve a problem software is supposed to solve by itself: getting humans to use the product.

You know the one. Won’t name it. Don’t have to. You’ve read the press release — “autonomous,” “agentic,” “category-defining” in paragraph one, and then, a few paragraphs down, where the money is actually going: hiring an army of engineers to sit inside customer offices and manually make the “autonomous” thing behave.

Nothing says “we’ve built the future of work” quite like raising half a billion dollars to hire more people to do the work.

The business model, decoded

Here’s how it works, in case the deck was too artfully vague:

1. Sell the customer an “AI platform.”
2. Discover the platform doesn’t actually do the thing on its own.
3. Send in the forward deployed engineers: sharp young people with CS degrees who now spend six months hand-building the workflow the website implied already existed.
4. Bill the customer for “software.”
5. Book it as ARR.
6. Raise a bigger round because “ARR go up.”
7. Repeat, forever, with an increasingly large and increasingly expensive engineering org standing between the product and the promise it made.

This is not a flywheel. This is a payroll with a pitch deck.

And the industry has stopped even pretending otherwise — it’s fundraising on it, loudly.

pledged valued at $10 billion, with another $4 billion from ,, , and . The vehicle, DeployCo, has been with more than $4 billion in investment.

Meanwhile, job postings for “forward deployed engineer” are up somewhere between 800% and 1,000% year over year. Lightcast data reported by from January to August 2026. data carried by put listings up more than 800% from January to September 2025, and data from 643 in April 2025 to 5,330 in April 2026. Total comp is pushing past $300,000 in the U.S. list at $280,000 to $320,000 base pay and OpenAI at $185,000 to $300,000 base, plus equity. Fortune reported some Anthropic roles reaching $400,000 in total compensation.

Somewhere, a VC is calling this “operating leverage” with a completely straight face.

It is not operating leverage. It’s a staffing agency that learned to say “agentic.”

The tell

Ask the one question the pitch deck conveniently never answers: “If you pulled the embedded engineer out tomorrow, does the customer still get what they paid for?”

If the honest answer is “well, no, but the ARR is real” — that’s not product-market fit. That’s people-market fit, and people-market fit has a brutally unglamorous ceiling: You can only hire humans so fast, and you can never hire them cheap enough to earn a software multiple. Every new logo doesn’t get more profitable to serve. It gets another headcount request.

Two companies, same $20 million ARR. One needs 10 engineers to keep the lights on. The other needs 100. Both slides say, “AI software company.” The difference shows up in gross margin, cost to onboard the next customer, and whether that cost falls as the company scales.

The bill always comes due

Here’s the thing about people-market fit: it feels great right up until someone asks the unfashionable questions: What does it actually cost, fully loaded, to onboard the next customer? Does that number go down as the company scales, or does it just grow the org chart? What happens to gross margin if the first year growth slows and the headcount can’t hide behind hypergrowth anymore?

Companies funding their FDE armies with fresh nine-figure rounds are betting nobody asks these questions until after the round closes and the confetti is swept up. That’s a fine bet in a hype cycle. It’s a terrible one in a market that’s about to start reading the fine print.

Because eventually, someone always reads the fine print. A CFO builds a cost-per-deployment model. A CIO benchmarks time-to-value against three vendors instead of trusting one demo. An investor finally does the unglamorous work of separating software ARR from human-powered ARR on the same balance sheet.

The distinction buyers and investors can test is on the numbers. Software ARR should get cheaper to deliver as it scales. Human-powered ARR does not. OpenAI guaranteed the private-equity backers of its deployment vehicle an annual 17.5% return over five years, which is a different structure from a growth thesis.Âč

So no, your AI isn’t autonomous. But your business model might be — autonomously transferring the cost of a services company onto a software valuation, for as long as the music plays.

Enjoy the round. Hope the org chart was worth it.

, the co-founder and CEO of , is an entrepreneur and product leader with more than 20 years of experience across cybersecurity and AI. He has built three generative AI companies, including one that reached unicorn status. Gurtu is known for turning advanced AI into scalable, real-world platforms, and previously contributed to successful exits, including acquisitions by , and , and a public company, . He holds a master’s degree from the and CISSP certification.

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Two decades later, Herrin is building a business around the marketing problems she encountered along the way. She started in 2025 to help consumer brands decide which products to promote and prepare their campaigns. The startup has now raised $5 million in seed funding, it tells ÂÜÀòÊÓÆ” News exclusively.

led the round, joined by and angel investors including former and current leaders.

Marklo helps consumer brands plan marketing campaigns using their sales, inventory and past marketing data. Its software shows which promotions drove sales, suggests what products to promote next and uses AI to draft campaign briefs and organize tasks.

While Marklo is still pre-revenue, more than 100 brands are already using its software in a pilot, according to Herrin. Those brands include , , , and .

The Burlingame, California-based company is now opening its software to the public ahead of the holiday shopping season. It plans to use its new funding to hire engineers, develop the product and expand customer support.

Planning campaigns

To decide what to promote next, marketers first need to understand what drove past sales. That can be a challenging endeavor when sales, inventory and marketing records all sit in separate systems.

Marklo collects that information from Shopify and services such as Klaviyo, , and , then puts past launches, promotions and sales into a calendar. Teams can see, for example, whether a rise in sales attributed to paid advertising coincided with a new product launch or a widely shared social post — factors they might miss when reviewing each service separately, Herrin said. Marklo can then draft a campaign plan and create related tasks for the team to review, edit and assign.

Using those records, along with competitors’ promotions and social media trends, Marklo suggests what a brand should market next. It also considers what the retailer has in stock and when a promotion would run.

From there, teams can use the software to plan campaigns, draft marketing materials and assign work, with suggested campaigns and reports also delivered through or email.

Herrin said the lack of broader-picture context with other AI tools frustrated her. For example, a tool might identify a Fourth of July sale as a successful promotion and suggest repeating it.

“Hey, no, I can’t. It’s not Fourth of July. We don’t have the inventory anymore,” she recalled thinking.

In one case, Herrin described how Marklo identified a color trending on social media and a matching handbag a retailer had in stock but hadn’t promoted recently. It offered to plan a post based on one that had performed well before.

Latico Leathers offers another example of how a brand uses Marklo. The company used the software to plan a month’s worth of marketing in one day, a process that had previously taken most of a week across meetings, spreadsheets and five tools, according to Herrin.

In the end, humans review and approve Marklo’s proposed work. The company says it does not use customer data to train AI models.

As for its revenue model, Marklo plans to charge a monthly subscription fee, with additional charges based on usage. For now, brands can install and try it for free.

“I’m very passionate about small and medium businesses, and I want to make it accessible to every single brand that runs on Shopify,” Herrin told ÂÜÀòÊÓÆ” News in an interview, “so that they can operate like a big brand.”

A change of direction

The decision to build Marklo followed a change in how Herrin thought about Stella & Dot’s future.

That company began as a way for women to earn money on their own schedules by hosting trunk shows in their own homes. It expanded into apparel and skincare and paid out more than $500 million to its sellers, Herrin said.

“For me, it was never about the jewelry,” she told ÂÜÀòÊÓÆ” News.

When COVID-19 put a stop to in-home gatherings, business dropped 80% almost overnight. Herrin also realized that women who might once have signed up to sell Stella & Dot products suddenly had other options. They could promote multiple brands as influencers or open their own Shopify stores.

She described Stella & Dot as “the COVID victim that had pre-existing conditions.”

Rather than rebuild the old model, Herrin sold the businesses and brands separately. Later, she bought back the Stella & Dot brand to sell through a different channel and test Marklo.

She was also reconsidering what she wanted to do next. As she approached her 50th birthday, Herrin questioned whether she would choose to start the same business again.

“As an entrepreneur, you don’t just start a business; you start a life,” she said.

She still wanted to help people build businesses. What had changed was the problem she wanted to solve: Opening an online store had become easier, but managing its marketing still meant gathering information from several systems and coordinating the next campaign.

“The mission was the same, but the solution needed to evolve,” Herrin said.

A return to venture capital

For Marklo, Herrin also returned to venture funding after relying more on profits to grow Stella & Dot.

She had raised venture capital for her first company, which became , but came away feeling that it had raised too much money. She took a different approach with Stella & Dot, building it with about $2 million in capital and a focus on profitability.

“Here’s a new growth strategy,” Herrin recalled thinking. “How about profits?”

With Marklo, she wanted to hire engineers and develop the software faster. Its 11-person team includes engineers who previously worked at Shopify and Google. She also sought investors with commerce experience.

“Raising capital isn’t just about having money for expenses,” Herrin said. “It’s about bringing a brain trust together.”

Experience as ‘the customer she’s building for’

, founder and managing partner of Forerunner Ventures, said Herrin’s experience building Stella & Dot was a central reason for backing Marklo.

“Jessica uniquely has been the customer that she’s building for right now at every stage, from starting a brand to having a brand at scale,” Green told ÂÜÀòÊÓÆ” News in an interview. That experience gives Herrin firsthand knowledge of where teams lose time, which decisions affect profitability and what brands need before trusting new technology, she added.

While AI in general makes it easier to produce marketing content, Green sees the greater opportunity in helping brands decide on what exactly to promote, as well as in understanding the impact on margins and how to grow in ways consistent with their identity. Marklo, she said, incorporates the judgment needed to turn business data into useful recommendations.

The opportunity comes as AI is taking a larger share of investment in the broader sales and marketing sector. Startups in sales, marketing and customer relationship management raised $7.5 billion across 830 rounds globally this year through mid-September, with most of that investment going to companies in ÂÜÀòÊÓƔ’s AI-related categories. Funding is on pace to finish below 2025’s $11.1 billion, while deal volume could fall for a fourth straight year — a sign that investors are concentrating their bets in fewer companies.

“It’s one thing to collect the information; it’s another thing to understand how to present it and how to recommend an action against it,” Green added.

The investor also pointed to Marklo’s ability to serve businesses at different stages, noting that coordination becomes more complex and information harder to share as brands grow. As such, the prospect of software that can grow alongside its customers was another draw.

Green said she also valued Herrin’s decision to develop Marklo alongside brands already using it, giving the company customer feedback before its broader launch.

“I like the fact that she built this product while it was being used,” she said.

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Ten years ago, a seed-stage startup needed a product, a team and a pitch deck to raise capital. Today, that’s just the start. Technology and strategy have become inseparable, each fueling the other, and the rules that once defined success have quietly shifted under everyone’s feet.

Last month, my firm reviewed more than 2,500 inbound applications. Here are the key shifts we’re seeing in the startup ecosystem at the seed stage.

Broadening capital strategy

Equity is a powerful tool for building high-growth companies. But it’s no longer the only option. Non-dilutive growth capital is increasingly playing a strategic role for companies with revenue visibility and clear ROI channels.

For example, we recently financed a company with $1 million in growth capital it needed immediately to expand its team and infrastructure. Raising that through equity alone would have likely taken months, with significant time and execution cost along the way.

We’re now writing financing checks like this on a near-weekly basis.

Distribution focused

Leading with a “better” product isn’t enough to propel growth. The breakout companies are investing in building stronger distribution systems — aka what founders refer to as “traction.” Distribution is a critical moat for early-stage startups. Rapid scaling is no longer achieved by launching new products; it’s through distribution loops.

Distribution is something startups can now architect intentionally with social platforms, marketplaces and other ecosystems. One of the most common founder mistakes we see is delaying the distribution strategy until after the product launch. At that stage, the architecture is harder to retrofit. Strong startups design distribution before they scale their product.

For example, some of the fastest-growing startups now design their products around existing ecosystems from day one — building apps that tap into merchant marketplaces, AI tools distributed through or Teams integrations, or fintech products embedded directly into banking and payroll workflows. In many cases, the distribution channel becomes more valuable than the underlying product itself.

One of the most common mistakes we see is founders postponing distribution strategy until after the product is built. By then, the architecture is far harder to retrofit. The strongest startups design distribution into the company before they scale the product itself.

Learning over speed

“Move fast” is often dolled out as the best startup advice. Operating in a fast-paced environment remains a strategic asset, but it is not enough to maintain a competitive advantage.

Everyone is fast. It’s no longer a unique attribute. Instead, learning velocity is becoming the defining advantage in early-stage startups. How quickly can you reduce uncertainty? Competitive edge is achieved not by executing blindly, but by closing knowledge gaps faster than everyone else. Execution without learning equals wasted motion.

The founder focus advantage

Last year, my team reviewed close to 25,000 applications for our investment funds and bespoke accelerators. The ones that stand out are the companies doing the fewest things exceptionally well. The most-fundable companies can describe their business in one tight sentence. They can also defend exactly what they are not doing.

Disciplined constraint is one of the highest-leverage traits in venture-backed companies. When we review applications, this pattern consistently stands out.

When a company is focused, the residuals compound: stronger early retention, faster iteration cycles, cleaner capital deployment. In a capital-selective market, focus compounds faster than ambition.

Based on tens of thousands of applicants, close to 82% of the ones that stayed in business a year later had a strong go-to-market foundation in their deck. GTM is built on agility and learning fast.

AI as infrastructure, not experimentation

There’s no lack of interest in AI. But there is an implementation problem. We’ve seen companies struggle when AI is approached as experimentation rather than architecture. Rather than bolting tools onto already fragmented stacks and workflows, designing intelligent systems should be mapped from the ground up.

More than 78% of the founders applying to today are leveraging AI in at least one way in their startup.

The most successful playbook combines execution with operational clarity and emphasizes infrastructure over experimentation. We’ve seen successful implementations that center around two practical paths. The first is validation, with rapid prototypes and identifying market signal opportunities before investing in a full build. The second is system, designing and integrating custom AI agents directly into operating workflows for revenue-generating companies facing operational complexity. Both are required to move AI agents from concept to capability. A disciplined system design often matters more than flashy tooling.

Marketing is the moat

Marketing execution is one of the largest performance gaps we see across early-stage startups. Startups lose when they don’t distribute fast enough once there is something worth selling. Marketing is the propeller for the distribution engine.

Most startups fail at marketing because it is a business function that becomes a founder hustle with support from one junior hire. But breakout growth requires process, cadence and accountability. That’s not achievable without an experienced team and clear plan.

One of the biggest mistakes founders make is treating marketing as something that starts after launch. Founders must create unique strategies, test them and then analyze what works and what doesn’t. From there, they must keep iterating and creating to unlock the most product-market fit and traction.

If we see classic strategies in a pitch deck, it is an auto-reject. And beyond being unique, your strategies must have been tested by your team.

The key is simple: Test ideas early, measure what actually works, refine aggressively and scale the strategies that compound over time.

, a serial technology entrepreneur since age 12, is now a value-driven venture capitalist with a track record of backing more than 1,000 startups across the globe. He is a co-founder and general partner at , one of the world’s most active venture capital firms. Before becoming involved in venture investing, he built and scaled into the world’s largest debate-focused social network and edtech platform, reaching millions of users and serving students across more than 500 school districts, colleges and universities.

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Startups across sales, marketing and customer management have raised $7.5 billion so far this year, according to ÂÜÀòÊÓÆ” data. The largest rounds span everything from advertising and customer data to sales software, e-commerce and customer support — reflecting just how many companies are still trying to build a better way to market and sell.

The broad trend: Investors are making far fewer bets on sales and marketing startups than immediately before and after the COVID-19 pandemic, but they’re still writing checks into the space.

Unsurprisingly, AI-focused companies are capturing a much larger share of funding than during the prior peak, with most sales, marketing and CRM investment going to companies in ÂÜÀòÊÓÆ” AI-related categories.

The numbers: So far in 2026, startups in sales, marketing and CRM have raised $7.5 billion globally across 830 funding rounds, ÂÜÀòÊÓÆ” data shows. At the current pace, funding could finish near the $9.3 billion raised in both 2023 and 2024, although potentially below last year’s $11.1 billion. Deal volume, meanwhile, is on track to fall for a fourth consecutive year — pointing to a market where investors are putting more money into fewer companies.

Funding in recent years remains far below past levels. In 2022, for example, funding in the sector topped $27 billion, and in 2021, it totaled nearly $41 billion.

Notable deals

The year’s largest funding recipient so far was, which raised more than $1 billion in a June Series E from , , and . The San Francisco-based marketing measurement company, whose products now include AI agents that analyze marketing data and automate tasks, was valued at $2.7 billion.

Restaurant financing and rewards platform announced $450 million in new capital in February. The Austin-based company did not identify a lead investor or disclose a valuation.

In January, AI-native customer service company raised a $350 million Series D led by . The Berlin-based company develops AI agents that handle customer conversations by phone and other channels. The financing tripled its valuation to $3 billion.

Meanwhile, , an online marketplace for digital products, communities and courses, received a $200 million strategic investment from in February. The deal valued the New York-based company at $1.6 billion.

Another larger deal went to Dubai-based property listings platform , which announced a $170 million equity investment in January. The company uses AI in products including home valuations and tools that help real estate agents improve and prioritize listings. led the deal, with participation from another UAE sovereign wealth fund and existing investor . The company did not disclose a valuation.

On Sept. 9,Ìę AI-powered sales automation startup announced it had raised a $115 million Series D at a $7.1 billion valuation. This was more than double the $3.1 billion valuation it achieved when it raised a $100 million Series C in August 2025. Wellington led the latest round, with participation from , , ’s a16z Perennial wealth management arm, , and others. The company says the raise followed 4x revenue growth in 2025. It also told ÂÜÀòÊÓÆ” News that it’s on track to hit $200 million in ARR this quarter, and $240 million by the end of the fiscal year.

Exits

The sector has produced one notable public offering, but most exits are coming through acquisitions as larger companies buy specialized sales and marketing products to add to their existing platforms, ÂÜÀòÊÓÆ” data shows.

, a Redwood City, California-based mobile advertising and app-marketing company, began trading on the in June. It initially sold 19 million shares at $23 each, raising $437 million. The IPO valued Liftoff at $3.83 billion, based on the outstanding shares disclosed in its IPO prospectus.

There have been a number of M&A deals this year in the sector, too, though in most cases, the acquisition price was not disclosed.

The largest known deal was Dutch payments giant acquisition of , a Berlin-based loyalty and promotions platform, in July for about $880 million. Talon had previously raised over $120 million in venture funding.

Other startup M&A deals in the marketing and sales arena in 2026 include:

• In July, acquired Seattle-based sales intelligence startup to add information about prospective buyers to its sales products.
• In June, agreed to acquire , whose software helps companies identify and contact people visiting their websites.
• Sales platform acquired , which helps sales teams identify prospective customers based on product use and other signals, in March.
• acquired the Estonian startup , whose software connects sales and marketing data, in August.
• acquired India-based marketing intelligence startup in September through a team and technology deal.

Funding is down from peak years, but it’s clear investors haven’t lost interest in sales and marketing startups. However, they are putting more money into fewer of them. Companies that help businesses find customers, increase sales, or retain existing business are still landing big checks and attracting buyers. But with acquisitions far more common than IPOs, a public-market exit remains much harder to come by.

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As a partner at Menlo Park, California-based venture firm Norwest, Jacobsohn focuses on enterprise software, drawing on his background in finance, sales and business development. His 15 active portfolio companies range from pre-revenue startups to businesses generating more than $300 million in revenue. Much of his portfolio falls within finance and HR software, although he also invests in supply chain and construction technology — often in companies building finance applications for those industries.

The common thread, he explained, is a focus on next-generation business applications taking on entrenched providers that have struggled to keep up. Jacobsohn has found particularly fertile ground in finance, where companies already have software budgets and many categories remain dominated by aging systems.

Norwest, founded in 1961, manages $15.5 billion and is investing out of its 17th fund, a $3 billion vehicle raised in 2024. Over time, the global venture and growth equity firm has backed more than 700 companies in sectors spanning enterprise, consumer and healthcare.

In an interview with ÂÜÀòÊÓÆ” News, Jacobsohn discusses where he still sees openings in the crowded market for finance software, how far companies should trust AI with accounting work, why HR startups may be better off attacking the secondary products of large platforms, and why he tests a CEO’s sales ability before investing.

The interview has been edited for clarity and brevity.

ÂÜÀòÊÓÆ” News: The office of the CFO is an area where you’ve invested fairly extensively. Why is there still so much room for startups when finance software is already such a crowded market? Where is the opportunity right now?

Jacobsohn: I’m focused a lot on companies that are disrupting legacy players, and there are a lot of legacy players in the office of the CFO. We had more than 500 companies on our Office of the CFO market map, and probably three-quarters of those are legacy players.

What’s interesting about finance is that the CFO approves all software purchases across the organization, but CFOs also buy software for themselves. There’s actually one less layer of approval when they’re buying their own software, so it is a little easier to replace it when they’re the direct buyer.

I’ve found a lot of opportunities in both finance software that sells to every industry and software focused on specific industries. I’ve invested in a lot of horizontal applications, and so far the vertical solutions have been in construction and manufacturing. We’ve also invested in the healthcare space, but that’s not my area of focus. I’m also looking at companies in transportation and logistics.

Are there specific finance workflows that still strike you as surprisingly manual and therefore more ripe for disruption?

Jacobsohn: I actually think most workflows have been automated, but some are being automated by legacy solutions. Some could still be on-premise. Some could be companies making the transition from on-premise to the cloud that are still very legacy. You might even call them SaaS 1.0, because a company can be considered legacy and be only five to 10 years old now that a lot of the new generation is AI-native.

Every company wants to buy AI-native products these days. Some legacy companies have done a better job of reinventing themselves, and others are having more difficulty. Since most everything has been automated by someone, I’m focused on new-generation disruptors of legacy solutions.

What are some of the areas you think are ripe for disruption?

Jacobsohn: I have a portfolio company in some of these categories, and not in others.

One area where I do not have a company is ERP. I think there’s a potential opportunity to disrupt and Those companies have been around for a very long time. I’m seeing more disruption downmarket, and some of these companies will eventually move upmarket.

I think sales tax is another category with some ancient legacy players where there’s an opportunity to disrupt them. Treasury management also has some very old legacy players. Another area I’ve invested in is procurement.

Finance is particularly sensitive when it comes to accuracy and audits. Is that affecting how much work companies will actually hand over to AI agents, especially in accounting?

Jacobsohn: We think about this a lot. Finance people are risk-averse, and they need consistent answers. There’s some concern that there could be errors with AI, and there are.

It’s important to infuse AI into your finance products, but you have to be careful about what you’re giving AI to do. You don’t want AI doing calculations because it is not good at math. There are certain workflows it can handle where it doesn’t produce precise numbers. But when you need precision, accuracy and calculations, you can’t rely on AI for that.

In Norwest’s recent , you mentioned that categories including payroll, benefits and workforce management can be difficult to disrupt because of the time and expense associated with switching. If a startup wants to take business from Workday or ADP, how can it make switching more enticing?

Jacobsohn: I think it would be very hard to disrupt the core products of Workday, , SAP, and Dayforce. But it’s easier to disrupt some of their secondary products, where the category isn’t their core business. Those companies have really good distribution. Often, the best distribution wins, not necessarily the best product.

Workforce management is a category I’ve invested in through . UKG has a product in the space, but it started as an on-premise company and moved to the cloud. We’ve been a cloud-native AI player, and we’ve done well against it in the market.

Another company I invested in that complements these players is , which is in the benefits space. What’s interesting to me is that I worked at WageWorks, a legacy player in the space. Elevate is disrupting my old employer. Benefits isn’t the core business of the suite players I mentioned, but it’s a big enough market where a specialist can do well.

That’s how I look at it: What are some big markets where suite players aren’t putting much effort behind the product because they can only focus on so many things at once?

Is AI making it easier or harder to build a durable software company? Features and products can be built faster, but they can also be copied faster.

Jacobsohn: I do think it’s making it easier to build companies. We’re going from products that store data and automate some workflows to really smart solutions that understand, predict and execute work for you. It’s changing employees’ jobs. Employees can focus on higher-value work and automate some of their tasks with agents that can work really quickly.

As for whether anyone can vibe-code something, I think if you’re building a simple horizontal workflow for small businesses that isn’t very complex, it could be easy to build the product yourself, or it could lead to a lot of competition.

If you’re building something complex for the midmarket or enterprise, something that needs deep domain expertise or something vertical in nature, any of those areas would be really hard for a lot of people to build internally or for too many startups to compete in. Those solutions would also be really hard to maintain. I’m not seeing much competition from people wanting to build internally at my portfolio companies that are focused upmarket, where you need deep domain expertise.

The IPO market has improved, but it certainly isn’t where it was. How does the current exit environment affect what you’re willing to fund today, if at all?

Jacobsohn: It doesn’t impact our interest in funding. Our primary entry point is seed and Series A. I’ve done some Series B and C deals, so we can be opportunistic at the later stage.

We’re focused on backing entrepreneurs with deep domain expertise who are going after big markets with legacy players ripe for disruption, and we don’t worry about the exit environment. At some point, the IPO market will open up more, and maybe that will help us in the future. But more companies get acquired than go public.

I do want to invest in a company that, if it executes well, someday has the option to go public. But I’m realistic that most companies get acquired before that can happen.

How do you feel about an acquisition as an outcome?

Jacobsohn: You have to support your entrepreneurs and what’s in their company’s best interest. M&A can be a very good outcome, especially since we come in so early. If a company is acquired for less than $1 billion, it still could be a great outcome for us and the company.

The challenge is entering late, at a valuation above $1 billion. Not many companies will acquire another company for billions of dollars. We like to come in early so that if a company sells for less than $1 billion, which is where most buyers have budgets, it can be a really good outcome.

Is there a fundamental belief you have about funding or building startups that you think other investors might disagree with?

Jacobsohn: Something that’s different about me from most VCs is that I come from a sales background, and I think the CEOs I back need to be good at sales.

Just about every CEO I back comes from a product and engineering background, but that’s not enough. You need to be good at selling. You need to sell to customers, partners, investors and employees. Before I invest, I’ll go on a lot of sales calls I set up with the CEO to see how good they are at selling.

To me, that’s a big way of assessing the potential of a company.

Have you ever passed on a CEO or startup because you felt the founder didn’t have strong sales skills?

Jacobsohn: Yes. When I go on sales calls and people aren’t interested in a second meeting, and that’s a consistent theme, it often leads me to walk away.

Tell me about your Failure Museum. What are some of the biggest findings you’ve learned in building out the Failure Museum?

Jacobsohn: I have built a that includes more than 1,500 items from failed companies and products. I have them all on my website, where I study why they failed.

People are eager to share their successes and their failures. The museum evokes more optimism than one might think. People shouldn’t be afraid to take risks. Failure can be a springboard to success.

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After pausing his entire engineering team’s workload for two weeks to ship a requested fix, he discovered the effort made no difference. The customer still threatened to churn.

“We did a retro, and wouldn’t you know? The person who’s asking for the new request was the new decision maker [we] didn’t even realize existed,” said Kataria, co-founder and CEO of San Francisco-based , in an interview. “We missed the fact that the prior person had left, and the context had shifted hands, and no one had tracked that.”

That breakdown planted the seed for Centralize, an enterprise sales platform emerging from stealth today alongside a $15 million Series A funding round led by (NEA).

The financing includes participation from ,1 , , Ritual Capital, Adverb Ventures and high-profile angel investors including former co-founder and , CEO and founder of .Ìę

Combined with a previous $4 million seed round led by Salesforce Ventures, Centralize has now raised $19 million since its 2023 inception to build what Kataria calls a “deal GPS” for enterprise revenue teams.

Engineered by Big Tech vets

Kataria and co-founder and CTO met more than a decade ago as engineering students at the .Ìę

Both went on to build high-scale products across Big Tech. Kataria served as a founding engineer on Facebook Shops during e-commerce push during COVID-19, scaling the platform from zero to a quarter billion monthly active users in a year. Wang, meanwhile, created Slack Huddles, building the initial version alongside Slack executives (CTO), (VP of product), and Butterfield (CEO), and later leading engineering and product teams at Slack.

After honing their technical chops in big tech, Kataria joined Y Combinator-backed fleet card startup as a product tech lead. It was there, while working closely with go-to-market teams to save that churning enterprise customer, that he recognized a fundamental gap in modern revenue operations.

“It was just this sea of information that no one had a handle on. The deal was at risk because the relationship is what mattered most, and we didn’t have a handle on it,” Kataria told ÂÜÀòÊÓÆ” News in an interview. “One of the things that we always say is that the one thing AI can’t commoditize is relationships.”

Solving the ‘multi-threading’ problem

Founded through Y Combinator’s Winter 2024 batch, Centralize aims to fix what Kataria describes as a lack of an actual relationship layer in modern sales platforms.

After bringing its primary product to market in December 2024, Centralize focused heavily on “multi-threading,” or the practice of identifying, engaging, and organizing all necessary stakeholders high and wide within a target company, from procurement and legal up to the C-suite.

Rather than acting as a static record, Centralize operates as a visual, multiplayer surface centered around automated org charts that function like a map. AI agents analyze first-party data, call recordings, emails, calendar events, and web sources to continuously construct a live picture of key relationships.

“It’s kind of like a deal GPS,” Kataria explained. “Or like a visual map, in which the people are the map. It’s the puzzle pieces. It’s basically like a landscape of who we know, who’s missing, how we get there, and then it’s the turn-by-turn navigation.”

Centralize’s AI assistant is named “Centra,” and answers questions such as “Who owns the budget?” or “How do we approach the CRO?” in seconds, the company claims. It also flags the moment a champion leaves, a new decision-maker joins, or engagement drops on a key deal.

Besides proactively flagging missing stakeholders, such as empty leadership seats or unengaged decision-makers, the platform also identifies warm entry points through mutual connections or past company overlaps.

Rapid growth and a bottoms-up launch

Centralize charges enterprise revenue teams based on “accounts under management” with unlimited seats, encouraging cross-functional teams, including account executives, sales development reps, and customer success managers, to collaborate on account maps in real time.

The approach is driving rapid momentum. Over the past year, revenue has expanded significantly, driven by adoption among fast-growing enterprise companies.

“Since last year, we’ve… almost 8xed the company in revenue,” Kataria said, noting that much of that momentum accelerated over recent months.

The startup’s client roster features notable tech names, including , , , , , and .

To accelerate expansion, Centralize is launching a free, single-player tier alongside its funding news. The move allows individual account executives to sign up, build real-time account maps, and introduce the platform organically to executive leadership.

, venture partner at NEA, noted that the investment in Centralize was driven by the founders’ “unique” vision and execution.Ìę

“Rachit and Will have built something rare: a product that sales teams actually want to use, not just another system of record they’re forced into,” she wrote via email. “We led Centralize’s Series A because we saw a founding team with an unusually sharp read on how AI changes the day-to-day of enterprise sales.”

Koplow-McAdams also noted that buying committees have nearly doubled in size over the last decade. “The entire revenue tech stack was built around activity capture, rather than navigating buying committees,” she added. “That’s a structural gap, and it’s only widening as AI raises the stakes.”

Startups like Centralize that bring AI to bear on enterprise marketing and sales have seen a strong uptick in funding this year, ÂÜÀòÊÓÆ” , with 2026 on pace to beat last year, which was the strongest year for venture investment into startups related to sales, marketing and CRM technology since 2022.

 

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1. Salesforce Ventures is an investor in ÂÜÀòÊÓÆ”. It has no say in our editorial process. For more, head here.↩

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Last month, of , the corporate venture arm it launched in 2016 and grew to more than $850 million across three funds. The company hired to explore selling portfolio stakes on the secondary market, putting positions in companies such as and in play. The news also arrived weeks after .

Two corporate venture programs shutting down inside six weeks invites speculation that corporations are retreating from venture capital, but in fact the opposite is true.

Measured in dollars, corporate venture has never been stronger. According to , corporate investors participated in — venture’s strongest funding year since 2021.

, , , , and all led billion-dollar rounds into AI companies last year, per ÂÜÀòÊÓÆ” data. Nvidia by itself made more than 40 startup investments and appeared in. Meta paid $14.3 billion for its stake in Scale AI. 1Ìęand s venture arm backed Anthropic’s.

Amid this strength, though, corporate venture is also quietly splitting in two, and the proof is buried inside the record numbers. Bain attributes the elevated corporate participation , and the billion-dollar rounds trace back to the same short list of names.

Take that handful out of the data and the year looks very different. Venture capital itself went through the same sorting over the past decade, as mega-funds absorbed more and more of the capital while everyone else competed for allocation, and corporate venture is now following the same script. The people with the most at stake are the smaller funds and startups downstream.

And notice that the wind-downs are coming from serious programs. PayPal’s arm ran for a decade and , and Fidelity International manages hundreds of billions of dollars. Size never protected either one, and the dividing line runs through the mandate. For Nvidia, Alphabet, Salesforce and Cisco, startup investing is a core strategy, funded off enormous balance sheets, because their businesses depend on owning a position in the technology cycle. Nvidia backs the companies that build on its chips, and that commitment survives budget season. For most other corporations, venture is one strategic priority among several, competing for capital with the core business itself.

To be clear, there’s nothing wrong with that. When a new chief executive commits to finding , winding down even a well-run program can be the disciplined call, and disciplined capital allocation is what shareholders ask of public companies. Corporate venture has always moved in cycles, and the waves of closures after 2000 and 2008 said far more about parent balance sheets than about the returns on offer. Individual programs are mortal, but the asset class keeps growing.

When I started my career, technology drove roughly 2% of the American economy, and today it drives a double-digit share of GDP and nearly 40% of the stock market.

Who feels it first

For smaller funds and their portfolio companies, the split is already changing the math. ‘s finds corporate funds pursuing fewer, more targeted deals, and the share using the secondary market jumped from 15% in 2024 to 22% in 2025; PayPal’s Jefferies mandate takes that same path at the scale of an entire program.

When a corporate arm winds down mid-life, its portfolio companies lose a strategic backer and a source of follow-on capital at once, the smaller funds that syndicated alongside it lose their anchor for the next round, and a secondary sale replaces a committed partner with a financial buyer.

I spend my days working with early-stage venture funds, and I’m watching this pattern develop in real time: strong companies outside AI, with a departing corporate backer on the cap table, heading into rounds their existing syndicate can’t fill alone.

The lesson for startup management teams and VC fund managers is to plan for corporate capital to come and go. The pro rata rights that funds hold in their best companies become most valuable at exactly these moments, when a strategic investor steps back and ownership in a breakout company becomes available to whoever can fund it.

Smaller funds should line up committed follow-on capacity before their winners come back to market, so a corporate partner’s exit becomes a chance to buy more of a company they already know well. Founders should run the same exercise from the other side of the table and know today which investors on their cap table can carry the next round.

Corporate venture will keep growing because the forces behind it keep growing, and programs will open and close along the way, as they always have. What’s changed is the sorting: permanent capital consolidating at the top of the market, and everyone else learning to plan around that fact. The funds and founders who prepare for it will come out the other side owning more of the companies that matter.

is the founder and managing partner of , a growth-equity firm that co-invests in venture-backed companies by leveraging the unused pro-rata rights of more than 1,000 early-stage VC partners.

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But then you ask the only question that really matters: “Why are the numbers improving?”

That is where the actual strategic discussion begins.

Was growth improving because the company found a repeatable sales motion, or because it offered large discounts? Was retention strong because the product became deeply embedded in customer workflows, or because renewals had not yet come under pressure? Was gross margin structurally strong, or were infrastructure costs simply being pushed into the future?

Metrics and KPIs are useful. They give us a snapshot of the business. But they do not shine a light on strategy. They are the result of strategy — or sometimes the result of a lack of it.

Here are three areas where founders and boards should look deeper into unit economics and the strategies behind them.

LTV/CAC: Look at the quality of acquisition

LTV/CAC is one of the most important SaaS metrics. A strong ratio usually suggests the company can acquire customers efficiently and retain them profitably. But two companies can both report a 4x LTV/CAC ratio and still be very different businesses.

One may reach that ratio because it has strong positioning, low acquisition costs through partner programs, viral marketing, high retention through workflow integrations, and expansion revenue from additional products or services. Another may reach the same reported ratio because it charges higher upfront prices, assumes a longer customer lifetime, or has not yet seen churn show up in the data. On paper, both look efficient. In practice, one may have a healthy acquisition engine while the other may be relying on assumptions that still need to be proven.

When reviewing LTV/CAC, boards should ask:

• Is the company clearly positioned?
• Is it focused on the right customer segment?
• Are customers coming from scalable channels or expensive paid acquisition?
• Is pricing strong enough to justify the sales effort?
• Do we have cross-sell and upsell opportunities baked into the offering?
• Is the payback period reasonable?

A weak LTV/CAC ratio is not always a sales problem. Sometimes it is a positioning problem, a pricing problem or a market-selection problem.

GRR and NRR: Understand why customers stay

GRR and NRR are critical because they show whether customer revenue stays and expands. But they do not explain why customers stay or expand. Strong dollar retention usually comes from becoming embedded in the customer’s workflow.

The product delivers fast time-to-value, integrates with important systems, becomes part of a daily process, and becomes difficult to replace.

That is when expansion becomes easier. More seats, more usage, more modules, more geographies, more products. This is why setting a board goal to “increase NRR” is not enough. The real discussion should be around onboarding, integrations, product depth, customer success, pricing tiers and expansion paths.

Dollar retention improves when the product becomes more valuable, more embedded and more scalable within each customer.

Rule of 40 and Rule of 4: Check the quality of growth

ARR growth matters, but the board should ask what kind of growth it is. The Rule of 40 shows whether the company is balancing growth and profitability.

But a better number can come from real efficiency, or from cutting too deeply into product, customer success and future growth. The Rule of 4 adds a simple durability check: ARR growth divided by annual customer churn should be above four. If it is low, growth may be hiding a leaking bucket.

So the board should ask two questions:

Are we becoming more efficient, or simply underinvesting?

Are we growing on top of a loyal customer base, or replacing customers we should have kept?

Let’s use these metrics to dive deeper into the core long-term strategy.

is a strategic adviser to tech companies, investors, CEOs and boards, specializing in strategy, growth and M&A. He is a guest contributor to ÂÜÀòÊÓÆ” News and a university lecturer on strategy, finance and entrepreneurship. Learn more at and connect with him on .

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

This week, most of the largest U.S. startup funding rounds centered around the sector one would suspect: artificial intelligence. This was true for the week’s largest venture financing, a $1.5 billion Series F for AI inference technology provider , as well as a majority of rounds in the Top 10. Beyond that, the next-biggest area for startup funding was biotech.

1. , $1.5B, AI inference technology: Baseten, a provider of systems software to run AI applications workloads, raised $1.5 billion in Series F funding, its fourth fundraise in 18 months. , , , and co-led the round, which set a $13 billion valuation for the San Francisco-based company.

2. , $1B, digital marketing: AppsFlyer, a San Francisco-based provider of data analytics with digital marketing as a core use case, reportedly secured more than $1 billion in a Series E funding round at a post-money valuation of $2.7 billion. Backers reportedly include , , and .

3. , $650M, AI inference technology: San Francisco-based Groq closed on $650 million in new funding led by and that it says will be used to scale its AI inference cloud technology and infrastructure. The investment comes just over six months after an acquihire-type transaction in which hired away its founder and key team members and licensed its technology.

4. , $330M, ophthalmic therapies: Ollin Biosciences, a developer of therapies for vision-threatening diseases, picked up $330 million in Series B funding. and led the financing for the Austin-based company.

5. , $320M, foundational AI: General Intuition, developer of a foundational AI model based on gameplay, secured $320 million in Series A funding at a $2.3 billion valuation. led the financing for the New York-based company, while backers including and participated.

6. , $250M, government software: Peregrine Technologies, provider of a platform used by public safety agencies and other government entities, secured $250 million in Series D financing. , , , , and led the financing, which set a $6.8 billion valuation for the San Francisco-based company.

7. (tied) , $200M, risk intelligence: Palo Alto, California-based Quantifind, developer of a risk intelligence platform for financial crime detection and national security operations, closed on $200 million in growth financing led by .

7. (tied) , $200M, foundational AI: San Francisco-based Mirendil, a frontier lab building systems that excel at AI R&D, says it raised a seed round of $200 million led by and . The startup also counts as a backer.

9. (tied) , $190M, AI infrastructure: AI networking infrastructure startup Upscale AI raised $190 million in Series A extension funding, bringing total financing to $500 million. led the round, which set a $2 billion valuation for the Santa Clara, California-based company.

9. (tied) , $190M, biotech: San Francisco-based Osanni Bio, a therapeutics platform focused on ophthalmic therapies and other treatments, secured $190 million in Series B funding led by .

Large non-US deals:

The week also brought some large ÂÜÀòÊÓÆ” rounds:

, $569M, defense tech: Berlin-based defense tech startup Stark reportedly raised $569 million in a financing led by and .

, $546M, insurance: Paris-based health insurance startup Alan secured $460 million in new investment in primary and secondary equity led by .

Methodology

We tracked the largest announced rounds in the ÂÜÀòÊÓÆ” database that were raised by U.S.-based companies for the period of June 18-26. 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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The company is a marketing analytics platform that acts as an independent referee of sorts to track which digital ads actually drive mobile app downloads and in-app purchases. It helps companies measure their return on ad spend while claiming to protect user privacy and block ad fraud.

While AppsFlyer CEO and co-founder declined to comment on specific deal details, he did confirm to Axios that , , and each took a minority stake in the San Francisco-based startup.

AppsFlyer’s most recent raise before this was in 2020. With the latest round, the company has now raised $1.3 billion in known funding since its 2011 inception, per .

Previous backers include , 1, , and .

“They believe what we believe: that attribution and measurement must be independent, unbiased and trusted,” Kaniel was quoted as saying of AppsFlyer’s newest investors. “As AI takes over more of how advertising gets bought and optimized, the signals feeding those systems become the most consequential infrastructure in the industry.”

He added that the company is eyeing the public markets, calling the financing “a step on that path.”

So far in 2026, companies in sales, marketing and CRM categories have pulled in around $4.1 billion globally in seed- through growth-stage funding, per ÂÜÀòÊÓÆ” . That puts the space on track to come in roughly flat with or a bit up from the prior three years — when annual funding hovering around the $8 billion mark — though still far below boom-era levels, when sales and marketing investment topped $20 billion. Notably, many of the startups funded in recent quarters have been AI-focused, with many of them offering agentic tools and automation in areas such as sales, marketing and customer experience management.

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