Tech
a16z VC wants founders to stop stressing over insane ARR numbers
The AI investing boom (or perhaps bubble) is something Silicon Valley has seen many times before: a gold rush of VC money thrown at the Big New Thing. But one aspect of it is completely unique to these times: startups rocketing from $0 to as much as $100 million in annual recurring revenue, sometimes in a matter of months.
Word on the street is that many a VC won’t even look at a startup that’s not on the ARR superhighway, aiming for $100 million in ARR before their Series A funding round.
But Andreessen Horowitz general partner Jennifer Li, who helps oversee many of the firm’s most important AI companies, warns that some of the ARR mania is based on myths.
“Not all ARR is created equal, and not all growth is equal either,” Li said on an episode of TechCrunch’s Equity podcast. She said to be especially skeptical of a founder announcing spectacular ARR numbers or growth in a tweet.
Now, there is a legit, well-recognized term in accounting called annual recurring revenue, which refers to the annualized value of contracted, recurring subscription revenue. Essentially, that’s a guaranteed level of revenue because it comes from customers on a contract.
But what many of these founders are tweeting about is really “revenue run rate” — taking whatever money was paid in a period of time and annualizing it. That’s not the same.
“There’s a lot of missing nuances of the business quality, retention, and durability that’s missing in that conversation,” Li warned.
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A founder may have just had a killer month of sales, but not every month will necessarily repeat it. Or a startup may have a lot of short-term customers doing pilot programs, so revenue isn’t guaranteed to stick around after the pilot period.
Normally, such boasts about growth via tweets should be treated for what they are — that is, don’t take everything you read on the internet at face value.
But because fast growth is a hallmark of AI startups, such claims are “introducing a lot of anxiety” to inexperienced founders who are now asking how they can also instantly go from zero to $100 million, she said.
Li’s answer: “You don’t. Sure, it’s a great aspiration, but you don’t have to build a business that way, to only optimize for the top-line growth.”
She said a better way to think of it is this: how to grow sustainably, where once customers sign up, they stick around and expand their spend with your company. This can lead to “growing 5x or 10x year-over-year,” said Li, meaning growth from $1 million to between $5 million and $10 million in year one, to between $25 million and $50 million in year two, and so on.
Li pointed out that this is still “unheard of” levels of growth. If it’s coupled with happy customers — that is, high retention rates — those startups will find investors willing to back them.
Of course, some of the portfolio companies in Li’s a16z group (the infrastructure team) have hit those kinds of racing ARR numbers: Cursor, ElevenLabs, and Fal.ai. But that growth is tied to “durable businesses,” said Li, adding, “There’s real reasons behind each of them.”
Li also said that kind of growth comes with its own set of operational problems like hiring.
“How do we hire, not fast, but the right people who can really jump into this type of speed and culture,” she said. And the answer is: not easily.
That means those first 100 people wear a lot of hats and missteps are bound to happen. Last year, Cursor, for instance, angered its customer base with a poorly rolled out pricing change.
Li pointed out that other fast-growth startups deal with legal and compliance issues before they have systems in place to, or face new AI-age issues like countering deepfakes.
So while lightning-fast growth can be a good problem to have, it’s also a little bit of “be careful what you wish for.”
Listen to the full episode here:
Tech
ElevenLabs CEO: Voice is the next interface for AI
ElevenLabs co-founder and CEO Mati Staniszewski says voice is becoming the next major interface for AI – the way people will increasingly interact with machines as models move beyond text and screens.
Speaking at Web Summit in Doha, Staniszewski told TechCrunch voice models like those developed by ElevenLabs have recently moved beyond simply mimicking human speech — including emotion and intonation — to working in tandem with the reasoning capabilities of large language models. The result, he argued, is a shift in how people interact with technology.
In the years ahead, he said, “hopefully all our phones will go back in our pockets, and we can immerse ourselves in the real world around us, with voice as the mechanism that controls technology.”
That vision fueled ElevenLabs’s $500 million raise this week at an $11 billion valuation, and it is increasingly shared across the AI industry. OpenAI and Google have both made voice a central focus of their next-generation models, while Apple appears to be quietly building voice-adjacent, always-on technologies through acquisitions like Q.ai. As AI spreads into wearables, cars, and other new hardware, control is becoming less about tapping screens and more about speaking, making voice a key battleground for the next phase of AI development.
Iconiq Capital general partner Seth Pierrepont echoed that view onstage at Web Summit, arguing that while screens will continue to matter for gaming and entertainment, traditional input methods like keyboards are starting to feel “outdated.”
And as AI systems become more agentic, Pierrepont said, the interaction itself will also change, with models gaining guardrails, integrations, and context needed to respond with less explicit prompting from users.
Staniszewski pointed to that agentic shift as one of the biggest changes underway. Rather than spelling out every instruction, he said future voice systems will increasingly rely on persistent memory and context built up over time, making interactions feel more natural and requiring less effort from users.
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That evolution, he added, will influence how voice models are deployed. While high-quality audio models have largely lived in the cloud, Staniszewski said ElevenLabs is working toward a hybrid approach that blends cloud and on-device processing — a move aimed at supporting new hardware, including headphones and other wearables, where voice becomes a constant companion rather than a feature you decide when to engage with.
ElevenLabs is already partnering with Meta to bring its voice technology to products, including Instagram and Horizon Worlds, the company’s virtual-reality platform. Staniszewski said he would also be open to working with Meta on its Ray-Ban smart glasses as voice-driven interfaces expand into new form factors.
But as voice becomes more persistent and embedded in everyday hardware, it opens the door to serious concerns around privacy, surveillance, and how much personal data voice-based systems will store as they move closer to users’ daily lives — something companies like Google have already been accused of abusing.
Tech
Substack confirms data breach affects users’ email addresses and phone numbers
Newsletter platform Substack has confirmed a data breach in an email to users. The company said that in October, an “unauthorized third party” accessed user data, including email addresses, phone numbers, and other unspecified “internal metadata.”
Substack specified that more sensitive data, such as credit card numbers, passwords, and other financial information, was unaffected.
In an email sent to users, Substack chief executive Chris Best said that the company identified the issue in February that allowed someone to access its systems. Best said that Substack has fixed the problem and started an investigation.
“I’m reaching out to let you know about a security incident that resulted in the email address and phone number from your Substack account being shared without your permission,” said Best in the email to users. “I’m incredibly sorry this happened. We take our responsibility to protect your data and your privacy seriously, and we came up short here.”
It’s not clear what exactly the issue was with its systems, and the scope of the data that was accessed. It’s also not yet known why the company took five months to detect the breach, or if it was contacted by hackers demanding a ransom. TechCrunch asked the company for more details, and we will update our story if we hear back.
Substack did not say how many users are affected. The company said that it doesn’t have any evidence that users’ data is being misused, but did not say what technical means, such as logs, it has to detect evidence of abuse. However, the company asked users to take caution with emails and texts without any particular indicators or direction.
On its website, Substack says that its site has more than 50 million active subscriptions, including 5 million paid subscriptions — a milestone it reached last March. In July 2025, the company raised $100 million in Series C funding led by BOND and The Chernin Group (TCG), with participation from a16z, Klutch Sports Group CEO Rich Paul, and Skims co-founder Jens Grede.
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Tech
Fundamental raises $255M Series A with a new take on big data analysis
An AI lab called Fundamental emerged from stealth on Thursday, offering a new foundation model to solve an old problem: how to draw insights from the huge quantities of structured data produced by enterprises. By combining the old systems of predictive AI with more contemporary tools, the company believes it can reshape how large enterprises analyze their data.
“While LLMs have been great at working with unstructured data, like text, audio, video, and code, they don’t work well with structured data like tables,” CEO Jeremy Fraenkel told TechCrunch. “With our model Nexus, we have built the best foundation model to handle that type of data.”
The idea has already drawn significant interest from investors. The company is emerging from stealth with $255 million in funding at a $1.2 billion valuation. The bulk of it comes from the recent $225 million Series A round led by Oak HC/FT, Valor Equity Partners, Battery Ventures, and Salesforce Ventures; Hetz Ventures also participated in the Series A, with angel funding from Perplexity CEO Aravind Srinivas, Brex co-founder Henrique Dubugras, and Datadog CEO Olivier Pomel.
Called a large tabular model (LTM) rather than a large language model (LLM), Fundamental’s Nexus breaks from contemporary AI practices in a number of significant ways. The model is deterministic — that is, it will give the same answer every time it is asked a given question — and doesn’t rely on the transformer architecture that defines models from most contemporary AI labs. Fundamental calls it a foundation model because it goes through the normal steps of pre-training and fine-tuning, but the result is something profoundly different from what a client would get when partnering with OpenAI or Anthropic.
Those differences are important because Fundamental is chasing a use case where contemporary AI models often falter. Because Transformer-based AI models can only process data that’s within their context window, they often have trouble reasoning over extremely large datasets — analyzing a spreadsheet with billions of rows, for instance. But that kind of enormous structured dataset is common within large enterprises, creating a significant opportunity for models that can handle the scale.
As Fraenkel sees it, that’s a huge opportunity for Fundamental. Using Nexus, the company can bring contemporary techniques to big data analysis, offering something more powerful and flexible than the algorithms that are currently in use.
“You can now have one model across all of your use cases, so you can now expand massively the number of use cases that you tackle,” he told TechCrunch. “And on each one of those use cases, you get better performance than what you would otherwise be able to do with an army of data scientists.”
That promise has already brought in a number of high-profile contracts, including seven-figure contracts with Fortune 100 clients. The company has also entered into a strategic partnership with AWS that will allow AWS users to deploy Nexus directly from existing instances.
