DEV Community: ke yi

Character.AI: How a High-Growth Consumer AI Pioneer Is Reshaping the Industry

ke yi — Mon, 02 Nov 2026 07:47:15 +0000

Executive Summary

Character.AI is a Consumer AI company founded in 2021 and headquartered in Menlo Park, CA. Led by Noam Shazeer (Co-Founder), the company has raised a total of $150M across 1 funding round(s), reaching a valuation of $1.0B. AI platform for creating and chatting with AI characters, one of the most popular consumer AI apps.

The AI funding landscape continues to evolve rapidly, with significant capital flowing into companies pushing the boundaries of artificial intelligence. This period's activity reflects both growing investor confidence and the increasing maturity of the ecosystem. Deal sizes and valuations continue to climb as AI transitions from experimental technology to core enterprise infrastructure.

Company Overview

Character.AI is a consumer AI platform that allows users to create and interact with AI characters, from fictional personas to historical figures to custom-built assistants. Founded by Noam Shazeer (co-inventor of the Transformer architecture and a key figure behind Google's LaMDA) and Daniel De Freitas, the company has built one of the most popular consumer AI applications in the world with over 20 million monthly active users. Character.AI's technology enables remarkably engaging conversational experiences that keep users returning for hours of interaction. In a deal similar to Inflection AI's arrangement, Google licensed Character.AI's model technology and hired back co-founders Noam Shazeer and Daniel De Freitas, while the company continues to operate independently with a focus on its consumer platform.

Detail	Value
Founded	2021
Headquarters	Menlo Park, CA
Sector	Consumer AI
Subsector	Conversational AI
Employees	150
Founder	Noam Shazeer (Co-Founder)
Website	https://character.ai

This area of the AI market continues to attract significant investor attention, reflecting the sector's rapid growth and substantial long-term potential.

Funding History

Character.AI has raised a total of $150M across 1 round(s). Below is a complete breakdown of every known funding round.

Round	Amount	Valuation	Date	Lead Investor(s)	All Investors
Series A	$150M	$1.0B	Mar 15, 2023	Andreessen Horowitz	Andreessen Horowitz

Total Capital & Valuation

Total Raised: $150M
Latest Valuation: $1.0B (as of Mar 15, 2023)
Latest Round: Series A — $150M (Mar 15, 2023)
Number of Rounds: 1

The funding trajectory demonstrates accelerating momentum, with round sizes and valuations increasing as key milestones are delivered and market presence expands.

Key Investors

Character.AI has attracted 1 known investor(s) across its funding rounds. Below is a profile of each.

Andreessen Horowitz

Andreessen Horowitz, known as a16z, is a Silicon Valley venture capital firm founded in 2009 by Marc Andreessen and Ben Horowitz. The firm manages over $42 billion across venture, growth, crypto, and infrastructure funds. a16z has become the most active VC in AI, with investments spanning foundation models (Mistral AI, xAI), enterprise AI, robotics (Figure AI), and developer tools (Replit). The firm differentiates through its massive operational team providing portfolio companies with recruiting, marketing, and go-to-market support. a16z also runs one of the most influential tech media operations through its blog and podcast network.

Type: VC
Participated in: Series A
Led: Series A
View investor profile on AI Funding

The investor composition signals strong institutional backing and strategic interest. The presence of repeat investors alongside new participants suggests growing confidence in execution and market trajectory.

Sector Analysis: Consumer AI

Character.AI operates in the Consumer AI sector (specifically Conversational AI). Understanding the broader sector landscape provides context for the company's position and growth potential.

Other companies in the Consumer AI sector tracked by AI Funding:

Company	Founded	Location	Total Raised	Latest Valuation
Inflection AI	2022	Palo Alto, CA	$1.3B	$4.0B
Parade	2026	New York, NY	$4M	N/A
Mega	2026	San Francisco, CA	$12M	N/A

Looking ahead, deal activity is expected to remain strong as major AI companies continue scaling operations and new entrants attract institutional capital. Key areas to watch include frontier model development, enterprise AI adoption, and infrastructure plays that underpin the broader ecosystem.

Competitive Landscape

Character.AI competes with 3 other tracked Consumer AI companyies in our database:

Inflection AI: AI company building personal AI assistants for meaningful conversation and productivity.
Parade: Creator economy marketing platform founded by Cami Tellez, connecting brands with creators for authentic commerce-driven campaigns.
Mega: Building an ambient, privacy-first interface between humans and AI systems for seamless everyday interaction.

At a valuation of $1.0B, Character.AI ranks behind Inflection AI in sector valuation.

The competitive landscape remains dynamic, with established players and well-funded startups vying for market share. Differentiation increasingly comes from technology depth, go-to-market execution, and strategic partnerships that provide distribution advantages.

Revenue & Growth

Revenue run rate data is not publicly available for Character.AI at this time.

Revenue growth has been a key driver of investor interest, with strong product-market fit and expanding enterprise customer adoption underpinning the company's valuation trajectory.

Leadership

Noam Shazeer, Co-Founder

Character.AI was founded in 2021 by Noam Shazeer, who serves as Co-Founder. Under Noam Shazeer's leadership, the company has grown to approximately 150 employees and raised $150M in total funding.

Under current leadership, the company has navigated rapid growth while maintaining technical excellence and a clear strategic vision for the future of AI.

Frequently Asked Questions

How much funding has Character.AI raised?

Character.AI has raised a total of $150M across 1 funding round(s). The most recent round was a Series A of $150M on Mar 15, 2023.

What is Character.AI's valuation?

As of its most recent funding round (Series A, Mar 15, 2023), Character.AI is valued at $1.0B.

Who are Character.AI's investors?

Character.AI's investors include Andreessen Horowitz. These investors have collectively committed $150M across 1 round(s).

When was Character.AI founded?

Character.AI was founded in 2021 by Noam Shazeer and is headquartered in Menlo Park, CA.

What sector does Character.AI operate in?

Character.AI operates in the Consumer AI sector, specifically in Conversational AI. Other companies in this sector include Inflection AI, Parade, Mega.

Who is the CEO of Character.AI?

Noam Shazeer is the Co-Founder of Character.AI.

ElevenLabs: How a High-Growth Creative AI Pioneer Is Reshaping the Industry

ke yi — Sun, 01 Nov 2026 10:38:47 +0000

Executive Summary

ElevenLabs is a Creative AI company founded in 2022 and headquartered in New York, NY. Led by Piotr Dabkowski (CEO & Co-Founder), the company has raised a total of $260M across 2 funding round(s), reaching a valuation of $3.3B. AI voice technology platform for generating realistic speech, dubbing, and audio content.

Company Overview

ElevenLabs is the leading AI voice technology company, building tools that generate remarkably realistic and expressive speech in dozens of languages. The platform's text-to-speech, voice cloning, and AI dubbing capabilities serve content creators, publishers, game developers, and enterprises who need high-quality audio at scale. ElevenLabs has pioneered the ability to maintain emotional nuance, accent, and speaking style in synthetic speech, making its output nearly indistinguishable from human voice actors. The company has grown rapidly, reaching over a million users and significant revenue from its API and consumer products. ElevenLabs' dubbing technology enables seamless translation of video content while preserving the original speaker's voice characteristics, opening a massive market in global content localization.

Detail	Value
Founded	2022
Headquarters	New York, NY
Sector	Creative AI
Subsector	Voice & Audio
Employees	200
Founder	Piotr Dabkowski (CEO & Co-Founder)
Website	https://elevenlabs.io
Revenue Run Rate	$80M+ ARR

This area of the AI market continues to attract significant investor attention, reflecting the sector's rapid growth and substantial long-term potential.

Funding History

ElevenLabs has raised a total of $260M across 2 round(s). Below is a complete breakdown of every known funding round.

Round	Amount	Valuation	Date	Lead Investor(s)	All Investors
Series B	$80M	$1.1B	Jun 20, 2024	Andreessen Horowitz	Andreessen Horowitz, Nat Friedman & Daniel Gross
Series C	$180M	$3.3B	Jan 28, 2026	Andreessen Horowitz	Andreessen Horowitz, Nat Friedman & Daniel Gross, WndrCo

Total Capital & Valuation

Total Raised: $260M
Latest Valuation: $3.3B (as of Jan 28, 2026)
Latest Round: Series C — $180M (Jan 28, 2026)
Number of Rounds: 2

The funding trajectory demonstrates accelerating momentum, with round sizes and valuations increasing as key milestones are delivered and market presence expands.

Key Investors

ElevenLabs has attracted 3 known investor(s) across its funding rounds. Below is a profile of each.

Andreessen Horowitz

Andreessen Horowitz, known as a16z, is a Silicon Valley venture capital firm founded in 2009 by Marc Andreessen and Ben Horowitz. The firm manages over $42 billion across venture, growth, crypto, and infrastructure funds. a16z has become the most active VC in AI, with investments spanning foundation models (Mistral AI, xAI), enterprise AI, robotics (Figure AI), and developer tools (Replit). The firm differentiates through its massive operational team providing portfolio companies with recruiting, marketing, and go-to-market support. a16z also runs one of the most influential tech media operations through its blog and podcast network.

Type: VC
Participated in: Series B, Series C
Led: Series B, Series C
View investor profile on AI Funding

Nat Friedman & Daniel Gross

Nat Friedman (former GitHub CEO) and Daniel Gross (former Apple AI lead and Y Combinator partner) are among the most active and influential angel investors in AI. Together they have backed dozens of leading AI companies including Cursor, Poolside, and numerous other developer tools and infrastructure startups. Their technical credibility and operator experience make them highly sought-after early investors for AI founders.

Type: Angel
Participated in: Series B, Series C
View investor profile on AI Funding

WndrCo

WndrCo is a holding company and venture fund founded by Jeffrey Katzenberg, former chairman of Walt Disney Studios and co-founder of DreamWorks. The fund invests in technology companies at the intersection of media, entertainment, and AI, and has backed companies including ElevenLabs and numerous consumer AI startups.

Type: VC
Participated in: Series C
View investor profile on AI Funding

Sector Analysis: Creative AI

ElevenLabs operates in the Creative AI sector (specifically Voice & Audio). Understanding the broader sector landscape provides context for the company's position and growth potential.

Other companies in the Creative AI sector tracked by AI Funding:

Company	Founded	Location	Total Raised	Latest Valuation
Stability AI	2020	London, UK	$101M	$1.0B

Competitive Landscape

ElevenLabs competes with 1 other tracked Creative AI company in our database:

Stability AI: Open-source generative AI company behind Stable Diffusion and other creative AI models.

At a valuation of $3.3B, ElevenLabs has the highest valuation among its sector peers in our database.

Revenue & Growth

ElevenLabs has a reported revenue run rate of $80M+ ARR. This positions the company as one of the fastest-growing ventures in the Creative AI space.

With a valuation of $3.3B, investors are pricing in significant future growth beyond the current revenue base.

Revenue growth has been a key driver of investor interest, with strong product-market fit and expanding enterprise customer adoption underpinning the company's valuation trajectory.

Leadership

Piotr Dabkowski, CEO & Co-Founder

ElevenLabs was founded in 2022 by Piotr Dabkowski, who serves as CEO & Co-Founder. Under Piotr Dabkowski's leadership, the company has grown to approximately 200 employees and raised $260M in total funding.

Under current leadership, the company has navigated rapid growth while maintaining technical excellence and a clear strategic vision for the future of AI.

Frequently Asked Questions

How much funding has ElevenLabs raised?

ElevenLabs has raised a total of $260M across 2 funding round(s). The most recent round was a Series C of $180M on Jan 28, 2026.

What is ElevenLabs's valuation?

As of its most recent funding round (Series C, Jan 28, 2026), ElevenLabs is valued at $3.3B.

Who are ElevenLabs's investors?

ElevenLabs's investors include Andreessen Horowitz, Nat Friedman & Daniel Gross, WndrCo. These investors have collectively committed $260M across 2 round(s).

When was ElevenLabs founded?

ElevenLabs was founded in 2022 by Piotr Dabkowski and is headquartered in New York, NY.

What sector does ElevenLabs operate in?

ElevenLabs operates in the Creative AI sector, specifically in Voice & Audio. Other companies in this sector include Stability AI.

What is ElevenLabs's revenue?

ElevenLabs has a reported revenue run rate of $80M+ ARR.

Who is the CEO of ElevenLabs?

Piotr Dabkowski is the CEO & Co-Founder of ElevenLabs.

Top AI Consumer Hardware Startups in 2026: Funding, Valuations & Market Leaders

ke yi — Sun, 01 Nov 2026 10:38:46 +0000

Overview

The AI Consumer Hardware sector is one of the most dynamic areas of AI investment in 2026. This report profiles the top-funded startups in the space, their investors, and the market dynamics driving growth.

Sector at a Glance

Metric	Value
Companies Tracked	3
Total Funding	$150M
Average Latest Valuation	N/A
Sector Page	/sector/ai-consumer-hardware

Top AI Consumer Hardware Startups by Total Funding

1. Temple

Total Raised: $54M
Latest Valuation: Undisclosed
Latest Round: friends-and-family ($54M)
Founded: 2026 | HQ: Bengaluru, India
Key Investors: Undisclosed
Company Page: /companies/temple

Brain-monitoring wearable startup founded by Zomato's Deepinder Goyal, building consumer neurotechnology for cognitive health tracking.

2. Eight Sleep

Total Raised: $50M
Latest Valuation: Undisclosed
Latest Round: Undisclosed ($50M)
Founded: 2026 | HQ: New York, NY
Key Investors: Undisclosed
Company Page: /companies/eight-sleep

Smart sleep technology company building AI-powered temperature-controlled mattress systems that optimize rest and recovery by tracking biometrics and auto-adjusting climate.

3. Sandbar

Total Raised: $46M
Latest Valuation: Undisclosed
Latest Round: Undisclosed ($23M)
Founded: 2026 | HQ: San Francisco, CA
Key Investors: Undisclosed
Company Page: /companies/sandbar

AI-powered wearable technology company building Stream, a private voice ring device for capturing thoughts and notes via push-to-talk with AI-powered organization.

Most Active Investors in AI Consumer Hardware

Rank	Investor	Companies Backed
1	Undisclosed	3

This area of the AI market continues to attract significant investor attention, reflecting the sector's rapid growth and substantial long-term potential.

AI Consumer Hardware Trends to Watch

This area of the AI market continues to attract significant investor attention, reflecting the sector's rapid growth and substantial long-term potential.

Data sourced from AI Funding's funding database. Rankings based on total disclosed funding. This scaffold was generated with AI Funding's content tools and may require editorial review before publication.

Rhoda: How a High-Growth AI Robotics Pioneer Is Reshaping the Industry

ke yi — Fri, 30 Oct 2026 08:46:29 +0000

Executive Summary

Rhoda is a AI Robotics company founded in 2024 and headquartered in San Francisco, CA. The company has raised a total of $450M across 1 funding round(s). Rhoda builds foundation models for industrial robots, trained on web-scale video data to enable robots to perform complex manipulation tasks autonomously. Their approach of learning general-purpose robotic skills from visual data, rather than task-specific programming, represents a paradigm shift in industrial automation development.

Company Overview

Rhoda develops advanced robotic systems powered by foundation models trained on web-scale video data to perform complex industrial tasks autonomously. Backed by Khosla Ventures, the company works with customers across automotive, manufacturing, logistics, and ecommerce sectors to deploy AI-powered robotic automation.

Detail	Value
Founded	2024
Headquarters	San Francisco, CA
Sector	AI Robotics
Subsector	Computer Vision
Employees	N/A
Founder	Not disclosed
Website	https://rhoda.ai

This area of the AI market continues to attract significant investor attention, reflecting the sector's rapid growth and substantial long-term potential.

Funding History

Rhoda has raised a total of $450M across 1 round(s). Below is a complete breakdown of every known funding round.

Round	Amount	Valuation	Date	Lead Investor(s)	All Investors
Series A	$450M	N/A	Mar 11, 2026	Khosla Ventures	Khosla Ventures

Total Capital & Valuation

Total Raised: $450M
Latest Round: Series A — $450M (Mar 11, 2026)
Number of Rounds: 1

The funding trajectory demonstrates accelerating momentum, with round sizes and valuations increasing as key milestones are delivered and market presence expands.

Key Investors

Rhoda has attracted 1 known investor(s) across its funding rounds. Below is a profile of each.

Khosla Ventures

Khosla Ventures is a venture capital firm founded in 2004 by Vinod Khosla, co-founder of Sun Microsystems. The firm invests across clean energy, AI, healthcare, and frontier technology with over $15 billion in assets under management. Khosla Ventures led Glean's Series E and has backed Replit, reflecting the firm's deep conviction in AI's transformative potential for enterprise productivity and software development. Vinod Khosla is known for his bold, thesis-driven investing style and willingness to fund ambitious moonshots. The firm invests from seed through growth stage and maintains a highly concentrated portfolio approach.

Type: VC
Participated in: Series A
Led: Series A
View investor profile on AI Funding

Sector Analysis: AI Robotics

Rhoda operates in the AI Robotics sector (specifically Computer Vision). Understanding the broader sector landscape provides context for the company's position and growth potential.

Other companies in the AI Robotics sector tracked by AI Funding:

Company	Founded	Location	Total Raised	Latest Valuation
Figure AI	2022	Sunnyvale, CA	$754M	$2.6B
Mirai Robotics	Not disclosed	Bari, Italy	$4M	Not disclosed
Mind Robotics	Not disclosed	San Francisco, CA	$500M	$3.3B
Sunday	2023	San Francisco, CA	$165M	$1.1B
RoboForce	Not disclosed	San Francisco, CA	$52M	Not disclosed
Eternal.ag	Not disclosed	San Francisco, CA	$8M	Not disclosed
Rivian	2009	Irvine, CA	$16.9B	Not disclosed
Stateful Robotics	Not disclosed	Oxford, UK	$5M	$32M
Lucid Bots	Not disclosed	San Francisco, CA	$20M	Not disclosed
Nomadic	Not disclosed	San Francisco, CA	$8M	$56M

Competitive Landscape

Rhoda competes with 10 other tracked AI Robotics companyies in our database:

Figure AI: Building general-purpose humanoid robots powered by AI for commercial and industrial workforces. Partnered with BMW and OpenAI to deploy autonomous humanoids in real-world manufacturing environments.
Mirai Robotics: Physical AI company building autonomous maritime systems with dock-to-dock autonomy for defense surveillance, commercial offshore operations, and logistics.
Mind Robotics: Intelligent robotics company developing collaborative robots for industrial manufacturing, leveraging production-scale data from a strategic partnership with Rivian.
Sunday: Home robotics company building humanoid robots designed for household tasks including dishwashing, cleaning, and domestic chores. Sunday is developing affordable general-purpose home robots that use advanced manipulation and perception AI to navigate real kitchen and living environments, aiming to bring practical robotic assistance into everyday homes rather than just factories and warehouses.
RoboForce: Builds AI-driven robots to carry out dirty, dangerous factory tasks, reducing human exposure to hazardous industrial work and downtime risks.
Eternal.ag: Builds autonomous robots and accompanying software for commercial greenhouse operations to automate monitoring, tending and data-driven crop cultivation.
Rivian: Produces electric vehicles and partners with mobility platforms to support robotaxi and other autonomous mobility initiatives.
Stateful Robotics: Develops advanced robotic systems that utilize artificial intelligence to learn from experiences, maintain long-term memory, and adapt to dynamic environments for improved operational efficiency.
Lucid Bots: Develops specialized autonomous drone technology designed to automate window-washing and exterior maintenance tasks for commercial buildings and high-rise structures.
Nomadic: Provides data-wrangling software and infrastructure to collect, process and manage the large volumes of data produced by autonomous vehicles.

Revenue & Growth

Revenue run rate data is not publicly available for Rhoda at this time.

Revenue growth has been a key driver of investor interest, with strong product-market fit and expanding enterprise customer adoption underpinning the company's valuation trajectory.

Leadership

Leadership Team

Under current leadership, the company has navigated rapid growth while maintaining technical excellence and a clear strategic vision for the future of AI.

Frequently Asked Questions

How much funding has Rhoda raised?

Rhoda has raised a total of $450M across 1 funding round(s). The most recent round was a Series A of $450M on Mar 11, 2026.

Who are Rhoda's investors?

Rhoda's investors include Khosla Ventures. These investors have collectively committed $450M across 1 round(s).

When was Rhoda founded?

Rhoda was founded in 2024 by and is headquartered in San Francisco, CA.

What sector does Rhoda operate in?

Rhoda operates in the AI Robotics sector, specifically in Computer Vision. Other companies in this sector include Figure AI, Mirai Robotics, Mind Robotics, Sunday, RoboForce, Eternal.ag, Rivian, Stateful Robotics, Lucid Bots, Nomadic.

Gumloop AI Agent Startup Funding 2026: Series A & Growth

ke yi — Thu, 29 Oct 2026 02:34:47 +0000

TL;DR

Gumloop is an AI developer tools startup founded in 2023 in San Francisco that has raised $70M across 3 funding rounds. The company offers a no-code visual drag-and-drop platform enabling non-technical teams to build and deploy AI agents for business automation. Gumloop's platform democratizes agentic automation by connecting to existing tools and APIs, targeting operations, marketing, and support teams across the enterprise.

Frequently Asked Questions

How is GV (Google Ventures) connected to AI agent startup investments?

Gumloop is part of the rapidly growing AI agent startup ecosystem attracting significant venture capital in 2025-2026. The company has raised $70M total across 3 rounds, reflecting the broader trend of major investors backing AI automation platforms. Capital continues flowing into AI infrastructure and developer tools as the technology transitions from experimental to core enterprise infrastructure.

What AI agent startups received funding in July 2026?

Gumloop is a notable AI agent startup that has raised $70M in total funding across 3 rounds, positioning it among the well-funded companies in the AI automation space. The company's no-code platform for building AI agents represents the type of agentic AI infrastructure attracting significant Series A and growth-stage investment in 2026. Deal sizes and valuations in the AI agent category continue to climb as investor confidence grows.

How much funding are AI agent startups raising in 2026?

AI agent startups like Gumloop are raising substantial capital, with Gumloop securing $70M across 3 funding rounds since its 2023 founding. The AI funding landscape in 2026 reflects increasing maturity of the ecosystem, with significant capital flowing into companies building AI automation and agentic platforms. B2B SaaS AI infrastructure companies targeting enterprise use cases are particularly well-positioned to attract $200M-500M fund investments in this cycle.

Executive Summary

Gumloop is a AI Developer Tools company founded in 2023 and headquartered in San Francisco, CA. The company has raised a total of $70M across 3 funding round(s). AI automation platform enabling teams to build and deploy AI agents for business tasks without coding expertise. Gumloop provides a visual drag-and-drop builder where operations, marketing, and support teams can create sophisticated AI-powered workflows that connect to existing tools and APIs, democratizing access to agentic automation for non-technical users across the enterprise.

Company Overview

Gumloop is an AI automation framework that enables teams to build and deploy AI agents for business workflows. The platform provides pre-built agent templates for data analysis, customer support, CRM, and more, integrating with tools like Slack, Salesforce, and Google Workspace. Gumloop raised a $50M Series B led by Benchmark in March 2026.

Detail	Value
Founded	2023
Headquarters	San Francisco, CA
Sector	AI Developer Tools
Subsector	AI Automation
Employees	N/A
Founder	Not disclosed
Website	https://gumloop.com

This area of the AI market continues to attract significant investor attention, reflecting the sector's rapid growth and substantial long-term potential.

Funding History

Gumloop has raised a total of $70M across 3 round(s). Below is a complete breakdown of every known funding round.

Round	Amount	Valuation	Date	Lead Investor(s)	All Investors
Seed	$3M	N/A	Jul 23, 2024	First Round Capital	First Round Capital
Series A	$17M	N/A	Jan 10, 2025	Undisclosed	Undisclosed
Series B	$50M	N/A	Mar 12, 2026	Benchmark	Benchmark

Total Capital & Valuation

Total Raised: $70M
Latest Round: Series B — $50M (Mar 12, 2026)
Number of Rounds: 3

The funding trajectory demonstrates accelerating momentum, with round sizes and valuations increasing as key milestones are delivered and market presence expands.

Key Investors

Gumloop has attracted 3 known investor(s) across its funding rounds. Below is a profile of each.

First Round Capital

First Round Capital is an investment firm.

Type: VC
Participated in: Seed
Led: Seed
View investor profile on AI Funding

Undisclosed

Undisclosed is an investor in AI companies.

Type: VC
Participated in: Series A
View investor profile on AI Funding

Benchmark

Benchmark is an iconic Silicon Valley venture capital firm founded in 1995, known for its unique equal partnership structure where all partners share equally in economics and decision-making. The firm manages modestly-sized funds of around $425 million each, maintaining discipline and focus at the early stage. Benchmark's portfolio includes some of the most successful technology companies in history: eBay, Twitter, Uber, Snapchat, Instagram, and Discord. The firm led Lovable's Series B, betting on the AI-powered app development revolution. Benchmark partners take active board seats and are known for their deep operational involvement with portfolio companies, often serving as the most trusted advisor to founding CEOs.

Type: VC
Participated in: Series B
Led: Series B
View investor profile on AI Funding

Sector Analysis: AI Developer Tools

Gumloop operates in the AI Developer Tools sector (specifically AI Automation). Understanding the broader sector landscape provides context for the company's position and growth potential.

Other companies in the AI Developer Tools sector tracked by AI Funding:

Company	Founded	Location	Total Raised	Latest Valuation
Lovable	2023	Stockholm, Sweden	$217M	$2.8B
Replit	2016	San Francisco, CA	$705M	$9.0B
Cursor	2022	San Francisco, CA	$968M	$9.9B
Poolside	2023	New York, NY	$652M	$3.0B
Axiomatic AI	Not disclosed	Cambridge, MA	$18M	Not disclosed
Railway	Not disclosed	San Francisco, CA	$120M	Not disclosed
ChipAgents	Not disclosed	San Jose, CA	$50M	Not disclosed
Code Metal	2023	San Francisco, CA	$178M	Not disclosed
Reson8	Not disclosed	San Francisco, CA	$5M	Not disclosed
Hyground	Not disclosed	Hamburg, Germany	$3M	Not disclosed
Endform	Not disclosed	San Francisco, CA	$2M	Not disclosed
Gitar	Not disclosed	San Francisco, CA	$9M	Not disclosed
InsightFinder	Not disclosed	San Francisco, CA	$15M	Not disclosed
Antioch	Not disclosed	San Francisco, CA	$9M	Not disclosed
Dreambase	Not disclosed	San Francisco, CA	$4M	Not disclosed
ManaMind	Not disclosed	San Francisco, CA	$2M	$10M
CopilotKit	Not disclosed	San Francisco, CA	$27M	Not disclosed
CodeWords	Not disclosed	London, UK	$9M	Not disclosed
London-based CodeWords	Not disclosed	London, UK	$9M	$45M
Zerops	Not disclosed	San Francisco, CA	$2M	Not disclosed
SiliXon	Not disclosed	Europe	$2M	Not disclosed
Avrea	Not disclosed	San Francisco, CA	$5M	$31M
Cognition	Not disclosed	San Francisco, CA	$1.2B	Not disclosed
IMU Biosciences	Not disclosed	London, UK	$53M	Not disclosed
Supabase	Not disclosed	New Zealand	$500M	$3.3B
Niteshift	Not disclosed	San Francisco, CA	$7M	Not disclosed
repo	Not disclosed	San Francisco, CA	$7M	Not disclosed
Undo	Not disclosed	Cambridge, United Kingdom	$37M	Not disclosed
GitHits	Not disclosed	San Francisco, CA	$2M	Not disclosed
Devplan	Not disclosed	San Francisco, CA	$3M	Not disclosed

Competitive Landscape

Gumloop competes with 30 other tracked AI Developer Tools companyies in our database:

Lovable: Lovable raised $200M Series B at $2.8B valuation in June 2026 — the AI app builder that turns natural language into full-stack web apps. Founded by Anton Osika, backed by top VCs, $50M+ ARR. Complete funding history, investor breakdown, and product roadmap.
Replit: AI-powered software development and deployment platform enabling anyone to build and ship applications from the browser. Replit's AI agent can generate, debug, and deploy full-stack apps from natural language prompts, backed by a cloud IDE with instant hosting. Used by millions of developers and learners, Replit is a pioneer in democratizing software creation through AI.
Cursor: AI-first code editor built to supercharge developer productivity with intelligent autocomplete, chat, and deep codebase understanding. Forked from VS Code, Cursor integrates frontier LLMs directly into the editing experience for code generation, refactoring, and debugging. It has become the fastest-adopted AI coding tool among professional developers, reaching $100M+ ARR in record time.
Poolside: AI code-generation startup raised $500M+ in 2026 to rival Cursor & Copilot. See Poolside's latest funding round details, investors, and valuation.
Axiomatic AI: MIT spin-off building verified AI operators for engineering workflows using formal mathematical proofs, serving photonics, electronics, thermal, mechanics, and signal domains.
Railway: AI infrastructure funding tracker: Railway's $30M round and the latest AI funding rounds shaping 2025–2026 investment trends. See all recent deals.
ChipAgents: AI-powered platform that accelerates chip design and verification through agentic AI agents, making engineers approximately 10x faster at RTL design and debugging.
Code Metal: Verifiable code translation platform that automates conversion of code between programming languages and platforms with formal verification for correctness.
Reson8: Develops speech-focused AI solutions positioned as a response to Europe's perceived shortage in speech technology and capabilities.
Hyground: Develops autonomous AI site reliability engineering agents designed to automate infrastructure monitoring and maintenance for large-scale enterprise environments and critical transportation networks.
Endform: Automates web application testing with AI-driven workflows that reduce test execution time from around 30 minutes to approximately 2 minutes.
Gitar: Builds agent-based tools that secure code by detecting, triaging, and mitigating vulnerabilities across development workflows and CI/CD pipelines.
InsightFinder: Builds diagnostic and observability tooling that helps companies detect, diagnose and trace failures in AI agents so teams can see where automated systems go wrong.
Antioch: Offers a simulation platform for training and testing physical AI systems, aiming to be a developer-focused 'Cursor' for real-world AI.
Dreambase: Provides developer-focused tools and platforms to accelerate AI application development for engineering teams and startups.
ManaMind: Develops AI agents that automate playtesting and QA for video games by simulating diverse player behaviors to find bugs and optimize gameplay.
CopilotKit: Open-source AI copilot framework for in-app agents — raised $4.5M to help developers embed custom AI assistants. Latest funding details inside.
CodeWords: Provides developer tools that help businesses run on AI autopilot, enabling automation and orchestration of AI-driven workflows for operational teams.
CodeWords: Provides a developer platform that helps businesses automate operations and run on AI autopilot across enterprise workflows.
Zerops: Provides a platform to prevent code breakages between development and production, improving release reliability and reducing deployment risk for software teams.
SiliXon: Develops AI prompt-based tools for circuit board design to streamline electronics development and support manufacturing in Europe.
Avrea: Builds a GitHub competitor for software development teams, operating in the AI developer tools market.
Cognition: Develops AI coding tools aimed at helping software teams automate and accelerate programming workflows for software development.
IMU Biosciences: Blood-based immune profiling startup decoding immunity from a single sample. Latest 2026 funding round details, biotech AI sector insights inside.
Supabase: Open-source database and backend platform powering AI coding tools, with explosive growth driven by Claude Code and the $12B+ AI infrastructure funding wave.
Niteshift: AI coding startup Niteshift raised $4.5M in June 2026 to break Big AI platform lock-in. Full funding round details, investors, and what's next.
repo: Provides an open-source AI tooling product for developers, with a reported Seed raise before the project was archived overnight.
Undo: Builds runtime context tooling that helps AI-assisted software engineering systems understand and debug software behavior.
GitHits: Builds open-source AI software and is using its pre-seed capital to support product development.
Devplan: Develops software aimed at reducing coordination costs for teams deploying and managing AI workflows across organizations.

Revenue & Growth

Revenue run rate data is not publicly available for Gumloop at this time.

Revenue growth has been a key driver of investor interest, with strong product-market fit and expanding enterprise customer adoption underpinning the company's valuation trajectory.

Leadership

Leadership Team

Under current leadership, the company has navigated rapid growth while maintaining technical excellence and a clear strategic vision for the future of AI.

Frequently Asked Questions

How much funding has Gumloop raised?

Gumloop has raised a total of $70M across 3 funding round(s). The most recent round was a Series B of $50M on Mar 12, 2026.

Who are Gumloop's investors?

Gumloop's investors include First Round Capital, Undisclosed, Benchmark. These investors have collectively committed $70M across 3 round(s).

When was Gumloop founded?

Gumloop was founded in 2023 by and is headquartered in San Francisco, CA.

What sector does Gumloop operate in?

Gumloop operates in the AI Developer Tools sector, specifically in AI Automation. Other companies in this sector include Lovable, Replit, Cursor, Poolside, Axiomatic AI, Railway, ChipAgents, Code Metal, Gradient, Reson8, Hyground, Endform, Gitar, InsightFinder, Antioch, Dreambase, ManaMind, CopilotKit, CodeWords, London-based CodeWords, Zerops, SiliXon, Avrea, Cognition, IMU Biosciences, Supabase, Niteshift, repo, Undo, GitHits, Devplan.

AI Series A Funding 2026: Timeline, Top Rounds & Investors

ke yi — Wed, 28 Oct 2026 23:29:47 +0000

AI Series A funding is the first institutional priced round an AI startup raises after seed, typically to turn a working product into a repeatable business. In 2026 it has also become a launch vehicle for nine-figure rounds.

TL;DR

AI Funding tracked 221 AI Series A rounds dated February to October 28, 2026, totalling $8.38 billion, with a median round of $20 million. The 17 rounds of $100 million or more account for $3.58 billion, or 43% of all Series A capital, so the average round ($37.9 million) is almost double the median. The largest source-confirmed AI Series A rounds of 2026 are Hark's $700 million (led by Parkway Venture Capital) and Mind Robotics' $500 million (co-led by Accel and Andreessen Horowitz).

Key Takeaways

221 AI Series A rounds, $8.38B in total: AI Funding's dataset records this between February and October 28, 2026, with a median of $20M and a mean of $37.9M.
The top 10 rounds hold 34% of the capital: $2.85B of $8.38B went to just 10 companies, led by Hark ($700M) and Mind Robotics ($500M).
Most Series A rounds are still ordinary in size: 109 of the 221 rounds (49%) fall between $10M and $25M, and only 17 (7.7%) reach $100M.
Enterprise AI leads on deal count, robotics on round size: Enterprise AI took 70 rounds and $2.01B, while 12 AI Robotics rounds raised $1.29B.
Andreessen Horowitz is the most active named Series A investor: it appears on 9 rounds and led 8 of them. Lightspeed Venture Partners led all 5 of the Series A rounds it joined.
June and July were the busiest months: they logged 42 and 52 rounds, but March raised the most money ($2.12B) because of Mind Robotics' $500M round.

What counts as an AI Series A round?

A Series A is the first priced equity round led by an institutional investor after a company's seed or pre-seed financing. For AI startups the round usually funds the move from pilot customers to a sales motion, and for model or hardware companies it funds the first serious compute or manufacturing budget.

This analysis counts every round labelled Series A in AI Funding's database with a date from February to October 28, 2026. It excludes Series A Extension, Series A+, Pre-Series A and combined Seed and Series A labels, which together cover 9 more rounds. A duplicate record for Poetic, which repeated its $50M Series A as $500M, is also excluded; the source reports $50M. Our ingestion pipeline recorded its first 2026 Series A round on February 18, so January and early February are not covered. The figures describe the deals we track, not every AI Series A worldwide.

How did AI Series A funding change month by month in 2026?

The month-by-month timeline shows a market that is busy in volume but driven by a handful of very large rounds in its dollar totals.

Month (2026)	Rounds	Total Raised	Median Round	Largest Round
February	4	$144M	$36.5M	Humand ($66M)
March	32	$2.12B	$20.0M	Mind Robotics ($500M)
April	19	$536M	$20.0M	Sygaldry Technologies ($139M)
May	26	$1.24B	$19.0M	Hark ($700M)
June	42	$1.40B	$22.5M	Bionyra Pharma ($165M)
July	52	$1.35B	$18.5M	Prime Intellect ($130M)
August	10	$314M	$30.0M	Infinimmune ($75M)
September	21	$503M	$22.0M	Conveo ($50M)
October (to 28th)	15	$772M	$32.0M	doubleAI ($200M)*

*doubleAI's $200M round is recorded as Series A in our data, but the source article does not name the stage.

The median has held between roughly $18M and $37M every month, even as monthly totals swung from $144M to $2.12B. That is the clearest sign that the headline numbers for AI Series A are set by outliers. March and May together raised $3.36B, 40% of the nine-month total, mostly because of two rounds.

By half-year, the first half (February to June) logged 123 rounds and $5.44B. The second half so far (July to October 28) logged 98 rounds and $2.93B. The median barely moved, from $20.0M to $20.7M. The fall in total dollars comes from fewer $100M-plus rounds, not from smaller typical rounds.

Which were the largest AI Series A rounds of 2026?

These are the largest Series A rounds in our 2026 data whose round size and stage we checked against the cited source article.

Company	Date	Series A	Lead Investor(s)	Sector
Hark	2026-05-22	$700M	Parkway Venture Capital	Consumer AI
Mind Robotics	2026-03-12	$500M	Accel, Andreessen Horowitz	AI Robotics
Axiom	2026-03-13	$200M	Menlo Ventures	AI Security
Starcloud	2026-03-30	$170M	Benchmark, EQT Ventures	AI Infrastructure
TerraFirma	2026-07-15	$100M ($115M total raised)	Kleiner Perkins	Enterprise AI
General Compute	2026-10-27	$100M	Blackbird Ventures, Abstract Ventures	AI Infrastructure

Three other rounds of $200M or more appear in our data with weaker confirmation. Rhoda ($450M, Khosla Ventures) has no primary source on record. doubleAI ($200M) is recorded as Series A, but the source does not name the stage. Eridu emerged from stealth with more than $200M in total funding, and its report does not break out the Series A portion.

The verified list has one pattern in common: founder or parent-company pedigree. TechCrunch reports that Hark was founded by Brett Adcock, who also founded Figure AI and Archer, and that he seeded it with $100M of his own money before the Series A. Mind Robotics was spun out of Rivian in November 2025 and is chaired by Rivian founder RJ Scaringe. For these companies, "Series A" is a label for the first outside institutional round, not a sign of an early-stage risk profile.

The other common thread is capital-intensive products. Starcloud builds data centres for orbit, and its first satellite carried an Nvidia H100. General Compute runs a cloud on non-Nvidia inference chips. Axiom trains models that produce formally verified output in the Lean proof language. Each of these companies needs hardware or compute budgets that a $20M round cannot cover.

How big is a typical AI Series A round in 2026?

The typical AI Series A round in 2026 is $10M to $25M. Nearly half of all tracked rounds fall in that band.

Round Size	Rounds	Share of Rounds
Under $10M	26	11.8%
$10M to $25M	109	49.3%
$25M to $50M	50	22.6%
$50M to $100M	19	8.6%
$100M and above	17	7.7%

83.7% of AI Series A rounds in 2026 were under $50M, yet the 17 rounds of $100M or more raised $3.58B, 43% of the total. A founder benchmarking a raise should use the $20M median, not the $37.9M mean. Startups citing mega-round headlines are comparing themselves with companies whose founders, compute needs or corporate parents put them in a different category.

Which AI sectors raised the most Series A funding?

Sector	Rounds	Total Raised	Median Round
Enterprise AI	70	$2.01B	$20.0M
AI Robotics	12	$1.29B	$27.0M
AI Infrastructure	18	$0.94B	$30.5M
Deep Tech	27	$0.88B	$18.5M
Consumer AI	8	$0.87B	$22.4M
AI Security	14	$0.48B	$19.0M
AI Healthcare	23	$0.46B	$16.2M
AI Fintech	18	$0.44B	$20.0M
AI Developer Tools	7	$0.37B	$27.0M
Climate Tech	10	$0.20B	$16.3M

Enterprise AI is the volume leader: 32% of all Series A rounds, at a median right on the market's $20M. AI Robotics is the most concentrated sector. Twelve rounds raised $1.29B, but Mind Robotics alone supplied $500M of it. AI Infrastructure has the highest median ($30.5M) of any sector with more than 10 rounds, which fits its hardware-heavy cost base. Consumer AI's $0.87B is almost entirely Hark. Without that round, the other seven consumer Series A rounds raised about $170M.

AI Healthcare has the lowest median of the larger sectors ($16.2M) across 23 rounds. Clinical and regulatory milestones, not compute, set the pace of capital needs there.

Who are the most active AI Series A investors in 2026?

Investor	Series A Rounds	As Lead	Notable 2026 Series A Deals
Andreessen Horowitz	9	8	Mind Robotics ($500M), Tessera Labs ($60M), Lassie ($35M)
Y Combinator	6	0	Humand ($66M), Glimpse ($35M), Prosper AI ($30M)
Lightspeed Venture Partners	5	5	Isembard ($50M), Nuance Labs ($50M), Sandstone ($30M)
Kleiner Perkins	4	3	TerraFirma ($100M Series A), Poetic ($50M), Phia ($35.5M)
General Catalyst	4	0	Tsuga ($35M), Geordie AI ($30M), Stacks ($23M)
Plural	4	3	Ore Energy ($43M), Isometric ($40M), CircuitHub ($28M)
Menlo Ventures	3	3	Axiom ($200M), Gimlet Labs ($80M), Nectar Social ($30M)
Index Ventures	3	3	Conduct ($60M), Parallel ($20M), Adfin ($18M)
Accel	3	2	Mind Robotics ($500M), Viktor ($75M), Tracebit ($20M)

Two different strategies show up here. Andreessen Horowitz, Lightspeed, Menlo and Index lead almost every Series A they join. Of the 20 rounds those four firms joined between them, they led 19. Y Combinator and General Catalyst mostly follow on. YC appears on 6 Series A rounds because it holds pro-rata rights from its seed cheques in its own alumni, not because it prices the round.

Plural, the European fund, stands out with four Series A rounds concentrated in climate and industrial AI (Ore Energy, Isometric, Gigaton, CircuitHub). It is the only firm in the top tier with a clear sector thesis at Series A.

Disclosure is a major gap. 70 of the 221 rounds (31.7%) name no investor at all in our records. That share includes some of 2026's largest, such as Prime Intellect ($130M) and Foundry ($100M). Investor league tables at Series A are therefore lower bounds.

What does 2026 tell founders raising an AI Series A?

Four points stand out in the data:

Benchmark against the median. $20M is the 2026 AI Series A median, and the middle half of rounds sits between roughly $13M and $35M.
Mega Series A rounds are a separate market. Every source-verified round of $100M or more went to a company with a hardware, compute or heavy-model cost base (Starcloud, General Compute, Axiom, TerraFirma's remote-operated machinery) or with a famous founder or corporate parent behind it (Hark, Mind Robotics).
Lead-heavy firms are the ones to target. Andreessen Horowitz, Lightspeed, Menlo and Index led 19 of the 20 Series A rounds they joined. Getting one of them to price the round sets the rest of the syndicate.
Second-half volume is down, not prices. The July–October period has fewer $100M-plus rounds, but the median round size held at about $20M.

FAQ

How much is a typical AI Series A round in 2026?

The median AI Series A round in 2026 is $20 million across 221 rounds tracked by AI Funding from February to October 28. About 49% of rounds fall between $10M and $25M, and only 7.7% reach $100M or more.

What is the largest AI Series A round of 2026?

Hark's $700 million Series A, announced in May 2026 and led by Parkway Venture Capital with Nvidia, AMD Ventures, ARK Invest and others participating, is the largest source-confirmed AI Series A in our 2026 data. Mind Robotics' $500 million round, co-led by Accel and Andreessen Horowitz, is second.

Which VCs lead the most AI Series A rounds?

Andreessen Horowitz led 8 of the 9 AI Series A rounds it joined in 2026, the most of any firm. Lightspeed Venture Partners led all 5 of its Series A rounds, and Menlo Ventures and Index Ventures each led 3.

Which AI sector gets the most Series A funding?

Enterprise AI received the most AI Series A funding in 2026, with 70 rounds totalling $2.01 billion. AI Robotics ranked second on dollars ($1.29B across 12 rounds), driven mainly by Mind Robotics' $500M round.

Sources

Every 2026 AI Series A round in this analysis is listed with its investors and source on the AI Funding deals feed and company pages at aifunding.me, which update as new rounds are ingested.

AI Agent Regression Testing: Production Traces to CI

ke yi — Wed, 28 Oct 2026 19:29:55 +0000

AI Agent Regression Testing: How to Turn Production Traces into CI Gates

TL;DR: Trace-based regression testing turns a failing production agent trace into a CI test case. The trace's recorded tool and model outputs become fixtures, so the test replays offline and deterministically on every pull request. Tracely is an MIT-licensed open-source project that packages this loop: OTLP ingest, graders, failure clustering, promotion of a trace to a case, and a GitHub Action gate. Langfuse, LangSmith and Promptfoo cover parts of the same loop. None of them removes the main engineering cost, which is routing every tool and model call through a seam you can record and replay.

Key Takeaways

An agent regression test is a test built from a real failure. A trace that went wrong in production becomes a case your CI must pass forever after.
Hermetic replay is what makes the test usable in CI. Recorded tool and LLM responses stand in for live calls, so the suite needs no API keys, costs nothing in tokens, and gives the same result every time.
A fail-to-pass contract is the check that the test is trustworthy. The case must fail on the old code and pass on the fix, or the test proves nothing.
Tracely (about 1.2k GitHub stars, MIT, Python/FastAPI with ClickHouse) bundles ingest, grading, clustering, promotion and a tracely-gate GitHub Action in one self-hosted stack.
Langfuse and LangSmith let you add traces to datasets and run experiments over them. Promptfoo runs assertion suites in CI. All three re-call the model live unless you add your own fixture layer.
Replay only works if the agent sends tool and model calls through a recordable seam. Plan that refactor before you pick a tool.

Why do AI agents need regression tests built from traces?

Agent failures rarely show up in the test inputs a team writes on day one. They come from real conversations: a user phrases a refund request the planner did not expect, a tool returns an empty list instead of an error, or a model upgrade changes how often the agent calls a search tool. By the time someone sees the failure, it lives in a trace (a nested record of every prompt, model response, tool call and result in one run).

Teams usually handle this in one of two ways, and both leak:

Hand-written eval sets. Someone reads the bad trace, writes a new test input and an expected output, and adds it to a YAML file or dataset. The new input is a paraphrase of what happened, not a copy. The tool responses that caused the failure are usually lost, and the test then calls live tools that now return different data.
Dashboards with no gate. The observability platform records the failure, an online evaluator scores it low, and the score sits on a chart. Nothing stops the next pull request from bringing the same failure back.

Trace-based regression testing closes that gap. The trace is the test: its exact inputs, the exact tool outputs the agent saw, and the model responses in recorded order. A grader decides pass or fail. The suite runs on every pull request and blocks merges that bring a known failure back.

This is the same discipline as "write a failing test before you fix the bug" in ordinary software. The difference is that agents are non-deterministic and depend heavily on external services, so the test needs recorded fixtures before it can be repeated.

What does a trace-to-test pipeline look like?

The loop has five stages. Every implementation, whether bought or built, needs all five.

Stage	What happens	What breaks if it is missing
Observe	Every production run emits a trace (OpenTelemetry `gen_ai.*` or OpenInference attributes) indexed by agent, conversation, turn and step	You cannot reproduce a failure you never recorded
Grade	Structural checks (schema, tool-call order, no model needed) and LLM judges score runs online	Failures are found by users, not by the system
Triage	Failing runs are clustered by structural signature and semantic similarity	31 broken runs become 31 tickets instead of one issue with a count
Promote	A representative failing trace becomes a hermetic case with its fixtures recorded	The "test" re-calls live services and drifts
Gate	CI replays the suite against the change and fails the build on regressions	The fix ships, and the next refactor quietly reverts it

The Promote stage is where most homegrown setups stop. Copying a trace's input into a dataset is easy. Copying the world the agent saw (tool results, retrieval hits, intermediate model outputs) and replaying it in order is the actual work.

How does Tracely implement trace-native CI/CD?

Tracely is an open-source project by Julien Wuthrich that describes itself as "trace-native CI/CD for AI agents." It is MIT-licensed, has about 1.2k stars at the time of writing, and is still early (a few dozen commits). Its stack is a Python backend (FastAPI, Celery, uv) and a Next.js 15 UI. It stores traces in ClickHouse, the case registry in Postgres with pgvector, the queue in Redis and blobs in MinIO/S3. A local demo starts with docker compose --profile demo up -d --build --wait.

Ingest: OTLP first, SDK second

Any OpenTelemetry exporter can post to POST {endpoint}/v1/traces with a Bearer ingest key. Tracely reads both gen_ai.* semantic-convention attributes and OpenInference attributes, so existing instrumentation keeps working. The Python SDK (tracelyai-sdk) adds auto-instrumentation for OpenAI, Anthropic, Google, Mistral and LangChain:

import tracely_sdk as tracely

tracely.init(
    endpoint="http://localhost:8000",
    api_key="tracely_dev_key",
    service_name="support-agent",
    env="prod",            # prod | staging | ci | dev — the gating axis
    instrument="auto",
)

with tracely.trace(agent="support-agent", conversation="conv-1", user="u_42"):
    client.chat.completions.create(
        model="gpt-4o",
        messages=[{"role": "user", "content": "Where is order ORD-4471?"}],
    )

The env field matters more than it looks. It separates production traces (the source of failures) from CI traces (the ones the gate grades), so the same pipeline can serve both.

Grade and cluster

Evaluators come in two kinds. Structural checks need no model. LLM judges run at conversation, run or span level and can output a score, boolean, number, text or JSON-schema result. Failing runs are then clustered "structurally and semantically" with a structural signature plus embedding similarity. In the project's words, "31 broken runs become one issue with a count."

Promote with a fail-to-pass contract

This is the most important idea in the project. Promoting a failing trace bundles "recorded input, tool and LLM outputs" as fixtures. The case is trusted only if it "must fail on the old code and pass on the fix." A regression test that passes on the buggy code is a test that cannot fail, which is the most common way evaluation suites lie to their owners. The contract makes that check part of the workflow instead of leaving it to discipline.

Multi-turn behaviour uses scenarios instead of single cases: either a scripted conversation, or an adversarial goal that a red-team model improvises against.

Gate in GitHub Actions

Replay runs offline against the recorded fixtures, with no API keys and no model spend. tracely gate exits non-zero, posts a commit status and upserts a PR comment. The bundled action has two modes. Tracely can call your agent through an entrypoint (tracely replay planner --entrypoint my_pkg.agent:run, or --cmd "node run.js" for non-Python agents), or it can grade the CI traces your own test job emitted:

- uses: jwuthri-cloud/Tracely-ai/.github/actions/tracely-gate@master
  with:
    mode:  gate
    agent: planner
    api:   https://tracely.your-co.dev
    key:   ${{ secrets.TRACELY_KEY }}

Pin that @master reference to a commit SHA before you rely on it. A gate that can change underneath you is not a gate.

The constraint you cannot skip

The README states it plainly: hermetic replay "requires your agent to route tool/model calls through the SDK's call_tool / call_llm seam." Auto-instrumentation is enough to record a trace. It is not enough to replay one, because replay means intercepting the call and returning the recorded value instead of making it. If your agent calls requests.get from inside a tool, or a framework makes model calls you cannot reach, those calls go live during replay.

Other caveats from the project itself: judges and failure analysis degrade without an LLM key, the default AUTH_MODE=dev with tracely_dev_key is open access and only suitable for local use, and the Celery worker does not hot-reload.

How do Langfuse, LangSmith and Promptfoo handle the same loop?

None of the established tools is called "trace-native CI," but each covers some of the stages. The question is which stages you get for free and which you build.

Stage	Tracely	Langfuse	LangSmith	Promptfoo
Production tracing	Yes (OTLP + SDK)	Yes, core product	Yes, core product	No (test runs only)
Online grading	Structural + LLM judges	Online evaluators on sampled traffic	Online evaluators	No
Failure clustering	Built in	Manual filtering and annotation	Manual filtering and annotation	No
Trace → test	Promote to hermetic case	Add trace to dataset	Add run to dataset	Copy input into YAML test case
Recorded tool fixtures	Yes, bundled on promote	Inputs/expected outputs only	Inputs/reference outputs only	No, you write mocks
CI gate	`tracely-gate` action, PR comment	Experiments via SDK in your CI job	Evaluations via SDK in your CI job	`promptfoo eval` + GitHub Action
Offline / no model spend	Yes, by design	No, experiments call the model	No, evaluations call the model	No, unless you mock providers
Maturity	Early, small community	Mature, MIT core	Mature, commercial	Mature, MIT

A few clarifications on that table.

Langfuse and LangSmith datasets store inputs and expected outputs, not the world. Adding a trace to a Langfuse dataset or a LangSmith dataset captures what went in and, optionally, what should come out. When you run an experiment, your application runs again, live. That is the right design for comparing a new prompt or model against a fixed set of inputs: you want the new model's real behaviour. It is the wrong design for a regression test of agent logic, because the tools return today's data instead of the data that triggered the bug. For the trade-offs between these two platforms, see our LangSmith vs Langfuse vs Phoenix comparison.

Promptfoo is the strongest CI gate and the weakest trace source. Its GitHub Action and assertion language are mature, and it runs red-team suites no trace tool matches. But it never sees production. Every case is something a human typed, so the "trace to test" step is copy-paste. Our Langfuse vs Promptfoo comparison covers why many teams pair the two.

Tracely's real differentiator is the fixture bundle plus the fail-to-pass contract. The tracing and judging overlap heavily with Langfuse. Indeed, the project describes parts of its design as mirroring Langfuse's. What is new is treating promotion as a test-engineering operation with a validity check, not as a "save to dataset" button.

How do you build hermetic replay without adopting a new platform?

If you already run Langfuse or LangSmith and do not want another stack, you can build the missing stages yourself. The core is a single seam that every tool and model call goes through. This is a generic sketch of the pattern, not any vendor's API:

import json, hashlib
from contextvars import ContextVar

_mode = ContextVar("mode", default="live")       # live | record | replay
_tape = ContextVar("tape", default=None)          # list of recorded calls

def _key(kind, name, args):
    blob = json.dumps([kind, name, args], sort_keys=True, default=str)
    return hashlib.sha256(blob.encode()).hexdigest()[:16]

def seam(kind, name, fn, **args):
    """Every tool call and LLM call goes through here."""
    mode, tape = _mode.get(), _tape.get()
    if mode == "replay":
        rec = tape.pop(0)                         # strict order, not lookup
        if rec["key"] != _key(kind, name, args):
            raise AssertionError(f"divergence at {kind}:{name}")
        return rec["output"]
    out = fn(**args)
    if mode == "record":
        tape.append({"key": _key(kind, name, args), "kind": kind,
                     "name": name, "output": out})
    return out

Three design choices in that sketch decide whether the tests are useful.

Replay in strict order, and fail on divergence. If the fixed agent makes a different call than the recorded one, a lookup-by-key replay silently returns nothing or the wrong record. Raising on divergence turns "the agent took a different path" into an explicit result. Sometimes that is the fix working, and you re-record. Sometimes it is a new bug.
Decide which calls are fixtures and which are under test. In a test of planner logic, the model calls are the system under test, so replaying them proves only that the code around them is deterministic. A common split: replay tools and retrieval always, and replay the model only for the steps before the step that failed, then call the model live (or a pinned cheap model) for the step being tested. Tracely's offline mode replays everything. That is cheaper and deterministic, but it catches regressions in orchestration code, not in model behaviour.
Grade the outcome, not the transcript. Exact-match on model text breaks on every model upgrade. Grade structural facts ("called refund with the original order ID", "did not call cancel_subscription") and use an LLM judge only where structure cannot express the requirement.

Then write the fail-to-pass check as an actual test step: run the case against the parent commit and require a failure, run it against the fix and require a pass. Without that step you will end up with cases that were promoted from a trace but never reproduced the bug, and they pass forever.

When is trace-based regression testing the wrong tool?

It is not a replacement for every kind of evaluation.

Model or prompt comparisons need live calls by definition. Use dataset experiments (Langfuse, LangSmith) or a Promptfoo matrix, and accept the token cost.
Security and adversarial testing need inputs no user has sent yet. A trace suite only knows past failures. Red-team generation (Promptfoo plugins, or Tracely's adversarial scenarios) fills that gap.
Agents whose tools have side effects you cannot record. Examples are a browser agent, a coding agent running shell commands, or anything that depends on wall-clock time. These need a sandbox or a simulated environment, not a tape. Replaying a recorded DOM is possible but brittle.
Very low traffic. If you have a handful of production runs a week, hand-written cases are faster than running an ingest stack.

The approach pays off when you have steady production traffic, recurring failure modes, and orchestration code that changes often. That is the profile of most customer-facing tool-calling agents.

How should a team adopt trace-to-test in practice?

A sequence that keeps the investment proportional:

Standardise tracing on OpenTelemetry gen_ai.* attributes (see the OpenTelemetry GenAI semantic conventions). Every tool in this article can ingest them, so the choice of backend stays reversible.
Introduce the call seam in the agent code. Do this first, because it is the step every option needs and the one that takes longest.
Promote by hand for a month. Take the five worst production failures, record them, write structural graders, and run them in CI. You will learn which calls leak past the seam.
Then decide on a platform. If clustering and promotion are now the bottleneck, a tool like Tracely earns its operational cost (ClickHouse, Postgres, Redis and object storage). If your observability platform's datasets plus your own fixture layer are enough, stay there.
Treat the gate as production infrastructure. Pin action versions, alert on gate failures you did not expect, and review the suite whenever you upgrade a model. A failure across the whole suite after a model change is a signal, not noise.

FAQ

What is AI agent regression testing?

AI agent regression testing checks that a change to an agent's code, prompts or model does not bring back a failure that was already fixed. The strongest form builds each test from a real failing trace and replays it on every pull request.

What is hermetic replay for LLM agents?

Hermetic replay runs an agent test with recorded tool and model responses instead of live calls. The test needs no network access, no API keys and no token spend, and it gives the same result on every run, which makes it usable as a CI gate.

What is a fail-to-pass contract?

A fail-to-pass contract is a rule that a regression case must fail on the code that had the bug and pass on the code that fixes it. It proves the test actually reproduces the failure, instead of being a test that cannot fail.

Is Tracely a replacement for Langfuse or LangSmith?

Not yet for most teams. Tracely overlaps with their tracing and judging but is an early project with a small community. Its distinctive parts are fixture-bundled promotion, failure clustering and an offline CI gate, which you can also build on top of an existing platform.

Can Promptfoo test agents built from production traces?

Promptfoo can gate CI on agent outputs and run red-team suites, but it does not ingest production traces. You copy the trace's input into a test case by hand and write your own mocks for tool responses.

Do I need OpenTelemetry to do trace-based testing?

No, but it keeps your options open. Tracely, Langfuse, LangSmith and Phoenix all accept OpenTelemetry traces, so instrumenting once with gen_ai.* attributes lets you change backends without re-instrumenting the agent.

Sources

Originally published at fp8.co. Subscribe for weekly AI engineering analysis at fp8.co/newsletters.

Bessemer Venture Partners: AI Investment Portfolio & Strategy Analysis

ke yi — Wed, 28 Oct 2026 05:54:16 +0000

Investor Overview

Bessemer Venture Partners is one of the oldest venture capital firms in the world, with roots dating back to 1911 and the Carnegie steel fortune. The firm manages over $20 billion and has backed more than 200 IPOs including Shopify, Twilio, LinkedIn, Pinterest, and Wix. Bessemer backed Perplexity and maintains an active AI investment practice spanning enterprise applications, developer tools, and infrastructure. The firm is famous for its cloud computing investment thesis and annual State of the Cloud report, and has expanded its AI practice to identify the next generation of category-defining companies leveraging large language models and generative AI.

Detail	Value
Name	Bessemer Venture Partners
Type	VC
Website	https://bvp.com
Tracked AI Deals	3
Unique Portfolio Companies	2
Total Capital in Tracked Rounds	$1.1B
Lead Investments	1

Portfolio Summary

As of March 2026, Bessemer Venture Partners had participated in 3 tracked AI funding round(s) across 2 companyies. Below is a complete breakdown.

Company	Sector	Round	Amount	Valuation	Date	Role
Perplexity	AI Search	Series B	$73M	$520M	Apr 20, 2024	Participant
Poolside	AI Developer Tools	Series B	$500M	$3.0B	Oct 1, 2025	Lead
Perplexity	AI Search	Series C	$500M	$9.0B	Feb 5, 2026	Participant

Total Capital Deployed in AI

Across all tracked rounds, Bessemer Venture Partners has participated in deals totaling $1.1B in aggregate round sizes. Note: this represents total round amounts, not the investor's individual check sizes, which are not publicly disclosed for most rounds.

By Company

Company	Rounds Participated	Total Round Value	Latest Round
Perplexity	2	$573M	Series C (Feb 5, 2026)
Poolside	1	$500M	Series B (Oct 1, 2025)

This area of the AI market continues to attract significant investor attention, reflecting the sector's rapid growth and substantial long-term potential.

Sector Breakdown

Bessemer Venture Partners's AI investments span 2 sector(s). Here is how their deal activity breaks down by sector.

Sector	Number of Deals	% of Portfolio
AI Search	2	67%
AI Developer Tools	1	33%

These sector dynamics reflect the broader market's appetite for AI solutions across verticals, with both infrastructure and application layers seeing robust investment. The concentration of capital in leading sectors signals where investors see the highest near-term returns.

Co-Investor Analysis

As of March 2026, Bessemer Venture Partners had co-invested alongside 5 other investor(s) in tracked AI rounds. The most frequent co-investors are:

Co-Investor	Deals Together	Relationship
IVP	1	Single co-investment
NEA	1	Single co-investment
Nat Friedman & Daniel Gross	1	Single co-investment
Felicis Ventures	1	Single co-investment
Thrive Capital	1	Single co-investment

Co-investment patterns reveal the strategic networks that shape AI deal flow. Frequent co-investors often indicate shared investment theses and trusted relationships that accelerate due diligence and deal execution.

Deal Timeline

A chronological view of Bessemer Venture Partners's tracked AI investments:

Apr 20, 2024 — Series B in Perplexity: $73M at $520M valuation (Participant)
Oct 1, 2025 — Series B in Poolside: $500M at $3.0B valuation (Lead)
Feb 5, 2026 — Series C in Perplexity: $500M at $9.0B valuation (Participant)

Investment cadence reflects both market conditions and the firm's conviction in AI as a generational platform shift. The pace of deployment has accelerated as AI companies demonstrate commercial traction.

Lead vs Participation Analysis

As of March 2026, of Bessemer Venture Partners's 3 tracked AI deals:

Lead Investor: 1 deal(s) (33%)
Participant: 2 deal(s) (67%)

Deals Where Bessemer Venture Partners Led

Poolside Series B: $500M (Oct 1, 2025)

Deals Where Bessemer Venture Partners Participated

Perplexity Series B: $73M (Apr 20, 2024)
Perplexity Series C: $500M (Feb 5, 2026)

The balance between lead and participation roles provides insight into the investor's conviction level and typical deal role. Leading rounds typically signals highest conviction and a desire to secure board seats and governance rights.

Notable Deals

The largest rounds that Bessemer Venture Partners participated in:

1. Poolside — Series B ($500M)

Date: Oct 1, 2025
Valuation: $3.0B
Role: Lead Investor
Sector: AI Developer Tools
Details: Code-specialized foundation model lab scales training infrastructure.

2. Perplexity — Series C ($500M)

Date: Feb 5, 2026
Valuation: $9.0B
Role: Participant
Sector: AI Search
Details: AI search engine takes on Google with rapid user growth.

3. Perplexity — Series B ($73M)

Date: Apr 20, 2024
Valuation: $520M
Role: Participant
Sector: AI Search
Details: AI search startup shows explosive growth with unique answer engine approach.

These deals represent the investor's highest-conviction bets, each reflecting a thesis about where transformative AI value will be created over the next decade.

Frequently Asked Questions

How many AI companies has Bessemer Venture Partners invested in?

As of March 2026, Bessemer Venture Partners had invested in 2 AI companyies tracked by AI Funding: Perplexity, Poolside.

What sectors does Bessemer Venture Partners focus on in AI?

Bessemer Venture Partners's most active AI sector is AI Search with 2 deal(s). They invest across 2 AI sector(s) in total.

Who does Bessemer Venture Partners co-invest with most often?

Bessemer Venture Partners's most frequent co-investor is IVP, having co-invested in 1 deal(s) together.

Has Bessemer Venture Partners led any AI funding rounds?

Yes, as of March 2026, Bessemer Venture Partners had led 1 AI funding round(s), including Poolside's Series B.

What is the largest AI deal Bessemer Venture Partners has participated in?

The largest tracked round is Poolside's Series B at $500M (Oct 1, 2025).

Coatue Management: AI Investment Portfolio & Strategy Analysis

ke yi — Tue, 27 Oct 2026 06:24:48 +0000

Investor Overview

Coatue Management is a technology-focused investment firm founded in 1999 by Philippe Laffont, managing over $48 billion across public equities, private growth, and venture capital. The firm's crossover model enables it to invest across the full company lifecycle from early stage through public markets. Coatue led Cyera's Series D and is active across the AI landscape with investments in infrastructure, security, and enterprise applications. The firm is known for its rigorous quantitative research process, building detailed financial models and technology assessments before committing capital. Coatue's public market expertise gives it a unique perspective on private company valuations.

Detail	Value
Name	Coatue Management
Type	Crossover
Website	https://coatue.com
Tracked AI Deals	2
Unique Portfolio Companies	2
Total Capital in Tracked Rounds	$401M
Lead Investments	1

Portfolio Summary

As of March 2026, Coatue Management had participated in 2 tracked AI funding round(s) across 2 companyies. Below is a complete breakdown.

Company	Sector	Round	Amount	Valuation	Date	Role
Stability AI	Creative AI	Series A	$101M	$1.0B	Oct 1, 2023	Participant
Cyera	AI Security	Series D	$300M	$5.0B	Feb 1, 2026	Lead

Total Capital Deployed in AI

Across all tracked rounds, Coatue Management has participated in deals totaling $401M in aggregate round sizes. Note: this represents total round amounts, not the investor's individual check sizes, which are not publicly disclosed for most rounds.

By Company

Company	Rounds Participated	Total Round Value	Latest Round
Stability AI	1	$101M	Series A (Oct 1, 2023)
Cyera	1	$300M	Series D (Feb 1, 2026)

This area of the AI market continues to attract significant investor attention, reflecting the sector's rapid growth and substantial long-term potential.

Sector Breakdown

Coatue Management's AI investments span 2 sector(s). Here is how their deal activity breaks down by sector.

Sector	Number of Deals	% of Portfolio
Creative AI	1	50%
AI Security	1	50%

Co-Investor Analysis

As of March 2026, Coatue Management had co-invested alongside 2 other investor(s) in tracked AI rounds. The most frequent co-investors are:

Co-Investor	Deals Together	Relationship
Lightspeed Venture Partners	1	Single co-investment
Accel	1	Single co-investment

Deal Timeline

A chronological view of Coatue Management's tracked AI investments:

Oct 1, 2023 — Series A in Stability AI: $101M at $1.0B valuation (Participant)
Feb 1, 2026 — Series D in Cyera: $300M at $5.0B valuation (Lead)

Lead vs Participation Analysis

As of March 2026, of Coatue Management's 2 tracked AI deals:

Lead Investor: 1 deal(s) (50%)
Participant: 1 deal(s) (50%)

Deals Where Coatue Management Led

Cyera Series D: $300M (Feb 1, 2026)

Deals Where Coatue Management Participated

Stability AI Series A: $101M (Oct 1, 2023)

Notable Deals

The largest rounds that Coatue Management participated in:

1. Cyera — Series D ($300M)

Date: Feb 1, 2026
Valuation: $5.0B
Role: Lead Investor
Sector: AI Security
Details: AI-native data security platform scales globally.

2. Stability AI — Series A ($101M)

Date: Oct 1, 2023
Valuation: $1.0B
Role: Participant
Sector: Creative AI
Details: Stable Diffusion maker raises at unicorn valuation.

These deals represent the investor's highest-conviction bets, each reflecting a thesis about where transformative AI value will be created over the next decade.

Frequently Asked Questions

How many AI companies has Coatue Management invested in?

As of March 2026, Coatue Management had invested in 2 AI companyies tracked by AI Funding: Stability AI, Cyera.

What sectors does Coatue Management focus on in AI?

Coatue Management's most active AI sector is Creative AI with 1 deal(s). They invest across 2 AI sector(s) in total.

Who does Coatue Management co-invest with most often?

Coatue Management's most frequent co-investor is Lightspeed Venture Partners, having co-invested in 1 deal(s) together.

Has Coatue Management led any AI funding rounds?

Yes, as of March 2026, Coatue Management had led 1 AI funding round(s), including Cyera's Series D.

What is the largest AI deal Coatue Management has participated in?

The largest tracked round is Cyera's Series D at $300M (Feb 1, 2026).

xAI vs Thinking Machines Lab: Funding, Valuation & Strategy Compared

ke yi — Tue, 27 Oct 2026 06:24:47 +0000

Introduction

xAI and Thinking Machines Lab are two of the most closely watched companies in AI. This comparison breaks down their funding, valuations, investors, and strategic positioning.

At a Glance

Metric	xAI	Thinking Machines Lab
Founded	2023	2025
HQ	Palo Alto, CA	Paris, France
Sector	Foundation Models & AGI	Foundation Models & AGI
Employees	500	100
Total Raised	$6.0B	$1.0B
Latest Valuation	$50.0B	$5.0B
Latest Round	Series C ($6.0B)	Seed ($1.0B)
Revenue Run Rate	$500M+ ARR	N/A
Founder	Elon Musk (Founder & CEO)	Not disclosed

Funding Comparison

xAI — Funding History

Round	Amount	Valuation	Date	Lead Investor
Series C	$6.0B	N/A	Nov 15, 2025	Sequoia Capital

Total Raised: $6.0B across 1 round(s)

Thinking Machines Lab — Funding History

Round	Amount	Valuation	Date	Lead Investor
Seed	$1.0B	N/A	Feb 18, 2026	General Catalyst

Total Raised: $1.0B across 1 round(s)

The funding trajectory demonstrates accelerating momentum, with round sizes and valuations increasing as key milestones are delivered and market presence expands.

Investor Analysis

Unique to xAI

Sequoia Capital
Andreessen Horowitz
NVIDIA

Unique to Thinking Machines Lab

General Catalyst

Strategy & Positioning

xAI

AI company founded by Elon Musk, building Grok to understand the true nature of the universe.

Thinking Machines Lab

European AI research lab founded by Yann LeCun, focused on next-generation AI architectures and reasoning systems.

Frequently Asked Questions

Who has raised more funding — xAI or Thinking Machines Lab?

xAI has raised more total funding at $6.0B, compared to Thinking Machines Lab's $1.0B. That is a difference of $5.0B.

Which company is valued higher — xAI or Thinking Machines Lab?

xAI has a higher latest valuation at $50.0B, compared to Thinking Machines Lab's $5.0B. xAI's valuation is 10.0x that of Thinking Machines Lab.

Which company was founded first?

xAI was founded in 2023, 2 year(s) before Thinking Machines Lab (2025).

Which company has more employees?

xAI has approximately 500 employees, compared to Thinking Machines Lab's 100.

Verdict

Under current leadership, the company has navigated rapid growth while maintaining technical excellence and a clear strategic vision for the future of AI.

Learn More

This Week in AI Funding: Oct 19 – Oct 23, 2026

ke yi — Mon, 26 Oct 2026 14:23:51 +0000

AI Funding recorded 34 funding rounds dated 2026-10-19 through 2026-10-23. The 34 USD-denominated rounds total $18.4B.

These figures summarize the cited records in this tracker; they are not a measure of all market activity or confirmation of cash transferred. Deals are listed by date, without comparing amounts in different currencies.

What was recorded this week?

Metric	Value
Recorded rounds, all currencies	34
USD rounds	34
Total recorded in USD	$18.4B
Average USD round	$541M

Which funding rounds were recorded?

doubleAI — $200M Series A

Recorded date: 2026-10-19
Sector: AI Developer Tools
Recorded investors: Undisclosed (lead)
Source: Funding report
Company: doubleAI

Kodama — $7M Undisclosed

Recorded date: 2026-10-19
Sector: AI Robotics
Recorded investors: Undisclosed (lead)
Source: Funding report
Company: Kodama

Tolan — $30M Undisclosed

Recorded date: 2026-10-19
Sector: Consumer AI
Recorded investors: Undisclosed (lead)
Source: Funding report
Company: Tolan

Vessla — $3M Undisclosed

Recorded date: 2026-10-19
Sector: Enterprise AI
Recorded investors: Undisclosed (lead)
Source: Funding report
Company: Vessla

Atombeam — $18M Undisclosed

Recorded date: 2026-10-20
Sector: AI Infrastructure
Recorded investors: Undisclosed (lead)
Source: Funding report
Company: Atombeam

AuxoAI — $21M Series A

Recorded date: 2026-10-20
Sector: Enterprise AI
Recorded investors: Undisclosed (lead)
Source: Funding report
Company: AuxoAI

Dream Labs — $90M Undisclosed

Recorded date: 2026-10-20
Sector: AI Robotics
Recorded investors: Undisclosed (lead)
Source: Funding report
Company: Dream Labs

Firefly — $20M Series A

Recorded date: 2026-10-20
Sector: AI Security
Recorded investors: Definition Capital (lead)
Source: Funding report
Company: Firefly

Gradient — $4M Undisclosed

Recorded date: 2026-10-20
Sector: AI Fintech
Recorded investors: Undisclosed (lead)
Source: Funding report
Company: Gradient

Greptile — $50M Undisclosed

Recorded date: 2026-10-20
Sector: AI Developer Tools
Recorded investors: Undisclosed (lead)
Source: Funding report
Company: Greptile

Jooba — $4M Seed

Recorded date: 2026-10-20
Sector: Enterprise AI
Recorded investors: Undisclosed (lead)
Source: Funding report
Company: Jooba

Otter — $320M Undisclosed

Recorded date: 2026-10-20
Sector: Enterprise AI
Recorded investors: Undisclosed (lead)
Source: Funding report
Company: Otter

Otter.ai — $320M Undisclosed

Recorded date: 2026-10-20
Sector: Enterprise AI
Recorded investors: Undisclosed (lead)
Source: Funding report
Company: Otter.ai

Pigment — $250M Undisclosed

Recorded date: 2026-10-20
Sector: Enterprise AI
Recorded investors: Undisclosed (lead)
Source: Funding report
Company: Pigment

Vectorial — $3M Seed

Recorded date: 2026-10-20
Sector: Enterprise AI
Recorded investors: Undisclosed (lead)
Source: Funding report
Company: Vectorial

ASI — $325M Series C

Recorded date: 2026-10-21
Sector: AI Infrastructure
Recorded investors: Undisclosed (lead)
Source: Funding report
Company: ASI

Eka Robotics — $150M Undisclosed

Recorded date: 2026-10-21
Sector: AI Robotics
Recorded investors: Undisclosed (lead)
Source: Funding report
Company: Eka Robotics

Firstwork — $15M Undisclosed

Recorded date: 2026-10-21
Sector: Enterprise AI
Recorded investors: Undisclosed (lead)
Source: Funding report
Company: Firstwork

Flapping Airplanes — $1.0B Undisclosed

Recorded date: 2026-10-21
Sector: Foundation Models & AGI
Recorded investors: Undisclosed (lead)
Source: Funding report
Company: Flapping Airplanes

Mach9 — $34M Series A

Recorded date: 2026-10-21
Sector: Enterprise AI
Recorded investors: Undisclosed (lead)
Source: Funding report
Company: Mach9

Opply — $8M Undisclosed

Recorded date: 2026-10-21
Sector: AI Fintech
Recorded investors: Undisclosed (lead)
Source: Funding report
Company: Opply

PataBid — $2M Undisclosed

Recorded date: 2026-10-21
Sector: Enterprise AI
Recorded investors: Graphite Ventures (lead)
Source: Funding report
Company: PataBid

Reliable Robotics — $115M Undisclosed

Recorded date: 2026-10-21
Sector: AI Robotics
Recorded investors: Undisclosed (lead)
Source: Funding report
Company: Reliable Robotics

Watchtower Labs — $35M Seed

Recorded date: 2026-10-21
Sector: Defense AI
Recorded investors: Undisclosed (lead)
Source: Funding report
Company: Watchtower Labs

BioStack — $10M Undisclosed

Recorded date: 2026-10-22
Sector: AI Healthcare
Recorded investors: Undisclosed (lead)
Source: Funding report
Company: BioStack

DeepSeek — $15.0B Second funding round

Recorded date: 2026-10-22
Sector: Foundation Models & AGI
Recorded investors: Undisclosed (lead)
Source: Funding report
Company: DeepSeek

Mave — $34M Undisclosed

Recorded date: 2026-10-22
Sector: Enterprise AI
Recorded investors: Undisclosed (lead)
Source: Funding report
Company: Mave

Nirva — $33M Undisclosed

Recorded date: 2026-10-22
Sector: AI Consumer Hardware
Recorded investors: Undisclosed (lead)
Source: Funding report
Company: Nirva

Reduck — $3M Pre-Seed

Recorded date: 2026-10-22
Sector: AI Developer Tools
Recorded investors: Undisclosed (lead)
Source: Funding report
Company: Reduck

Slingshot Insurance — $27M Seed

Recorded date: 2026-10-22
Sector: AI Fintech
Recorded investors: Undisclosed (lead)
Source: Funding report
Company: Slingshot Insurance

Arcanus Aerial Systems — $500K Undisclosed

Recorded date: 2026-10-23
Sector: AI Robotics
Recorded investors: Undisclosed (lead)
Source: Funding report
Company: Arcanus Aerial Systems

BOB — $6M Undisclosed

Recorded date: 2026-10-23
Sector: AI Fintech
Recorded investors: Undisclosed (lead)
Source: Funding report
Company: BOB

Maxinsights — $30M Undisclosed

Recorded date: 2026-10-23
Sector: AI Robotics
Recorded investors: Peak XV (lead)
Source: Funding report
Company: Maxinsights

Pathos AI — $237M Series E

Recorded date: 2026-10-23
Sector: AI Healthcare
Recorded investors: Undisclosed (lead)
Source: Funding report
Company: Pathos AI

How do the recorded rounds break down by sector?

Sector	Rounds, all currencies	USD subtotal	Share of USD total
AI Consumer Hardware	1	$33M	0.2%
AI Developer Tools	3	$253M	1.4%
AI Fintech	4	$44M	0.2%
AI Healthcare	2	$247M	1.3%
AI Infrastructure	2	$343M	1.9%
AI Robotics	6	$393M	2.1%
AI Security	1	$20M	0.1%
Consumer AI	1	$30M	0.2%
Defense AI	1	$35M	0.2%
Enterprise AI	11	$1.0B	5.5%
Foundation Models & AGI	2	$16.0B	86.9%

USD subtotals and shares exclude rounds recorded in other currencies. No exchange rate or market forecast is inferred.

Sources

Lux Capital: AI Investment Portfolio & Strategy Analysis

ke yi — Mon, 26 Oct 2026 06:28:42 +0000

Investor Overview

Lux Capital is a venture capital firm focused on emerging science and technology, managing over $5 billion across funds investing in AI, robotics, space, synthetic biology, and quantum computing. Founded in 2000, Lux has backed companies including Anduril, Desktop Metal, Echodyne, and Saildrone. The firm is known for its deep science investing approach, backing founders building hard-tech companies with genuine technical differentiation.

Detail	Value
Name	Lux Capital
Type	VC
Website	https://luxcapital.com
Tracked AI Deals	4
Unique Portfolio Companies	3
Total Capital in Tracked Rounds	$350M
Lead Investments	3

Portfolio Summary

As of March 2026, Lux Capital had participated in 4 tracked AI funding round(s) across 3 companyies. Below is a complete breakdown.

Company	Sector	Round	Amount	Valuation	Date	Role
Sakana AI	LLM / Foundation Models	Seed	$30M	$200M	Jan 15, 2024	Lead
Sakana AI	LLM / Foundation Models	Series B	$300M	N/A	Nov 20, 2025	Participant
Cellbox Solutions	Deep Tech	Series A	$15M	N/A	Mar 5, 2026	Lead
AIRMO	Climate Tech / AI	Seed	$5M	N/A	Mar 12, 2026	Lead

Total Capital Deployed in AI

Across all tracked rounds, Lux Capital has participated in deals totaling $350M in aggregate round sizes. Note: this represents total round amounts, not the investor's individual check sizes, which are not publicly disclosed for most rounds.

By Company

Company	Rounds Participated	Total Round Value	Latest Round
Sakana AI	2	$330M	Series B (Nov 20, 2025)
Cellbox Solutions	1	$15M	Series A (Mar 5, 2026)
AIRMO	1	$5M	Seed (Mar 12, 2026)

This area of the AI market continues to attract significant investor attention, reflecting the sector's rapid growth and substantial long-term potential.

Sector Breakdown

Lux Capital's AI investments span 3 sector(s). Here is how their deal activity breaks down by sector.

Sector	Number of Deals	% of Portfolio
LLM / Foundation Models	2	50%
Deep Tech	1	25%
Climate Tech / AI	1	25%

Co-Investor Analysis

As of March 2026, Lux Capital had co-invested alongside 2 other investor(s) in tracked AI rounds. The most frequent co-investors are:

Co-Investor	Deals Together	Relationship
Khosla Ventures	2	Repeat co-investor
NVIDIA	1	Single co-investment

Deal Timeline

A chronological view of Lux Capital's tracked AI investments:

Jan 15, 2024 — Seed in Sakana AI: $30M at $200M valuation (Lead)
Nov 20, 2025 — Series B in Sakana AI: $300M (Participant)
Mar 5, 2026 — Series A in Cellbox Solutions: $15M (Lead)
Mar 12, 2026 — Seed in AIRMO: $5M (Lead)

Lead vs Participation Analysis

As of March 2026, of Lux Capital's 4 tracked AI deals:

Lead Investor: 3 deal(s) (75%)
Participant: 1 deal(s) (25%)

Deals Where Lux Capital Led

Sakana AI Seed: $30M (Jan 15, 2024)
Cellbox Solutions Series A: $15M (Mar 5, 2026)
AIRMO Seed: $5M (Mar 12, 2026)

Deals Where Lux Capital Participated

Sakana AI Series B: $300M (Nov 20, 2025)

Notable Deals

The largest rounds that Lux Capital participated in:

1. Sakana AI — Series B ($300M)

Date: Nov 20, 2025
Valuation: $2.0B
Role: Participant
Sector: LLM / Foundation Models
Details: Japan's leading AI lab raises to expand nature-inspired foundation models.

2. Sakana AI — Seed ($30M)

Date: Jan 15, 2024
Valuation: $200M
Role: Lead Investor
Sector: LLM / Foundation Models
Details: Transformer co-author's Tokyo AI lab raises initial round.

3. Cellbox Solutions — Series A ($15M)

Date: Mar 5, 2026
Role: Lead Investor
Sector: Deep Tech
Details: Cellbox Solutions raises $15M Series A for AI-driven cell therapy manufacturing optimization.

4. AIRMO — Seed ($5M)

Date: Mar 12, 2026
Role: Lead Investor
Sector: Climate Tech / AI
Details: AIRMO bags €5M seed round to hunt unreported methane leaks using AI and satellite data.

These deals represent the investor's highest-conviction bets, each reflecting a thesis about where transformative AI value will be created over the next decade.

Frequently Asked Questions

How many AI companies has Lux Capital invested in?

As of March 2026, Lux Capital had invested in 3 AI companyies tracked by AI Funding: Sakana AI, Cellbox Solutions, AIRMO.

What sectors does Lux Capital focus on in AI?

Lux Capital's most active AI sector is LLM / Foundation Models with 2 deal(s). They invest across 3 AI sector(s) in total.

Who does Lux Capital co-invest with most often?

Lux Capital's most frequent co-investor is Khosla Ventures, having co-invested in 2 deal(s) together.

Has Lux Capital led any AI funding rounds?

Yes, as of March 2026, Lux Capital had led 3 AI funding round(s), including Sakana AI's Seed, Cellbox Solutions's Series A, AIRMO's Seed.

What is the largest AI deal Lux Capital has participated in?

The largest tracked round is Sakana AI's Series B at $300M (Nov 20, 2025).