Since its inception in 2018, Agile Robots, a robotics startup based in Germany, has expanded rapidly, acquiring over a dozen companies. Based in Munich, the firm is placing its bets on a blend of manufacturing know-how and advancements in artificial intelligence to boost both revenue and profitability.

Last year, Agile Robots recorded a revenue of 300 million euros, about $345.9 million. The company expects to see that figure double this year, which could pave the way for profitability within the next two to three years, according to Chief Executive Zhaopeng Chen in a recent interview. 

Chen emphasized that these targets are realistic, stating, “If you look at the numbers, it’s actually not ambitious.” He also noted that Agile has secured customer contracts that support this year’s revenue forecasts. Additionally, the company has raised approximately $1.5 billion from investors, with SoftBank Group at the forefront.

Although Agile's humanoid robots are currently utilized in pilot programs, Chen remains optimistic about the potential for broader applications in the robotics market. He anticipates that future AI systems will be capable of comprehending and engaging with complex environments effectively.

In the long run, he envisions robots evolving into consumer items, similar to how smartphones and cars are integrated into daily life.

German robotics company Agile Robots poised to double its revenue this year.

A modern German factory bathed in warm natural sunlight, where two sleek Agile Robots robotic arms ‘clink’ precision parts like a celebratory toast. The large Agile Robots logo is emblazoned on a central control console. Above, a German flag hangs proudly. Behind them, a glass whiteboard shows hand-drawn revenue graphs shooting upward, capturing the sense of doubling growth.

German Robotics Startup Agile Robots Set to Double Revenue This Year - WSJ

- Agile Robots aims to leverage manufacturing expertise for increased revenue and profits.  
- The company forecasts doubling last year's revenue of 300 million euros this year.  
- CEO Chen anticipates robots will eventually become consumer products in diverse markets.

Since it was founded in 2018, Agile Robots has acquired more than a dozen companies. Sean Gallup / Getty Images

MUNICH—German robotics startup Agile Robots is betting that its manufacturing expertise, not just advances in artificial intelligence, will lead to a surge in revenue and produce profits.

The company’s revenue last year was 300 million euros, equivalent to $345.9 million, and is set to double this year, helping it turn profitable in two to three years, Chief Executive Zhaopeng Chen said in an interview.

“If you look at the numbers, it’s actually not ambitious,” Chen said.

He added that Agile has signed customer contracts underpinning this year’s projected revenue and has so far raised about $1.5 billion from investors led by SoftBank Group 9984 1.10 %increase; up pointing triangle .

Although its humanoid robot have only been deployed in pilot projects, Chen believes today’s industrial business is laying the foundation for a broader robotics market. He expects future physical AI systems to be able to understand and interact with complex environments.

Eventually, he said, robots will become consumer products, occupying a place somewhere between smartphones and automobiles.

German Robotics Startup Agile Robots Set to Double Revenue This Year

Sapien, a two-year-old artificial intelligence startup, has successfully secured a fresh round of funding, spearheaded by Ali Partovi from Neo, and is now valued at $180 million. The company has evolved from simply offering financial planning software to developing a more extensive platform that analyzes the impact of operational decisions on a business’s financial performance. Notable clients include Bayer, Carlex, Cooper Standard, Blink Charging, and Westgate Resorts.

The changing nature of Sapien's platform use is highlighted by a recent case study on automotive supplier Carlex. In this case, Sapien enhanced an existing profitability analysis, revealing that factors previously believed to contribute positively to the company’s EBITDA by $10 million were actually leading to a $2 million decline. Additionally, the platform uncovered a previously unnoticed opportunity worth $1.5 million through patterns in customer channels, all accomplished in approximately 20 minutes.

Jason Waltz, the VP of finance for Carlex's Aftermarket Division, remarked on the efficiency of this analysis, noting that a process that could have taken his team two weeks was completed in a fraction of the time with Sapien’s technology.

Founded in 2024 by Ron Nachum, Pranav Ravella, and Arya Grayeli, Sapien initially centered on financial planning and analysis. However, the company's vision has expanded to include a focus on understanding how operational choices influence financial results. Nachum describes Sapien as an integrated system that links financial data from a company’s statements with the underlying business activities, enabling finance and operations teams to delve into the factors affecting revenue, margins, and cash flow.

This new approach goes beyond merely providing dashboards or automating reports; Sapien aims to leverage AI to perform the investigative tasks that finance and operations teams typically manage. Carlex has since broadened its use of the platform to areas such as pricing, inventory management, customer orders, and supply chain operations. Other firms like Cooper Standard and Blink Charging have also reported similar efficiencies, with analyses being streamlined from hours or days down to mere minutes. 

Nachum acknowledges that a significant hurdle for AI adoption in financial sectors is not the capability of the technology itself, but rather building trust in its outputs. He notes that, while large companies generate substantial data, they often struggle with effective analysis. Establishing trust in the results is essential, and once finance teams feel secure using the platform for their analysis, its use can expand to other functional areas such as sales and accounting.

This evolution signifies Sapien's ambition to connect financial outcomes with the operational decisions that create them. Nachum is committed to steering clear of creating an "Excel copilot" and instead aspires to design a system that can analyze business operations, identify valuable trends, and assist teams in transforming these insights into replicable processes.

The overarching vision for Sapien is to harness AI not merely for accelerating existing financial workflows but to discover operational strategies that can dramatically enhance a company's financial performance.

In leadership changes, Jennifer Fulk has recently been appointed CFO of Kura Oncology, effective September 8. Fulk comes with over 15 years of experience from Eli Lilly and has held various senior financial roles in different organizations. Meanwhile, Mira Mircheva has opted to step down as EVP and CFO of Bally's Corporation effective September 4, with George Papanier stepping in as interim CFO while the search for a permanent replacement begins.

In employment updates, U.S. employers added 162,000 jobs in August, a significant rise compared to an average of 31,000 monthly over the previous year, while the unemployment rate remained steady at 4.1%. Job growth was particularly strong in the restaurant and bar sector, which contributed 59,000 new jobs, while local government education added 42,000 positions. However, the information sector saw a decline, losing 23,000 jobs, primarily due to ongoing cuts in tech-related industries. Wage growth remained steady at 3.1% year-over-year, with average hourly earnings reaching $37.75, and the Bureau of Labor Statistics revised job numbers for June and July upward by 55,000.

As the market anticipates a potential Federal Reserve rate hike, current employment trends may influence these expectations.

AI startup Sapien secures funding at a $180M valuation to assist businesses in identifying the true drivers of profitability.

A bright, sunlit modern boardroom where executives in smart business attire study a large transparent touchscreen display filled with rising profit graphs and glowing data nodes. A sleek golden neural-network sphere labeled “Insight” floats at the center, with the bold Sapien logo prominently embossed on the screen. Through floor-to-ceiling windows, a vibrant cityscape basks in morning light, hinting at new growth.

Exclusive: AI startup Sapien raises at $180M valuation to help companies find what’s really driving profit - Fortune

- Sapien raised funding at a $180 million valuation, expanding beyond financial planning software.  
- Analysis previously taking weeks is now done in minutes, revealing significant financial opportunities.  
- The company emphasizes building trust in AI for finance to enhance operational decision-making.  

Good morning. Sapien has raised a new funding round led by Neo’s Ali Partovi at a $180 million valuation, as the two-year-old AI startup pushes beyond financial planning software into a broader system for analyzing how operational decisions affect a company’s bottom line. Its customers now include Bayer, Carlex, Cooper Standard, Blink Charging and Westgate Resorts.

The shift is reflected in how customers are using the platform. According to a forthcoming case study I was able to review on automotive supplier Carlex, Sapien rebuilt an existing profitability analysis and found that factors the company had identified as contributing $10 million in positive EBITDA were actually producing a $2 million drag.

Sapien later found another $1.5 million opportunity in a customer-channel pattern the team had not been looking for. The analysis took about 20 minutes.

“It took 20 minutes,” said Jason Waltz, business unit VP of finance for Carlex’s Aftermarket Division. “It would have probably taken us two weeks, and we probably wouldn’t have gotten to that level.”

That kind of analysis is increasingly the focus for Sapien, which founders Ron Nachum, Pranav Ravella, and Arya Grayeli launched in 2024 with an $8.7 million seed round led by General Catalyst.

The company has also grown quickly, with headcount increasing fivefold over the past year, bringing together AI researchers and engineers from Meta, Google, and Palantir with executives and operators from McKinsey & Company, Blackstone, Barclays, and Plaid.

Nachum, Ravella, and Grayeli met in high school before attending Harvard, Stanford, and the University of Texas at Austin, respectively.

From FP&A to operating decisions

When I first spoke with Nachum, who serves as CEO, two years ago, Sapien was focused on FP&A. The company has since moved toward a broader question: not just what happened to a company’s financial results, but what operational decisions caused them.

Nachum describes Sapien as a financial and operational system that connects the numbers in a company’s financial statements with the underlying business. The idea is to help finance and operating teams investigate changes in revenue, margins and cash flow, identify the drivers behind them and then act on those findings.

That is a different proposition from adding another dashboard or automating another report. Sapien is betting that AI can do more of the investigative work traditionally handled by finance and operating teams.

Carlex has since expanded its use of Sapien into pricing, inventory, customer orders, OEM quoting, supply chain and operations, Nachum said.

Cooper Standard and Blink Charging are using the platform in similar ways, according to the case studies, with analyses that previously took hours or days being reduced to minutes and teams using the results to identify potential savings and margin improvements.

The trust factor

For Nachum, the biggest obstacle to AI adoption in finance isn’t necessarily whether the technology can produce an answer.

“Every large company is data rich and analysis poor,” he said. The harder part, he argues, is getting finance teams to trust the answer—whether the underlying numbers are correct, the analysis is reliable and the company’s data remains secure.

That makes trust an important part of Sapien’s approach. Once teams become comfortable using the platform for financial analysis, Nachum said, its use can spread into functions such as supply chain, sales, and accounting.

The expansion also reflects Sapien’s broader shift away from being an FP&A tool. Rather than treating finance as a standalone function, the company is trying to connect financial outcomes to the operational decisions that produce them.

One of Nachum’s core bets has been to avoid turning the company into what he calls an “Excel copilot.” The goal, instead, is to build a system that can investigate a business, find important patterns and eventually help teams turn those findings into repeatable processes.

For Sapien, that is ultimately the bigger opportunity: using AI not simply to make existing financial work faster, but to find the operational decisions that can materially change a company’s financial performance.

Sheryl Estrada

Sheryl.Estrada@fortune.com

Leaderboard

Jennifer Fulk was appointed CFO of Kura Oncology, Inc. (Nasdaq: KURA), a biopharmaceutical company, effective Sept. 8. For more than 15 years at Eli Lilly and Company, she held increasingly senior roles spanning commercial finance, research and development, investor relations, treasury, and global operations. Her positions included CFO of Lilly's U.S. Bio-Medicines business. Fulk has also served in a series of senior finance and operating leadership roles, including as CFO of Talkspace, chief operating officer and CFO of 120Water, and, most recently, CFO of Soleno Therapeutics.

Mira Mircheva has resigned from her role as EVP and CFO at Bally's Corporation, effective Sept. 4, due to personal reasons, according to an SEC filing. Her last day will be Sept. 30. George Papanier, Bally's president, will serve as interim CFO. The company is searching for a permanent CFO.

Big Deal

U.S. employers added 162,000 jobs in August, the Bureau of Labor Statistics reported, beating the average monthly pace of just 31,000 over the prior year, while unemployment held steady at 4.1%.

Restaurants and bars led hiring with 59,000 new jobs, and local government education added 42,000, largely reversing a dip the month before. Manufacturing extended its climb, up 16,000 and now 58,000 above its December low.

The information sector was the exception, shedding 23,000 jobs as tech infrastructure, data processing, publishing, and broadcasting firms kept cutting. Wage growth held at 3.1% year-over-year, with average hourly earnings reaching $37.75, and the BLS revised June and July's job counts up by a combined 55,000.

Going deeper

"As Wall Street shifts expectations towards a Fed rate hike, the White House turns up the pressure on Warsh’s central bank" is a Fortune article by Eleanor Pringle.

Surprisingly healthy employment data has tipped expectations for a rate hike at the Federal Open Market Committee’s meeting higher this week, with interest rate traders now placing the likelihood at 58.4%, Pringle writes. Read more here.

Overheard

"In a world of infinite information, trust is the scarcest resource."

—Nirav Tolia, CEO, president, chair, and co-founder of Nextdoor, writes in a Fortune opinion piece.

AI startup Sapien raises at $180M valuation to help companies find what’s really driving profit

In the dynamic landscape of Silicon Valley, there’s little that excites investors more than an impressive growth in annual recurring revenue (ARR). However, amid the ongoing AI surge, venture capitalists are starting to raise questions about the true meaning of this figure.

Greg Isenberg, CEO of Late Checkout, sparked discussion with a candid remark that went viral, revealing concerns about the misinformation surrounding ARR from VC-backed AI firms. He noted, "The amount of VC-backed AI companies lying about their ARR publicly is absolutely unsettling," highlighting a sentiment that has been commonly speculated but seldom articulated in tech discussions.

Originally popularized during the software boom, ARR was meant to provide a snapshot of a company's expected yearly earnings from customers who pay regularly, serving as a quick reference for investors evaluating a startup's worth. However, insiders who spoke to Business Insider suggest that the term has become increasingly ambiguous, conflating various forms of revenue such as subscribed services, future contracts, hardware sales, and even one-off revenue spikes.

In essence, true recurring revenue is often misrepresented. Shruti Gandhi, a general partner at Array Ventures, referred to the trend as "ARR inflation," expressing that there currently exists a lack of accountability. She has pointed out instances of founders inflating their figures, with the common rebuttal being that such behavior is ubiquitous.

Alexander Niehenke, a partner at Scale Venture Partners, notes that this trend indicates a psychology reminiscent of the peak of the 2021 market, where risk tolerance among investors was elevated. "It feels like we're at the tail end of 2021 again, given some of the behavior I'm seeing in the venture ecosystem," he remarked.

Founders are well aware of the enthusiasm VCs have for ARR. Niehenke compares their tactics to "catnip," exploiting investor excitement for their own advantage. This behavior was exemplified when Roy Lee, CEO of Cluely, admitted to inaccurately reporting his company’s ARR to a TechCrunch journalist—a rare moment of honesty in an otherwise unregulated environment for startups, where revenue claims often go unchecked.

In the wake of these revelations, more startups are turning to run rate as a metric, which extrapolates a single month’s revenue to project annual earnings without implying that the revenue is consistent. This approach emerged as the nature of AI revenue became less predictable. Unlike traditional software-as-a-service (SaaS), where predictable contracts ensure stable ARR, many AI companies base their pricing on variable usage—commonly calculated in tokens—which leads to significant fluctuations in both revenue and costs.

Pocket, known for its AI recording device, recently announced it surpassed a $100 million run rate. Founder Akshay Narisetti prefers this metric since it reflects the unpredictable nature of token-based revenues. He emphasizes avoiding the pitfalls of annualizing revenue spikes from singular high-performing months.

Despite the issues surrounding run rate, it offers a clearer alternative during these uncertain times. For example, major AI entities like OpenAI refer to their subscription sales as ARR but label their new advertising revenue as an annualized run rate. Meanwhile, Anthropic has generally categorized its sales under the run-rate banner.

In contrast, Linear—a unique SaaS startup—recently affirmed its achievement of $100 million in ARR based on the stability of its revenue model, which includes multiyear contracts. Cofounder and CEO Karri Saarinen defended the use of ARR by pointing to Linear's longevity and reliable forecasting capabilities, though he acknowledged that this is not the case for many newer AI-focused startups, which often find themselves in an increasingly foggy revenue landscape.

VCs Scrutinize AI Firms' High Revenue Assertions

Dark boardroom scene under a single tungsten spotlight: a circle of sharply dressed venture capitalists lean over a sleek glass table strewn with printed charts and spreadsheets. One holds a magnifying glass over a giant, fractured revenue graph glowing in warm amber—its bars bulging then cracking. Behind them, a shadowy silhouette of a generic “AI” emblem flickers on the wall. Tense postures, furrowed brows and long shadows convey deep skepticism.

'There's no accountability;' AI startups keep claiming huge revenue. VCs say the math is getting 'murky.' - businessinsider.com

- Venture capitalists are increasingly skeptical of AI companies' claims about annual recurring revenue.  
- Many startups exaggerate revenue metrics, leading to what is termed "ARR inflation."  
- AI's unpredictable revenue model makes traditional ARR less reliable than in software companies.

Few metrics thrill Silicon Valley investors more than surging annual recurring revenue (ARR). In the AI boom, however, VCs are increasingly skeptical of what that number actually represents.

"The amount of VC-backed AI companies lying about their ARR publicly is absolutely unsettling," Greg Isenberg, CEO of Late Checkout, wrote in a post that went viral for saying something out loud that has been whispered about, but rarely said publicly in tech circles.

ARR was a metric that came into vogue during the software era, intended to estimate the yearly sales a company can expect from paying customers, a quick way for investors to judge a startup's valuation.

Now, venture capitalists and founders who spoke with Business Insider said ARR has become so loosely used that it can blur the line between contracted subscriptions, future deals, token consumption, hardware sales, and a single strong month of revenue.

In short, recurring revenue is often not recurring.

"It's ARR inflation," said Shruti Gandhi, a general partner at Array Ventures. "There's no accountability right now."

Gandhi says she has called out founders for exaggerating revenue. Their defense: "Everybody else is doing it."

To Alexander Niehenke, a partner at Scale Venture Partners, the current trend of padding ARR reflects a top-of-the-bubble mentality when founders have all the leverage.

"It feels like we're at the tail end of 2021 again, given some of the behavior I'm seeing in the venture ecosystem," he said. "Anytime you get deeper into a bull cycle, everybody has more tolerance for risk."

Explore BI Games Take a smarter break in your day - and see how far you get. Play now Want more Business Insider in your news feed? Add BI in Google so our reporting is easier to find when you’re searching for what matters. Add to Preferred Sources

Founders know how excited VCs get about ARR, according to Niehenke. "The smart and brilliant entrepreneurs use it like catnip on us," he said.

Cluely CEO Roy Lee raised eyebrows earlier this year when he admitted to misstating his startup's ARR to a TechCrunch reporter. But that was very much the exception. With startups under much less regulatory scrutiny than public companies, revenue claims are rarely verified.

it’s genuinely crazy tech has so isolated itself from regulatory forces that you can just admit to securities violations on the internet — Will Manidis (@WillManidis) March 5, 2026

More startups are using run rate, but is it better?

Before AI, Silicon Valley was in love with software-as-a-service companies, where ARR was much more dependable. SaaS companies like Salesforce or Workday often sell annual or multiyear deals, making it fairly straightforward to tally the value of subscriptions likely to recur.

"When you have ARR, you have a signed contract, and you know what you've signed up for for the year," said Matt Murphy, a partner at Menlo Ventures.

AI has proven less predictable. Many AI companies no longer sell a simple per-seat license that costs the same every month. They charge customers based on how much they use, often measured in tokens. Someone may use a product aggressively, then pull back, or switch to a cheaper model. The startup's own costs vary widely, too, because it may have to pay a model provider or cloud company every time a customer uses its product.

AI startups have increasingly shifted to describing run rate, which extrapolates a month of revenue over a full year and does not purport to be recurring, according to Murphy.

"Venture does this from time to time, which is that there's a new metric that shows up," he said.

Pocket, which makes an AI recording device, is one of those companies, having recently said it crossed the $100 million run rate threshold. Founder Akshay Narisetti said he avoids ARR because tokens make revenue too unpredictable to expect it to be recurring.

Run rate can come with its own problem in that a gangbusters month does not necessarily translate to a year, but Narisetti said he avoids doing that.

"We never annualize one large month with a spike," Narisetti said.

The biggest AI labs have used both metrics. OpenAI has described subscription sales as ARR while calling its newer advertising business an annualized revenue run rate. Anthropic has generally described its overall sales as run-rate revenue.

Linear, which is an increasingly rare breed of SaaS startups, recently said it crossed $100 million in ARR.

Cofounder and CEO Karri Saarinen said he stands by the metric because Linear sells multiyear contracts and has been in business since 2019.

"Since we've been operating longer, we can better forecast the actual ARR," he said, adding that for newer AI startups, it is a different story.

"It's gotten really murky," he said.

Why VCs Are Questioning AI Companies' Big Revenue Claims

According to a report from Bloomberg News on Monday, Anthropic has chosen to abandon its plans to acquire the artificial intelligence startup known as Decart AI. This decision comes from sources who are knowledgeable about the situation.

Anthropic abandons efforts to acquire $6 billion-valued AI startup Decart

A dimly lit corporate boardroom at night, overhead tungsten spotlight casting long shadows over an empty negotiation table. On the table lies a torn contract stamped with the Decart logo. On the far wall, a large illuminated Anthropic logo flickers and dims as if powering down. The scene is moody and atmospheric, dark grays and warm amber highlights emphasizing a deal collapsed.

Anthropic drops pursuit of AI startup Decart - The Economic Times

- Anthropic has opted not to acquire AI startup Decart AI, reports indicate.  
- Sources reveal the decision follows internal evaluations of the potential acquisition.  
- The move reflects Anthropic's strategic focus and current investment priorities.

Synopsis

Anthropic âhas decided not âto pursue an acquisition of artificial âintelligence startup Decart AI, Bloomberg News reported on Monday, citing people familiar with the matter.

Anthropic drops pursuit of AI startup Decart valued at $6 billion

Anthropic has decided not to proceed with its planned acquisition of Israeli AI startup Decart, according to a Bloomberg report released on Tuesday. This shift in direction comes after the company conducted its due diligence, which revealed that the transaction, potentially valuing Decart at around $6 billion, was not aligned with its strategic goals.

The creators of the Claude chatbot had advanced in the acquisition discussions sufficiently to perform due diligence on Decart. However, despite the progress, Anthropic has chosen to abandon the deal.

According to Bloomberg's report, the two organizations may still explore alternative collaborations, indicating that Anthropic recognizes the potential value in Decart's technology despite its decision against a full acquisition.

This decision carries weight because the proposed acquisition would have equipped Anthropic with technology that addresses a critical challenge in the AI sector: optimizing the cost and efficiency of computing resources.

Decart specializes in software designed to enhance the efficacy of AI chips, significantly reducing the expenses associated with training and deploying AI models. For an AI firm that requires vast computational power for its products, this technology is at the forefront of its burgeoning business.

As Anthropic ramps up its investments in computational capacity while developing new offerings and gearing up for a potential initial public offering, the discussions with Decart held particular significance. The company seemed poised to invest in the foundational economics that underpin the operation of advanced AI systems, rather than simply adding another software application to its lineup.

The anticipated $6 billion valuation would have underscored a substantial commitment to the importance of Decart's technology. However, after a thorough evaluation of Decart's operations and offerings, Anthropic has opted against the acquisition.

The intention to keep channels of collaboration open suggests that Anthropic’s choice does not imply a dismissal of Decart’s technology itself. Instead, it seems Anthropic believes it can engage with the startup productively without needing to acquire it outright.

Founded in 2023, Decart currently employs around 100 individuals and has successfully raised $450 million thus far, with its most recent valuation set at $4 billion.

Anthropic cancels its acquisition of Israeli AI startup Decart.

A dimly lit, moody boardroom at night: a single warm spotlight falls on a torn contract split in two—one half stamped with the Anthropic logo, the other with Decart’s. A small Israeli flag hangs on the dark paneled wall behind. Scattered papers drift off the edge of a polished mahogany table into deep shadow, hinting at plans abandoned.

Anthropic drops plans to acquire Israeli AI startup Decart - calcalistech.com

- Anthropic has abandoned plans to acquire Israeli AI startup Decart after due diligence.  
- The acquisition could have valued Decart at approximately $6 billion prior to abandonment.  
- Anthropic may still pursue collaboration with Decart, valuing its technology without acquisition.

Anthropic has abandoned plans to acquire Israeli artificial intelligence startup Decart after conducting due diligence on the company, Bloomberg reported Tuesday, marking a reversal from talks over a transaction that could have valued the startup at about $6 billion.

The Claude chatbot maker had explored an acquisition of Decart and advanced far enough in the process to conduct due diligence. Anthropic ultimately decided not to pursue the transaction.

1 View gallery Decart founders. ( Photo: Yuval Chen )

The two companies could still work together in other ways, Bloomberg reported, leaving open the possibility that Anthropic sees value in Decart's technology even though it has decided against buying the company outright.

The decision is notable because the reported acquisition would have given Anthropic control of technology aimed at one of the most consequential constraints facing the AI industry: the cost and efficiency of computing.

Decart develops software designed to improve the efficiency of AI chips and reduce the cost of training and running AI models. For an AI company whose products require enormous amounts of computing capacity, the technology addresses a problem that sits at the center of its rapidly expanding business.

Anthropic has been increasing its spending on computing capacity as it develops new products and prepares for a potential initial public offering. That made the reported discussions with Decart particularly significant. Rather than simply adding another AI application to its portfolio, Anthropic appeared to be considering an investment in the underlying economics of operating increasingly powerful AI systems.

The reported $6 billion price tag would also have represented a substantial bet on the importance of that technology. But after reviewing Decart's business and technology in detail, Anthropic has now decided that an acquisition is not the right path.

The companies' willingness to continue exploring other forms of collaboration suggests the decision does not necessarily reflect a rejection of Decart's technology itself. Instead, Anthropic may have concluded that it can work with the company without taking ownership of it.

Founded in 2023, Decart employs approximately 100 people and has raised $450 million to date, most recently at a $4 billion valuation.

Anthropic drops plans to acquire Israeli AI startup Decart

The entrepreneur who successfully sold Fanbytes to Brainlabs is now channeling his own substantial exit into supporting AI startups led by minority founders, leveraging a $6.7 million fund named after a street in Ghana.

Timothy Armoo achieved remarkable success early in his career. He co-founded Fanbytes, an influencer marketing agency that partnered with significant clients like the UK government, Deliveroo, and Samsung, before selling it to Brainlabs in 2022 for an eight-figure sum at just 27 years old. Now, at 31, he has invested £5 million, roughly $6.7 million, into the Legon Fund, which specifically targets AI-centric startups spearheaded by minority entrepreneurs, as shared in a Fortune interview published on August 31, 2026.

The fund's name holds personal significance. Legon refers to the street in Ghana where Armoo grew up before relocating to London, where he experienced life on a council estate marked by gang violence and other challenges. Arriving in the UK without financial resources or connections has influenced the type of founders Armoo seeks to support. Unlike many seed investors, his focus isn’t on first-time founders with mere ideas and presentations, but rather on entrepreneurs who have established traction but require specific resources to move forward.

In one succinct expression, Armoo states, “The main differentiator now is distribution.” This sums up his investment philosophy perfectly. He’s not interested in funding just any product development; instead, he aims to support individuals who have already created something valuable and need help reaching customers. While it might sound somewhat mundane, this focused approach sets his fund apart from the spectacle of pitch-deck presentations typical of many seed funds.

Armoo is already familiar with angel investing. Prior to launching the Legon Fund, he invested in early-stage new media and e-commerce startups with financial commitments ranging from £25,000 to £50,000 for 1 to 5% equity—an approach reflective of someone who has experienced the rigors of starting a business. His background provides him with insight into what founders require at a crucial growth stage, as he has navigated similar challenges himself.

In contrast, institutional capital flowing into AI often targets larger endeavors requiring millions before they can even consider customer interaction. Armoo’s strategy is to make smaller investments later in the trajectory of growth, focusing on individuals who, much like himself at the age of 21, possess talent but lack the safety net often enjoyed by others. As he noted, “My wealth wave was social media. Now I believe the wealth wave is AI.”

Armoo does not shy away from his perspective on wealth creation, describing it as “scarily easy” in today's climate and labeling this period as the “greatest era of wealth creation ever.” He cites accessible tools like ChatGPT and Claude, in conjunction with social media distribution, as pivotal to this trend. His candid remarks toward the younger generation of job seekers have sparked debate online, but the core argument remains clear: the obstacle to starting a venture is no longer capability but rather access to distribution funding. This is the very gap that the Legon Fund aims to address.

Ultimately, the noteworthy aspect is not just the fund’s size—$6.7 million pales in comparison to the vast sums raised by companies like OpenAI or Anthropic. What stands out is Armoo's strategic decision on how to deploy his capital following his successful exit. Instead of merely investing in traditional vehicles like index funds or family offices, he is reinvesting in early-stage founders who mirror his own background prior to the success of Fanbytes. This approach diverges from the conventional venture capital trajectory of institutional fundraising and prolonged fund lifespans; it signifies a founder-driven initiative to reintegrate into the entrepreneurial ecosystem.

As for whether the Legon Fund will achieve its own significant exit in the future remains to be seen. However, what is clear is Armoo's calculated investment: a defined amount directed at a specific profile of founder, with the belief that distribution is the current scarce commodity in the realm of AI. This focused perspective is narrower than the oft-repeated trend of “AI is the next big thing," making it a compelling narrative to monitor in the coming years.

Timothy Armoo Invests $6.7 Million from His Fanbytes Wealth into AI Startups

A bright, sunlit workspace: a sleek golden chalice embossed with the Fanbytes logo tilts, pouring a cascade of gleaming gold coins and tiny microchip-shaped seeds onto rich soil, where delicate circuit-board sprouts unfurl under warm natural light.

Timothy Armoo Pours $6.7 Million of His Fanbytes Fortune Into AI Startups - startupfortune.com

- Timothy Armoo launched the Legon Fund with $6.7 million for minority AI entrepreneurs.  
- The fund focuses on startups with traction needing help with customer distribution strategies.  
- Armoo's approach contrasts big VCs, targeting smaller, later-stage investments to support diverse founders.

The founder who sold Fanbytes to Brainlabs is now betting his own eight-figure exit on AI startups run by minority entrepreneurs, and he's doing it with a $6.7 million fund named after a street in Ghana.

Timothy Armoo made his money once already. He co-founded Fanbytes, an influencer marketing agency that worked with the UK government, Deliveroo and Samsung, and sold it to Brainlabs in 2022 in an eight-figure deal when he was 27. Now 31, he's put £5 million, about $6.7 million, of his own cash into a new vehicle called the Legon Fund, aimed squarely at AI-first startups founded by minority entrepreneurs, according to a Fortune interview published August 31, 2026.

The name is personal. Legon is the street in Ghana where Armoo lived as a child before he moved to London and grew up on a council estate, by his own account surrounded by gang violence and knife crime. He arrived in the UK with no money and no network. That biography shapes who the fund is looking for, and it's a narrower brief than most seed investors run: not first-time founders with a slide deck and an idea, but operators who already have traction and are missing one specific thing.

The thesis in one line

"The main differentiator now is distribution," Armoo told Fortune. That's the whole thesis. He's not writing cheques to help people build a product. He's writing cheques to help people who've already built something get it in front of customers. It's a narrow, almost boring-sounding filter. And that's exactly what makes it different from the pitch-deck theatre most seed funds run on.

Armoo isn't new to writing cheques. Before Legon Fund, he'd been angel investing in pre-seed new media and ecommerce startups at ticket sizes around £25,000 to £50,000 for 1 to 5% equity, the kind of money that comes from someone who remembers being on the other side of the table. That history is worth more here than a track record slide. When a founder has spent years building an audience-driven agency and then handed it off in an acquisition, they know exactly what a founder needs at the traction stage. They needed it themselves, too.

A different kind of money

Compare that to how most institutional AI money moves right now. Big-name VCs are chasing model companies and infrastructure plays, the kind of deals that need tens of millions before they ever touch a customer. Armoo is doing the opposite: smaller cheques, later in the traction curve, aimed at people who look like he did at 21, arriving with talent and no safety net. "My wealth wave was social media," he said. "Now I think the wealth wave is AI."

He's not shy about the framing, either. In the same Fortune interview, Armoo said it's "scarily easy" to build wealth right now and called this the "greatest era of wealth creation ever," pointing to free tools like ChatGPT and Claude paired with social distribution as the on-ramp. He had little patience for excuses from unemployed Gen Z jobseekers. That stance has drawn plenty of pushback online since the piece ran. Whatever you make of the tone, the underlying claim is concrete: capability isn't the barrier to starting any more - distribution money is. That's the exact gap Legon Fund says it's built to close.

Frankly, the more interesting story here isn't the fund's size. $6.7 million is a rounding error next to what OpenAI or Anthropic raise in a single week. It's what Armoo's move says about where founder money goes after an exit. He built an audience business, sold it for eight figures. Instead of parking that capital in an index fund or a family office, he's redeploying it into founders who look like he did before Fanbytes existed. That's a different pattern from the traditional VC path of institutional fundraising and a decade-long fund life. It's closer to a founder writing himself back into the game. One cheque at a time.

Whether Legon Fund produces its own eight-figure exit is unknowable from here. What's verifiable is the bet itself: a specific dollar amount, a specific founder profile, and a thesis that distribution, not ideas, is now the scarce resource in AI. That's a much narrower claim than "AI is the next big thing." And it's exactly the kind of specific, checkable wager worth watching over the next few years.

Also read: NewLimit Says Its AI System Reversed Aging Signs in Human Liver Cells • Nvidia Is Buying the AI Startup That OpenAI's Own Models Hacked • Taiwan Turns Its Chip Dominance Into Diplomatic Leverage Over the US and EU

Timothy Armoo Pours $6.7 Million of His Fanbytes Fortune Into AI Startups

Busan Port Authority (BPA) has selected three artificial intelligence startups to test and commercialise new technologies across safety, smart port operations and international logistics.

Xylo Labs, Uncommon Lab and Cargo Tree have joined 1876 Busan, the startup support platform operated by BPA alongside the Busan Metropolitan Government and Korea Ocean Business Corporation.

The programme, launched in 2022, gives early-stage maritime and logistics companies access to Busan Port infrastructure to trial technologies while receiving office space, consulting, development support, investment matchmaking and opportunities to participate in BPA research projects.

Xylo Labs has developed AI technology that analyses the condition and operation of port cranes, looking for early indications of equipment failure.

Uncommon Lab develops AI-powered operating systems and software for smart ports and will work with BPA as the authority rolls out its longer-term artificial intelligence strategy.

Cargo Tree operates an AI-based multinational freight forwarding platform. Its work with BPA will include expanding shipping and logistics networks as well as looking at commercial opportunities linked to emerging Arctic shipping routes.

“The selected companies have the potential to grow into global players, using Busan Port’s infrastructure and research support as a springboard,” said BPA president Song Sang-keun.

Busan, South Korea’s largest port and one of the world’s busiest container hubs, has increasingly been positioning itself as a testing ground for automation, artificial intelligence and other technologies aimed at improving cargo handling and terminal productivity.

Busan Port recruits AI startups

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Busan Port recruits AI startups - Splash247

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Google DeepMind just handed a lifeline to 16 organizations across Asia-Pacific, all racing to solve some of the region's toughest environmental problems with frontier AI. The move plugs frontier machine learning into climate resilience, agriculture, and biodiversity work in a region that's home to some of the world's most climate-vulnerable economies, and it signals Google's growing bet that AI can do more than write emails and generate images. Google DeepMind is putting its weight behind 16 organizations across Asia-Pacific, all building frontier AI tools aimed squarely at climate, agriculture, and biodiversity problems. The announcement, posted on Google's Keyword blog by Spencer Low, Google's Head of Regional Sustainability for APAC, marks the latest expansion of the company's AI Planet Accelerator, a program that's been quietly building a portfolio of environmentally-focused AI startups and nonprofits. The timing isn't random. Asia-Pacific is arguably ground zero for the kind of climate stress that makes headlines every monsoon season and every failed harvest. From rising seas threatening coastal cities to erratic weather patterns wrecking crop yields, the region has both the most exposure to climate risk and, increasingly, some of the sharpest technical talent trying to build tools to manage it. Google's decision to funnel accelerator support here rather than just in the US or Europe tracks with a broader pattern of big tech companies chasing growth and goodwill in emerging AI markets simultaneously.

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Google DeepMind, the AI research lab responsible for breakthroughs like AlphaFold, has increasingly positioned itself as a science-first counterweight to more consumer-facing AI labs like OpenAI. Its accelerator model gives selected organizations access to DeepMind researchers, compute resources, and mentorship, essentially trying to compress years of R&D timeline into months. That's a meaningful offer for smaller orgs in the climate and agtech space, where compute costs and access to cutting-edge large language models can be prohibitively expensive on a nonprofit or early-stage startup budget. What's notable here is the specificity of focus. Rather than a broad-strokes 'AI for good' pitch, the 16 organizations are reportedly working on concrete problems: modeling climate resilience for vulnerable communities, optimizing agricultural yield predictions, and tracking biodiversity loss through AI-powered monitoring systems. That kind of applied, narrow-scope AI work has become something of a signature for how Google frames its sustainability initiatives, distinct from the more generalized productivity tools coming out of Microsoft or the consumer gadgets from Apple and Samsung. Industry watchers have long debated whether corporate AI accelerators actually move the needle on climate outcomes or whether they're mostly reputation management dressed up as R&D support. Skeptics point out that pilot programs backed by big tech companies often fail to scale once the accelerator cohort ends and funding dries up. But supporters argue that even seed-stage support, especially access to frontier models and technical expertise that would otherwise be out of reach, can meaningfully accelerate timelines for climate tech that's chronically underfunded compared to sectors like fintech or enterprise SaaS.

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Google DeepMind Backs 16 APAC Climate AI Startups

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Google DeepMind Backs 16 APAC Climate AI Startups - The Tech Buzz

VCs, celebrities bet on AI startups in media, sports

NEW DELHI: Artificial intelligence investment is widening beyond enterprise software and chip bets, and one of the areas money is increasingly flowing into is media, entertainment and sports. In India, venture capital firms, private equity investors, family offices and celebrities are backing startups using AI to create content, localise it, distribute it and build intellectual property.Azim Premji's investment firm Premji Invest earlier this year participated in the $315 million Series E funding of Runway, a New York headquartered firm that builds generative AI tools for video creation, editing, and world modeling. Peak XV, an India fund, led Dashverse's $13 million Series A investment last year. Dashverse, headquartered in US and India, and founded by Sanidhya Narain, Lalith Gudipati, and Soumyadeep Mukherjee, is an AI-powered entertainment company that builds tools to create and distribute short-form mobile video dramas and digital comics.IAN Alpha Fund, another India-based fund, led SportVot's Rs 33 crore investment round in April.Mumbai-headquartered SportVot, founded by Sidhhant Agarwal, Shubhangi Gupta, and Yash Bhagwatkar, is an AI-powered sports production, streaming and analytics venture. Its AI system automatically identifies key events - like a goal in football or a buzzer-beater in basketball - and clips, formats, and tags the footage in real time without manual editing delays. It tracks individual player movements and generates specific player clips to help talent scouts find regional athletes."Artificial Intelligence is rapidly reshaping the entertainment and sports industries, transforming how content is created, how celebrities engage with audiences, and how brands leverage influence," says Umakanta Panigrahi, MD of valuation advisory services at Kroll, a global financial and risk advisory firm.Nandagopal P, limited partner in VC firm Arya Ventures, says there's rising investor interest in AI media startups. "They always had three expensive bottlenecks: content creation, localisation and distribution. AI directly attacks all three by reducing production cost, ad monetisation and turning sports or entertainment data into real-time fan engagement. The numbers explain the momentum: the global AI media and entertainment market estimated at $34 billion in 2025, is projected to reach $160 billion by 2033, while AI in sports is projected to grow from $11 billion to nearly $50 billion during the same period," he said.A number of celebrities have also made investments in AI-based ventures in entertainment and sports. Shah Rukh Khan's family office, along with a bunch of investors, has invested in Mythik, which uses advanced technologies like AI and virtual production to bring Eastern mythology, history, and folktales to worldwide audiences. Mythik was founded by Jason Kothari, former CEO of Freecharge and Housing.com. Ravi Shastri, former cricketer and cricket coach, is funding Smartan FitTech, a Chennai-based venture that uses AI and computer vision to analyse athlete movements and prevent sports injuries.Actor Bhumi Pednekar has founded Maya that focuses on using AI-enabled workflows to create music videos, advertising campaigns, and feature-length films. Actor Ajay Devgn has founded Prismix that specialises in generative AI storytelling for filmmakers, brands, and creators.Nandagopal says celebrities are entering the sector because this is not just financial investing for them; "AI can multiply their IP, likeness, voice, fandom and distribution."

VCs, celebrities bet on AI startups in media, sports

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VCs, celebrities bet on AI startups in media, sports - The Times of India

Since the remarkable rise of Wiz, Israeli high-tech has not produced another capital-raising and revenue-growth machine on the scale of the cybersecurity company, which was eventually acquired by Google for $32 billion. Until now. Wonderful AI is beginning to look like the closest candidate.

Last week, Wonderful moved into a league occupied by very few Israeli companies, particularly those outside cybersecurity. Less than two years after its founding, the company raised $550 million at a $5 billion valuation, more than doubling the $2 billion valuation at which it raised capital just six months ago. Founded in early 2025 by Bar Winkler and Roey Lalazar, Wonderful has now raised more than $800 million.

1 View gallery Wonderful founders. ( Photo: Daniel Jackont )

But the funding numbers, extraordinary as they are, are not the most interesting part of the story. Wonderful has also achieved remarkable revenue growth, going from virtually no revenue a year ago to an annualized pace of about $70 million today. By the end of the year, that figure is expected to reach $100 million.

Wiz reached a $6 billion valuation roughly two years after its founding and hit $100 million in annual recurring revenue at the age of 18 months. Wonderful is now moving on a similarly steep trajectory, albeit with a very different business model.

So what explains the phenomenon, and is Wonderful emerging as Israel's standout AI company?

In Wiz's case, the formula was relatively easy to understand. Its founders had worked together at Microsoft, where they built the foundations of a cybersecurity platform before leaving to establish the company. Wonderful's story is considerably harder to define, partly because the company itself has changed so rapidly.

Wonderful initially identified a gap in the AI market: while the technology was developing rapidly in English-speaking markets, adoption was slower in countries where English was not the primary language. The company built a strong solution for voice-based call centers in languages other than English and expanded rapidly outside the United States.

Israeli consumers may already have interacted with Wonderful's technology without realizing it when calling the customer-service centers of Maccabi Health Services, Bank Leumi, Bezeq or Israel Electric Corporation.

But in its latest fundraising announcement, Wonderful presented itself in much broader terms, describing its offering as an "AI operating system" and making little mention of the voice call-center business that featured prominently in its pitch only a year ago.

People familiar with the company describe a rapid, and at times almost chaotic, evolution driven by the extraordinary pace of the AI revolution.

"Wonderful entered the strongest and largest organizations with interesting technology that was impressive for its time," says Uri Eliabayev, an artificial intelligence expert and consultant to companies on AI. "Later, they were able to build a strong marketing and sales organization, based on senior executives from well-known high-tech companies, and expanded rapidly.

"What was truly ingenious was that the people at Wonderful quickly realized that once they got their foot in the door of an organization, it needed more and more assistance and guidance in adopting AI. That's what the company is providing today."

That realization is central to understanding both Wonderful's rapid growth and its appetite for capital.

The company's primary product is, in effect, a workforce that helps organizations adopt AI, and it is an expensive workforce because it requires people with specialized AI expertise.

Two numbers that receive considerably less attention than the company's valuation illustrate the challenge.

The first is its headcount relative to its age and revenue. Wonderful already employs about 650 people, roughly half of them in Israel, and plans to triple its workforce within a year. About a month ago, it also announced plans to establish a center in Mumbai, where it expects to hire another 1,000 employees over the coming year.

The second number follows directly from the first. According to The Wall Street Journal, Wonderful's gross profit margin is about 52% of revenue. That is substantially below the 70%-90% gross margins typically associated with software companies.

Wonderful is therefore not a classic software-as-a-service company of the kind that dominated the technology market in recent years. It is closer to the increasingly important category of service-led software, in which software and human services are closely intertwined.

About 400 of Wonderful's 650 employees work at customer sites rather than from the company's own offices. Their job is to help implement AI agents and integrate them into organizations. In practice, they act as a bridge between the technology and the customer, identifying where AI can be deployed quickly and smoothly and then helping execute those projects.

That also gives Wonderful a potentially valuable advantage: its employees become deeply familiar with how an organization operates and where additional AI agents could be introduced. In that sense, the implementation workforce can also become a sales force.

The model has similarities to Palantir, which pioneered a service-heavy approach to deploying software, particularly in complex security and government projects. Palantir is now valued at roughly $400 billion. As AI adoption accelerates, the model is spreading, including in Israel, where startup Unframe is pursuing a similar approach.

The comparison may not be one Wonderful would embrace, but there is another historical parallel worth considering. In its early years, Amdocs was essentially a company built around "skulls", highly skilled professionals, many of them engineers, who were sent to customers struggling to adopt the internet and the new services it enabled.

That comparison helps explain the paradox at the heart of Wonderful.

On the one hand, the company is increasingly being valued as something closer to AI infrastructure than a conventional SaaS startup. Its ability to enter large organizations, help them adopt AI and then expand its role as their needs grow has allowed it to build revenue at extraordinary speed.

On the other hand, that model comes with high costs. Wonderful needs hundreds of employees to deliver its services, and it plans to add many more. That means the company will likely require substantial amounts of capital to sustain its expansion.

There is also a more fundamental question about how much of its current growth is being driven by its technology and how much by its pricing and willingness to deploy large teams at customer sites.

Some people familiar with the market argue that Wonderful is currently able to grow so quickly partly because it offers services at relatively low prices compared with established players such as Salesforce and Accenture. The comparison is reminiscent of Uber's early years, when venture capital helped subsidize prices and accelerate adoption before the company had to confront the economics of operating at scale.

Wonderful is growing like Wiz, but its business is very different

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Wonderful is growing like Wiz, but its business is very different - calcalistech.com

The advent of artificial intelligence has introduced remarkable changes, particularly in the realm of enterprise IT. According to a prediction by market research firm IDC, companies that have traditionally been hesitant and selective about their tech investments are projected to spend around $4.25 trillion on technology by 2026, largely driven by advancements in AI.

A recent survey conducted by the venture capital firm Madrona indicates that 74% of the 150 enterprise IT professionals questioned expect to increase their AI budgets over the next year, while the remaining participants aim to maintain their current spending levels. Yet, despite this optimism, less than half of these enterprises report that their AI pilot projects transition into full-scale production.

This represents a significant improvement compared to findings from MIT last year, which revealed that a staggering 95% of enterprise AI initiatives failed to deliver a return on investment. Although having less than half of projects succeed sets a low standard, it is certainly an improvement from the previously dismal 5% success rate.

Moreover, Madrona's research highlights that when companies do implement AI technology, they frequently reassess their partnerships with AI vendors. An impressive 77% of firms conduct these evaluations every six months or even continually. This trend fosters a “fast in, fast out” environment that starkly contrasts with traditional enterprise software as a service (SaaS), where lengthy contracts usually create a barrier to switching providers. As noted in the report, “In enterprise AI, switching costs are lower, and the re-evaluation cadence is relentless.”

This trend has significant implications for the rapidly increasing annual recurring revenue (ARR) figures that many startups currently report. The initial boom in AI spending by enterprises was fueled by trial budgets, and this year was anticipated as a turning point where big clients would start making long-term commitments to AI startups. These enterprise contracts are crucial for many AI businesses, enabling them to showcase impressive revenue growth, particularly the phenomenon where startups jump from $0 to $10 million in just three months.

However, an unprecedented challenge has emerged: enterprise revenue remains precarious even after a startup's AI solution moves beyond the pilot stage and is adopted by a company.

One contributing factor is that many AI startups are still struggling to establish effective pricing strategies for their offerings tailored to enterprises. Research from VC firm Andreessen Horowitz, which surveyed 50 technical AI buyers, found that a majority would prefer AI fees to be linked to results or completed work rather than usage metrics, such as the number of tokens utilized.

Adopting a pricing model based on consumption, like that of traditional SaaS, poses a challenge. Once a company identifies a need for tools like email, HR software, or cloud storage, their decision is mostly influenced by employee numbers or data volume. In contrast, pricing AI in relation to tangible outputs—such as reports processed, tickets resolved, or leads generated—demonstrates the product's value more effectively, creating a beneficial economic relationship for both the startup and client, as stated by a16z partners Tugce Erten and Sarah Wang.

Ultimately, these dynamics suggest that AI has initiated a new phase of experimentation in enterprise environments. This shift may present opportunities for startups, as enterprises seem increasingly open to testing new technologies. Yet, it remains uncertain whether and when these organizations will revert to their traditional long-term purchasing patterns.

New research indicates that startup ARR is more vulnerable than ever.

A moody, dimly lit startup office at night. At the center, a large neon “ARR” sign sputters and cracks, glowing red and orange as streams of binary code pour out like leaking data. On the floor beneath, a shattered padlock and scattered security audit reports lie in disarray. In the shadows, a lone silhouetted entrepreneur stands, shoulders slumped, staring at the damaged sign. Overall dark theme with dramatic shadows and warm accent lights to highlight the breach.

Startup ARR is less secure than ever, new research shows - TechCrunch

- Companies are projected to spend $4.25 trillion on technology by 2026, driven by AI.  
- 74% of IT professionals plan to expand AI budgets, despite low pilot success rates.  
- Enterprises reevaluate AI vendors frequently, affecting long-term commitments and revenue stability.  

AI has ushered in a lot of never-happened-before moments, but one of the most transformative is its impact on enterprise IT. Companies that have historically been cautious and committed long-term to what they buy are on pace to spend $4.25 trillion on technology in 2026, market researcher IDC predicts. It’s almost all driven by AI.

New research from venture capital firm Madrona shows that 74% of 150 enterprise IT professionals it surveyed plan to expand their AI budgets in the next 12 months, and the rest plan to hold spending steady. Yet these same enterprises say that fewer than half of their AI pilots ever make it into full production.

That’s actually an improvement. Last year, MIT famously reported that 95% of enterprise AI projects had failed in terms of ROI. Fewer than half succeeding is a pretty low bar, but it’s better than a 5% success rate.

But the most telling finding from Madrona’s report is that, even when an enterprise does roll out the AI tech, it doesn’t commit to it long term.

Some 77% of enterprises reevaluate their AI vendors every six months or even on a rolling basis. “This creates a ‘fast in, fast out’ dynamic that is fundamentally different from traditional enterprise SaaS, where multi-year contracts provided a moat of inertia,” Madrona writes in the report. “In enterprise AI, switching costs are lower and the re-evaluation cadence is relentless.”

This has widespread implications for all those fast-growing annual recurring revenue (ARR) numbers startups report. Enterprise trial budgets are what fueled the initial AI boom of 2025. This year was supposed to be the year these big customers settled in and started committing long term to AI startups. Enterprise contracts are what allow so many AI startups to claim astronomically fast revenue growth — think the phenomenon of startups going from $0-$10 million in three months.

Yet, for the first time ever, enterprise revenue remains insecure, even after a startup’s AI product graduates out of a pilot phase and gets adopted by a company.

Part of the issue is that many AI startups haven’t fully landed on a good way to price their AI wares for enterprises. New research from VC firm Andreessen Horowitz that surveyed 50 technical AI buyers found that more than half of them want AI fees tied to the work produced or other outcomes, rather than to usage like the number of tokens consumed.

Charging for usage like tokens is basically a SaaS-era business model. Once an enterprise knows it needs email, or HR software, or cloud storage, it’s merely a matter of how many employees or how much data it must pay for.

For AI, pricing “around the recognizable work” is what helps the startup prove its worth to the customer. When the fees revolve around, say, how many reports are processed, or tickets closed, or leads generated, this makes the product “economically valuable to both sides,” writes a16z partners Tugce Erten and Sarah Wang.

All of this means that AI has potentially ushered in a new era of enterprise experimentation. That opens doors to startups — enterprises are more willing to try their tech — but it also means an enterprise contract no longer secures long-term revenue. When or if enterprises will revert to their long-term buying habits remains to be seen.

German robotics company Agile Robots poised to double its revenue this year.

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