It brought in $1B in revenue last year, surpassing Scale’s $850M.

Entirely bootstrapped. Not a single dollar of VC funding raised.

We talk a ton about how AI is changing how software is written. I think this technology cycle will fundamentally change how companies are funded.

As a caveat, this is not just because of AI, but it is, of course, part of the catalytic forces.

First, a rising taxonomy with new company Archetypes I’ve observed in the past few years:

                      ┌────────TITANS (ARR $150-400B)────────┐
                      │  Alphabet · Apple · Microsoft ·      │
                      │  Meta · Nvidia   (ARR $150-400B)     │
                      └──────────────────────────────────────┘

               ─────── ANCHORS (ARR $3-10B) ───────
                  OpenAI · Anthropic · Snowflake · Databricks
                      

          ─────── LEAN GIANTS (ARR $500M-1B) ───────
             Cursor · Surge  

     ─────── FAST CLIMBERS (ARR $20-100M) ───────
        Supabase · Vercel · Genspark · Gamma ·
        Lovable · Harvey · Glean   

 ─────── SUPER SOLOS (ARR $1-5M) ───────
    Base44 

The Super Solos ($1 - 5M ARR)

Solo founders or ultra-lean teams building with agentic tools like Cursor or Lovable. Typically bootstrapped or lightly funded. Products show early breakout traction and are often acquired within 6 to 12 months for talent, distribution, or product love.

Looks like:

Base44, a prompt-to-code app acquired by Wix for $80M this week. It was generating $3.5M ARR, and reportedly grew to 250,000 users in 6 months. At this stage, the Solo startups tend to be high-growth, low-profitability: Base44 netted out at $189,000 in profit in May on its $3.5M ARR, after covering high LLM token costs.

The Fast-Climbers ($20M - $100M ARR)

Startups that have quickly captured the imagination and attention of the early adopters. Many developers consider them as core to the new software stack. Tends to be highly collaborative within the ecosystem. Fast-growing, but early revenue may not be an indication of long-term user retention as early-adopters can be flaky in loyalty.

Looks like:

Supabase ($20M ARR), Vercel ($20M ARR), Genspark ($36M ARR), Gamma ($50M ARR), Lovable ($75M ARR), Harvey ($75M ARR), Glean ($100M ARR)

*numbers are estimates from what I could find online.

** There are a few in-betweeners like Hugging Face ($130M ARR) and Windsurf ($100M ARR) who could be on their way up to the next tier.

The Lean Giants ($500M - $1B ARR)

Companies operating at scale with surprisingly small teams. Some are heavily venture-funded; others, like Surge, are entirely bootstrapped. They’ve locked in real enterprise traction and are not slowing down any time soon. Typically lean teams, with remarkably high revenue per employee.

Looks like:

Cursor ($500M ARR; ~50 person team; raised >$1B VC funding)

Surge ($1B ARR; ~100 person team; raised $0 VC funding)

The Anchors ($3 - $10B ARR)

These are the foundational players driving the AI cycle. They’re building the infrastructure like models, platforms, protocols, and products that others build on.

Looks like:

OpenAI ($10B ARR), Anthropic ($3B ARR), Snowflake ($3.8B ARR), Databricks ($2.8B ARR)

The Titans ($150B - $350B ARR)

Tech giants dominating capital, distribution, and compute. They’re integrating AI into everything, from chips to cloud to consumer.

Looks like:

Alphabet ($350B ARR), Apple ($400B ARR), Microsoft ($245B ARR), Meta ($170B ARR), Nvidia ($150B ARR)

So where does venture go from here?

The funding landscape has been shifting dramatically post-ZIRP. I’ve compiled some observations, and built in a few predictions on how venture will change over the next decade. In the most radical of my projections, I would say that the majority of VC funds today will be irrelevant in its current form in the next 10 years.

We will see a lot more consolidation in the later rounds, and likely a lot more fragmentation in the early-stages. We will see retail-oriented platforms dominating in late-stage secondaries or pre-IPO trades. We will see more private equity and real estate plays (not just from VC firms) to capitalize on this technology wave.

These predictions are grounded in some data, but still speculative in nature!

1. Late-stage private markets will pull in even more capital.

In the last few years, US liquidity channels have flipped from IPOs to secondaries. As a data point: from 2019 to 2021, US IPO proceeds were about 2 to 4x larger than VC secondaries. By 2024, VC direct secondaries (think, GP-led deals or employee tenders) are now almost double that of the US IPO proceeds, and growing 20% year-on-year.

Whether or not the IPO windows ease up over the next few years, there are clear signs that there is heavy interest and demand from new capital to access the private markets. There’s a heavy emphasis on brand - similar to how “The Magnificent Seven” drives a significant portion of the S&P 500’s performance, secondary transactions are concentrated among a small group of top-tier startups.

Consolidation seems inevitable in this asset class. Capital, especially retail, will likely gravitate towards a handful of well-known venture brands or platforms (e.g. Forge, Hiive, Caplight) that can successfully market to retail investors and private capital.

2. Anchors and Titans will aggressively acquire

Young startups with early distribution, technical brilliance, or viral UX hooks will continue to be acquired quickly, sometimes within months. Others will get copied. The Titans and Anchor archetypes will use balance-sheet cash to snap up promising startups with any sign of distribution, data, or AI talent.

This quarter alone (Q1 2025), there were 550 M&A deals involving venture-backed startups, totaling about $71 billion in exit value - the highest dollar volume since 2021. Most of the high-profile deals were in AI , with 81 AI-related M&A like Wiz ($32B), Ampere Computing ($6.5B), Weights & Biases (~$1.7B).

This pace has continued well into Q2 2025, with acquisitions like Codeium ($3B), Scale ($14.3B), Jony Ive’s I/O ($6B).

PE firms are moving in as well, and ramping up their startup purchases. Crunchbase reports that private equity firms spent more than $56 billion in disclosed-price deals (the real number is likely 5x more).

3. There will be more Lean Giants

Many of them will sell picks and shovels into the AI boom, i.e. serving builders rather than end-users. These may be software plays, or service providers or infrastructure.

We will see lean teams pull outsized revenues per employee. For instance, CoreWeave’s ~$3.5 M revenue per employee is about 30× the median private-SaaS benchmark.

New companies that can move quickly to capitalize on supply-side pull (GPU demand, energy-efficient compute, data-labeling for training) will create multi-billion-dollar niches where small, execution-heavy teams can thrive.

Becoming part of the AI infrastructure flywheel is key for these Lean Giants, as adoption across the ecosystem will make it easier for them to capture value (and a moat) over time, without directly chasing end-user markets.

I also see private equity playing a heavier role here, who aren’t as reticent as venture to lean into services-driven sales. We’re seeing some emergent “venture roll-ups” like acquiring accounting firms and enhance their profit margin with AI. Maybe we’ll see more partnerships between private equity and real estate infrastructure players with VC or tech insiders.

4. New types of financing to serve the Super Solos

The solo SaaS startups (Super Solos making $1 - $5M ARR) isn’t a fit for traditional venture returns, who are looking for billion-dollar breakouts. The truth is, most AI startups today aren’t venture-like, and shouldn’t be overcapitalized to be.

There’s an opportunity for a new breed of funds or financing structures to serve this emerging startup archetype - blending early-stage bets with revenue-share financing. Returns might be capped (say, at 3 - 5x returns), but cash distributions can happen as the company grows. Most revenue-based financing or private credit products start at $200K min ARR - with the rise of solo SaaS, there could be appetite to take earlier bets like in venture, with better economics than RBF.

Just as a back-of-napkin thought exercise:

• Fund size: $50 M, deployed as 500 x $100 k revenue-share checks.

• Terms: 5× cap on each check; monthly rev-share until cap is hit.

Outcomes (assuming 60 % hit the cap in 4 years, 20 % return 2× in 4 years, 20 % fail):

Gross MOIC: 3.4x

Gross IRR (4 years): ~36%

As a comparison, traditional VC could get to 3.4x, but in about 10 years. This is more of a thought exercise, but I’m curious if a capped-return, revenue-share fund can land venture-like multiples with materially faster paybacks, appealing to LPs who are looking for shorter-cycle vehicles and quicker liquidity.

Note from reader Madhu Chamathy:
One suggested addition: revenue-based financing for AI infra plays. The most powerful proof of this shift is CoreWeave's $7.5 billion debt financing, which I believe is one of the largest debt financings ever. Out of their $12 billion, $8 billion is in debt at ~14% interest. Lambda also did $500MM debt financing.

In a way, such AI infra companies are more like utilities vs. software companies (assuming they have predictable revenue, for e.g. from hyperscalers). This kind of capital play is very different from the venture playbook.

5. More private capital into moonshot bets

We are seeing an increasingly informed flow of private capital into moonshot bets that benefit humanity. Like the AI scientist non-profit Future House to Astera (a non-profit foundation focused on life sciences and space), dollars are channeling into big bets that could define the next era of humanity.

This capital is patient and thesis-driven, and might be a much-needed, dynamic complement to federal or academic funding.

Conclusion

It will be more important than ever for investors (GPs and LPs) to stay agile over the next decade. The venture playbook has never been stagnant throughout the decades, and the upcoming cycle will be more transformative than ever. It takes experimentation, boldness, and grounding to capitalize on shifting sands.

If you enjoyed this piece, please share it! and follow our work at Strange Ventures, a first-check firm investing in the future of computing.

Introduction

Long before ink touched paper, human civilizations used the spoken word as a way to store, retrieve, and spread information and knowledge. As one of the most natural ways of human communication, voice will become one of the most intuitive and powerful interfaces for interacting with advanced AI models.

1. Zero UI:
   Voice interactions are an intuitive way to navigate advanced AI models like knowledge graphs (KGs), large language models (LLMs), and more. 
   ‍

2. Human-Machine Feedback Loops:
   ‍Real-time feedback loops enhance the learning curve of these algorithms, paving the way for more personalized and efficient AI experiences.
   ‍

Recent advancements in text-to-speech (TTS) and speech-to-text synthesis are leading to more realistic and human-like Voice AI. In particular, novel approaches like transformers increase abilities for contextual and emotional intelligence — i.e. the nuances of knowing how to say something, as well as novel, multimodal techniques that improve prosody (speech patterns that invoke expressiveness) from the concatenative methods that led to more robotic, choppy, staccato-like speech previously. 

Listen to audio samples from various papers

By 2026, it is estimated that 90% of online content will be synthetically generated. And humans aren’t as good at detecting synthetic voices as we might think. In a 2023 study funded by Dawes Centre for Future Crime, researchers found that humans could only discern deep fakes from human voices 70% of the time in a test environment. The study infers that we’d likely do worse in real-life scenarios. 

Like every technology, Voice AI can be used for positive and more sinister purposes. To master the future, we believe we have to design with it, and this living research report looks into the emergent use-cases, key technical concepts, and forecasts for a technology that looks to become a lot more prevalent in the near future. 

On The Horizon

We may see more of these emergent patterns and trends as Voice AI and its ecosystem matures in its development and adoption.

Personalization at Scale

Gone are the days of the cookie-cutter approach. With Voice AI, businesses are headed towards personalizing their interactions with every customer, increasing touchpoints and opportunities to upsell. Think sales assistants that remember what you last bought or game characters that chat and react to how you play.

Invisible to the Human Ear

AI catches stuff we simply can't. As a quick example: Hedge funds are now using Voice AI to tune into earnings calls. Apart from the usual numbers, this tech picks up on voice changes, hesitations, and even the confidence in an exec's voice, and is used to infer stock market moves. In a different context, teachers in classrooms are using Voice AI to detect early signs of dyslexia in a more efficient way.

Getting the Bigger Picture

This is about getting the whole story—with real-time feedback as events unfold. Contextual intelligence will become part of live diligence and reporting. Think a copilot for “fact checking” during a live congressional hearing or a presidential debate.
‍

Democratizing Access

Perhaps one of the most profound impacts of Voice AI is in the realm of accessibility. Breaking down barriers related to special needs, or language, the technology ensures that information is accessible to all. For individuals with special needs, voice-controlled devices can be life-changing, allowing them to regain the ability to speak or to listen to the written word the way they prefer.

Want to dive deeper? join our slack.

At A Glance

Latest Concepts

Contextual Intelligence

The effectiveness of Voice AI is closely tied to its ability to understand and interpret context. Recent models pair text analysis (what is being said) with audio language models (how it’s being said) to get to more expressive and nuanced speech.
‍

Zero or Few Shot Learning

We are getting closer to zero or few shot learning, which means models can produce convincing output with little training or few samples. This means greater speed in producing voice models, as well as efficiency, such as the case of being able to recreate a voice model from a small dataset of recordings of say, a niche, regional dialect. 

Prosody & Expressiveness

Recent models have made strides in the proper modeling of prosody (i.e. the rhythm, melody, and intonation patterns of spoken language) to achieve more natural and expressive voice synthesis. Techniques include duration modeling, pitch contour generation, and emphasis modeling. This can also be applied to accents, speech patterns and quirks, leading to more accurate voice models and avatars.

Key Papers To Check Out

AudioPaLM (2023)

NaturalSpeech2 (2023)

FastSpeech2 (2021)

Tacotron (2017)

WaveNet (2016)

Current Challenges

There are a few challenges and limitations to the current state of technology in Voice AI, that keeps it in the realm of “uncanny valley”.

Emotional Intelligence

Ensuring that synthesized speech can convey the intended emotion is a significant challenge. It’s still challenging to always correctly detect nuance and subtlety of intentions encoded in information.

Going Multilingual

Especially in low-resource languages (languages with thin data-sets), producing natural-sounding speech is challenging.

Slang or Colloquial speech

Language is an evolving thing, and the variety in colloquial speech, technical jargon, or even regional dialect makes it difficult for generalized models to be accurate.

Deepfakes & Ethics

It can be hard to track or discern voice models, which can be used irresponsibly, or even cloned without notice.

Library of Repos & Tools

A1000.org Audio Samples Comparison

Text-to-Speech Synthesis Benchmark

Emergent Applications

Media, News, Entertainment

Americans spend over 8 hours a day consuming digital content, and it’s only forecasted to grow. In this section, we explore four areas where we see the most potential for Voice AI to disrupt: podcasts, sports, news, and media production.

Strange Hunches: How AI May Supercharge Trends in 2024

• Podcast and audio content consumption is on the rise

  • Over 150M monthly podcast episodes were consumed worldwide in July 2023, doubling from 2021. We believe that we will see even more growth in audio media.

• Dynamic Sports entertainment and betting experiences

  • Sports live streaming and betting experiences will be enhanced with real-time analytics, for instance bringing updated statistics to live sportscasting or betting odds.

• Exponential increase in user-generated and studio content .

  • The pace of media production has accelerated more than twofold. From content generation to transcription to new editing tools, we will see an exponential increase in user-generated and studio content across all channels.

• A “land grab” moment in media and entertainment

  • Media companies will seize the opportunity to expand across mediums, platforms, thematics, and geographies, accelerated by AI.

Notable Case Studies

AI Sportcasters

Major sports tournaments, such as Wimbledon and the Masters, are leveraging AI to provide insights, analysis, and statistics for sports games.

Check out this AI commentary delivered over highlight reels, aided by models built by IBM.

Round-the-Clock Reporting

DeFiance Media partnered with Hour One to automate news content creation, delivering of news stories every 2 hours, all year round.

View their 24/7 news channel.

Film & Audio Post-Production

To continue the legacy of James Earl Jones' portrayal of Darth Vadar past his retirement, Respeecher cloned the actor's voice using archival footage for the Obi-Wan Kenobi series on Disney Plus.

Gaming

Nearly 40% of the world is a gamer. That’s more than one in every three people you meet. And they invest serious time into gaming, with 18% of US gamers playing up to 6 to 10 hours a week. We believe that this trend is set to accelerate with AI, through the creation of more immersive, dynamic, and collaborative gaming experiences that can also safely expand across demographics (like, gaming for kids) and geographies.

Strange Hunches: How AI May Supercharge Trends in 2024

• Immersive Experiences
  Voice AI adds depth to avatars and characters, enhancing the gameplay experience.

• Safer gaming environments
  Live moderation of in-game chats will lead to the safe expansion into markets like the under-18s, and beyond.

• Easy Localization
  Localization through language and narratives is now more accessible to even indie game studios.

Notable Case Studies

Immersive experiences

Interactions with non-player characters (NPCs) are no longer a side-show. AI can create more dynamic and lifelike voice interactions with NPCs, enhancing player engagement, game narrative, and storytelling. 

Get a glimpse of the future possibilities of gaming here.

Localization, Now Within Reach

Localization was previously limited to game-makers with large budgets. Now, even indie game-makers can localize narrations or scenes in multiple languages quickly and cost-effectively. Voices, clues, and narratives can be generated in different languages.

Moderation Makes New Markets

ToxMod, developed by Modulate, is an AI voice moderation tool designed to combat toxicity and extremism in online gaming. Powered by machine learning, it identifies harmful behaviors in gaming voice chats, prioritizing player protection and safer gaming environments.
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"The bonus factor is that there's empirical evidence that strong safety tools pay for themselves - we've found ToxMod lifts platforms' new user retention, for instance, by as much as 10-20%. "
— Mike Pappas, CEO, Modulate

Learning & Development

Education pervades all steps in one’s life, from early development in childhood to continuous learning in your career. The applications for Voice AI in education particularly stood out to us in 3 sectors of the industry: schooling (K-12 and universities), language fluency, and corporate training.

The market size of each vector is massive. Revenue for public and private schools in the US is estimated at over 1 trillion US dollars^1 2. The global English language learning market alone is forecasted to reach a value greater than 35 billion USD by 2030³. And global corporate training was valued at 151.75 billion US dollars in 2021 and is expected to grow to 487.3 billion USD in 2030⁴. The opportunities to make a dent in these areas are vast and exciting.

Strange Hunches: How AI May Supercharge Trends in 2024

• Personalized attention in the classroom

  • Voice AI enhances teachers’ abilities to provide more personalized feedback and assessments of all their students, from language fluency to identifying potential learning difficulties, more efficiently.

• Less effort is required to develop Corporate Training Videos

  • The flexibility to create, edit, and share Voice AI generated content enables companies to build and disseminate essential corporate training modules across different roles and geographies faster.

• Accessibility in educational institutions is becoming a more prevalent topic

  • Text-to-speech AI technology will become increasingly common in educational settings as an alternative method to digest written material.

STAY in the loop for future updates

Notable Case Studies

Dyslexia Identification

EarlyBird Education partnered with SoapBox Labs, a speech recognition company, to enhance its early literacy assessment, including detection of dyslexia.

This voice-based assessment helps educators identify challenges faced by struggling young students before they start reading. Learn more about it here.

Read by Listening

Students, from NYC Public Schools to Barnard College,  who struggle with reading comprehension can listen to textbook chapters from convenient mobile devices on 2x speed while going for a walk, rather than struggling for hours to read through one chapter.

Corporate Training

Voice AI is particularly handy for corporate training. Companies can put together division or regional-specific training videos and lessons with ease by using text to speech technology that does not require extensive recording and editing.

Sales, Marketing, Customer Support

Whether it be when making a purchase (via Alexa or even a restaurant drivethru), listening to an ad, or getting information about a product, you may have interacted with some form of Voice AI without even knowing it. All of these activities hit on core customer-facing activities like sales (making a purchase or upselling), marketing (viewing an ad), and customer support (getting info on a product). Statista estimates that in the US alone 2023 spending on digital advertising will be 271 billion US dollars, up to 325 billion in 2025, with spend specifically on audio advertising increasing to nearly 7 billion in 2025.

Strange Hunches: How AI May Supercharge Trends in 2024

• Customers want to feel “seen”

  • As machine learning continues to advance, advertisements, including a voice ad you might hear on Spotify, will become far more targeted to the specific listener.

• Restaurant drive-thrus are streamlining the process

  • In a constant effort to keep finding new ways to improve the consumer experience, we are starting to see fast food chains implementing Voice AI solutions to free up workers for more challenging customer interactions.

• Advanced customer support

  • Voice AI can take the human bias out of assessing emotion. With a Voice AI companion, sales agents can get additional insight into customer sentiment, helping to find upsell/cross-sell opportunities and a better understanding of customer satisfaction.

Notable case studies

Customer Calls 

Now any business can scale how they chase leads at an exponentially lower cost. 

With the right integrations, Air AI voice calls can analyze ongoing conversations, identify customer sentiments, and even make a sale. 

Check out Air AI's demo of an AI agent's 10 min sales call.

Hyper-relevant Ads

A Bollywood star gives a personal shoutout to thousands of small family-run grocers — a feat only possible with AI.

In 2021, Ogilvy Mumbai cloned Shah Rukh Khan's face and voice to create hundreds of personalized ads in an ad for Cadbury Chocolate.

Drive Thrus

Fast-food giants are getting into the game. Franchises like Wendy’s and White Castle are piloting Voice AIs in drive-thrus. White Castle has set a number of hefty goals, including order completion rates of 90% (greater than current benchmarks by staff) and that an average order gets received and processed in just over 60 seconds.

Looking Forward

This living research report will be updated on a bi-annual basis. Questions, comments, feedback? Let us know at research@strangevc.com.

By Tara Tan and Daniella Levitan
Design by Jacob Waites