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This presentation includes proprietary information of Battery Ventures
Battery Ventures
THE STATE OF AI REPORT
This presentation includes proprietary information of Battery Ventures
DECEMBER 2025
This presentation includes proprietary information of Battery Ventures
Disclaimers
2
This disclaimer applies to this document and the verbal or written comments of any person presenting it. This document, taken together with
such verbal or written comments, is referred to herein as the “presentation .”This presentation is being provided for informational purposes
only. Nothing herein is or should be construed as investment, legal or tax advice, a recommendation of any kind or an offer to sell or a
solicitation of an offer to buy any security or offer investment advisory services with regard to securities . This presentation does not purport to
be complete on any topic addressed . The information in this presentation is provided to you as of December 18, 2025 unless otherwise noted
and Battery Ventures does not intend to update the information after its distribution, even in the event the presentation becomes materially
inaccurate . Certain information in this presentation has been obtained from third party sources and, although believed to be reliable, has not
been independently verified and its accuracy or completeness cannot be guaranteed . Certain logos, tradenames, trademarks and copyrights
included in the presentation are strictly for identification and informational purposes only. Such logos, trade names, trademarks and copyrights
may be owned by companies or persons not affiliated with Battery Ventures and no claim is made that any such company or person has
sponsored or endorsed the use of such logos, trade names, trademarks and copyrights in this presentation . This presentation includes various
examples of companies in which Battery Ventures has invested . For a complete list of all companies in which Battery Ventures has invested,
please visit: https ://www .battery .com/list -of-all-companies/ . Past performance is not evidence of future results and there can be no assurance
that a particular Battery portfolio company will achieve comparable results to any other company .
The information contained herein is based solely on the opinions of Dharmesh Thakker, Danel Dayan, Jason Mendel, and Sudhee ndra
Chilappagari and nothing should be construed as investment advice . This presentation and the anecdotal examples throughout are intended for
an audience of entrepreneurs in their attempt to build cloud -focused businesses and not recommendations or endorsements of any particular
business or an offering of investment advisory services .
This presentation includes proprietary information of Battery Ventures
The Battery team
3
Dharmesh Thakker
dharmesh@battery.com
Jason Mendel
jmendel@battery.com
Sudhee Chilappagari
sudhee@battery.com
Danel Dayan
ddayan@battery.com
This presentation includes proprietary information of Battery Ventures
Market Overview
This presentation includes proprietary information of Battery Ventures4
This presentation includes proprietary information of Battery Ventures
($5)
$0
$5
$10
$15
$20
Nov-22 Feb-23 May-23 Aug-23 Nov-23 Feb-24 May-24 Aug-24 Nov-24 Feb-25 May-25 Aug-25 Nov-25
AI is driving public-market value creation
AI companies represent 50% of S&P 500 value, and since Nov. 2022 have added ~$18T in market cap, accounting for ~75% of all g ains.
5
Note: AI Basket represents a selected set of public companies across key segments of the AI value chain, including AI Power & Industrials (utilities, cooling, electrical systems), AI Hardware & Infrastructure (GPUs, CPUs, servers,
edge devices), AI Cloud (cloud providers and model platforms), AI Software (enterprise, analytics, security), and AI Componen ts (memory, storage, analog, sensors, manufacturing tools). Ticker list reflects major S&P 500 AI -exposed
companies based on Battery Ventures analysis. Market data as of 12/15/2025.
Sources: CapIQ, Company filings.
Market Cap Added Since Nov 2022 (US$T)
AI: +185%
Non -AI: +28%
+$18T
+$7T
Market Cap Added Across S&P500 Companies Since ChatGPT Launch (Nov. 2022)
AI BasketEx-AI Basket
This presentation includes proprietary information of Battery Ventures
Macro -Driven Optimization
Cloud providers are entering a new growth cycle driven by AI demand
Cloud provider growth is reaccelerating at $285B of run -rate revenue as new AI workloads come online
6Note: Growth rates reflect FX -adjusted figures (i.e., not constant currency).
Sources: Company filings, Wall Street research
$74 $79 $82 $86 $85 $89 $92 $97 $100 $105 $110 $115 $117 $123 $132
$32 $36 $38 $41 $40 $45 $49 $54 $54 $61 $66 $71 $72
$85
$92
$23 $25 $27 $29 $30 $32 $34 $37 $38
$41
$45
$48 $49
$54
$61
$129 $140 $147 $156 $156 $166 $175
$188 $193
$207
$221
$234 $239
$263
$285
40%
35%
31%
25%
21%
19% 19% 21%
24%
25% 26% 25% 24%
27% 29%
0%
5%
10%
15%
20%
25%
30%
35%
40%
45%
$0
$50
$100
$150
$200
$250
$300
Q1'22 Q2'22 Q3'22 Q4'22 Q1'23 Q2'23 Q3'23 Q4'23 Q1'24 Q2'24 Q3'24 Q4'24 Q1'25 Q2'25 Q3'25
$ in Billions
AI - Driven Reacceleration
Y/Y Growth
This presentation includes proprietary information of Battery Ventures
$85 $116 $130 $133 $191
$31 $14 $3 $89
$137
$116 $130 $133
$222
$329
$0
$200
$400
$600
$800
$1,000
$1,200
$1,400
$226 $316
$425 $517 $609
$89
$109
$92
$148
$596
$316
$425
$517
$666
$1,205
$0
$200
$400
$600
$800
$1,000
$1,200
$1,400
Explosive AI demand continues to outpace available infrastructure capacity
Despite investing $329B in CapEx in the last 12 months, cloud providers remain capacity -constrained with $1.2T of backlog, resul ting in 4x
demand overhang
7Note: Revenue Backlog represents Remaining Purchase Obligations (RPO). Cloud providers represents AWS, GCP, Azure, and Oracle .
Sources: Company filings, Wall Street research.
Cloud Provider Revenue Backlog vs. CapEx
Cloud Provider Revenue Backlog &
CapEx Spend ($B)
4x
Demand
Overhang
Existing
Net New
3.0x
2021 2022 2023 2024 LTM
Beg. Revenue Backlog Net New Revenue Backlog Prior Year CapEx Incremental CapEx
This presentation includes proprietary information of Battery Ventures
However, cloud provider revenue is shifting from training to inference
AI cloud revenue is still training -heavy, but the next wave of agentic applications will shift the center of gravity to inferenc e
8
Cloud Provider Revenue Contribution
Low
High
Revenue Driver / (Time Period)Training / (Today) Inference / (Future)
Training
Inference
Note: 1. Denotes a past or current Battery company. For a full list of all Battery investments, please click here.
1
1
1 1
1 1 1 1 1 1
This presentation includes proprietary information of Battery Ventures
A new infrastructure layer is taking shape
The next era of infrastructure expands beyond scaling storage and compute to scaling intelligence
9Note: 1. Denotes a past or current Battery company. For a full list of all Battery investments, please click here.
AI - Native Challengers
Dev Tools and Collaboration
Data and Analytics
Cloud Infrastructure
Networking and Silicon
Cloud Era Incumbents
1
1
1
This presentation includes proprietary information of Battery Ventures
Foundation models are powering the AI wave…
Foundation models are scaling at unprecedented speeds, reaching $10B of ARR 5x faster than platform incumbents
10 Note: ARR represents annualized run -rate where ARR is not disclosed.
Sources: The Information, Epoch AI, Company filings.
Foundation Model Growth Time to $10B ARR
$6
$20
$30
$60
$100
$1
$9
$26
$40
$70
$0
$20
$40
$60
$80
$100
$120
2024A 2025E 2026E 2027E 2028E
OpenAI Anthropic
Annual Recurring Revenue ($B)
25
20
19
10
10
9
8
3.5
2.5
0 5 10 15 20 25 30
Nvidia
ServiceNow
Salesforce
Meta
AWS
Azure
Google
Anthropic
OpenAI
Years
Since ChatGPT launch
This presentation includes proprietary information of Battery Ventures
…And are enabling a new class of applications
Just as networking unleashed the internet and the cloud abstracted infrastructure, the agentic future is being built on top o f foundation models
11 Note: 1. Denotes a past or current Battery company. For a full list of all Battery investments, please click here.
Applications UnleashedKey Enablers
PC & Internet
SaaS
AI Apps
Networking
(1990s-2000s)
Cloud (2010s)
Foundation Models
(2020s+)
Infrastructure Era
1
This presentation includes proprietary information of Battery Ventures
The walled garden of foundation models is being challenged by open alternatives
Open models, runtimes and inference engines are fueling the new generation of intelligent applications
12 Note: 1. Denotes a past or current Battery company. For a full list of all Battery investments, please click here.
Sources: Company filings, OpenRouter State of AI.
Token Usage by Model Type
Emerging Open Ecosystem
Foundation Model Monthly Token Growth
10
480
1,300
0
200
400
600
800
1,000
1,200
1,400
May-24
Jun-24
Jul-24
Aug-24
Sep-24
Oct-24
Nov-24
Dec-24
Jan-25
Feb-25
Mar-25
Apr-25
May-25
Jun-25
Jul-25
Aug-25
Sep-25
Oct-25
Reasoning Tokens Explode
Open Models
Runtimes
Inference
1
98%
2%
70%
30%
Closed Models Open Models
2024A 2025E
Tokens Processed (T)
This presentation includes proprietary information of Battery Ventures
Early leaders are racing to vertically integrate…
Players across the stack are moving up and down the value chain to build stronger moats and defensibility
13 Sources: Company filings.
Strong/mature presence Emerging/partial presence Weak or absent
Own the Infrastructure Own the Models Own the Apps
AI ApplicationsFoundation ModelsCloud Providers
Silicon TPUs Maia 100 Trainium
Inferentia
NVIDIA
AMD
Trainium
Inferentia
TPUs
NVIDIA
Infrastructure GCP Azure AWS Azure AWS / GCP
Baseten
Fireworks
Together
Applications
AI Search
Gemini App
Antigravity
CoPilot Kiro
Amazon Q ChatGPT Claude Code
Cursor IDE
Cursor Agent
Bugbot
Models Gemini
Gemma OpenAI Nova GPT Claude
Composer -1
Claude/Gemini/OpenAI
This presentation includes proprietary information of Battery Ventures
…Which may improve the currently inverted margin structure
While silicon and cloud infrastructure command a majority of the margin today, value is beginning to expand up the stack
14 Sources: Battery Ventures estimates.
SaaS
Gross Margin
AI
Gross Margin
Future
Value Capture Path to AI Margin Expansion Up the Stack
Application
80%+ 0% - 30%
•
AI margins reflect distribution and market -share capture
•
Long - term pricing shifts toward value - based/outcome pricing
•
Cost of intelligence falls via optimization, boosting gross margins
Model
Inference
N/A 30% - 60%
•
Token cost deflation via smaller, specialized models and open models
•
Smarter routing to cheapest effective models
•
Improved efficiency techniques such as caching and speculative decoding
•
Workflow stickiness creates pricing power beyond raw compute
Cloud
Infrastructure &
Platform
60% 60%
•
Scale benefits are offset by intense competition
•
High CapEx requirements limit margin expansion
Chips
45% 75%
•
Supply catches up to demand, reducing pricing power
•
Cloud Providers vertically integrate, pressuring incumbent margins
This presentation includes proprietary information of Battery Ventures
State of VC Funding: AI vs. Non -AI Companies
VC funding is concentrated in AI deals
15 Note: 1. Denotes a past or current Battery company. For a full list of all Battery investments, please click here.
Sources: Pitchbook.
$124 $121 $132
$257
$168
$103 $107 $89
$26 $35 $43
$103
$68
$64
$108 $161
$149 $156 $175
$360
$236
$166
$216
$250
19% 20% 22% 23% 24% 28% 33% 32%
17% 22% 25% 29% 29% 38%
50%
64%
2018 2019 2020 2021 2022 2023 2024 2025 (YTD)
AI represents 64%
of VC funding, despite
only accounting for
32% of deals
4 Companies =
40% of funding
A small number of large -scale AI companies now absorb the majority of venture dollars despite accounting for only one -third of t he deal volume
VC Funding Value ($B)
AI as a % of Venture Deals
1
AI VC Deals Ex-AI VC Deals
AI Deal Funding % AI Deal Count %
This presentation includes proprietary information of Battery Ventures
The public market is rewarding early AI beneficiaries
Incumbents are building AI products, buying AI startups and hiring AI talent to capture value in the public market
16
Note: "AI Beneficiaries" classification is based on Battery Ventures internal analysis and includes: ADBE, APP, CRM, CRWD,
CYBR, DDOG, DOCS, FIG, FROG, MDB, NET, NOW, PLTR, RBRK, SHOP, SNOW, and ZS. Market data as of 12/15/25.
Sources: CapIQ, Company filings.
18.6x
6.2x 6.0x
4.6x
3.7x
0.0x
5.0x
10.0x
15.0x
20.0x
AI Beneficiaries SaaS 1.0
(>25% Growth)
SaaS 1.0
(15%-25%
Growth)
SaaS 1.0
(Total Bucket)
SaaS 1.0
(<15% Growth)
EV/NTM Revenue: AI Beneficiaries vs SaaS 1.0
$16
$40
$52
$74
$4 $6 $5 $5
$0
$20
$40
$60
$80
2021.5 2022 2022.5 2023 2023.5 2024 2024.5 2025 2025.5
AI Beneficiaries SaaS 1.0
Median Market Cap ($B): AI Beneficiaries vs SaaS 1.0
Regardless of growth, public SaaS 1.0 companies
without an AI story are capped at ~6.0x EV/NTM revenue
This presentation includes proprietary information of Battery Ventures
IPO candidates have reached unprecedented scale and growth
AI-native companies are approaching IPO scale, not only replacing legacy software but also capturing spend that once went
to services and human labor
17 Note: Data revenue at IPO date exclusively represents software and infrastructure companies. 1. Denotes a past or current Bat tery company. For a full list of all Battery investments, please click here.
Sources: CapIQ, ARR.Club.
$100M+ ARR$200M+ ARR$1B+ ARR
$381
$436
$766
$878
$0
$100
$200
$300
$400
$500
$600
$700
$800
$900
$1,000
2018-2019 2020-2021 2022-2023 2024-2025
Median NTM Revenue at IPO ($M) Emerging AI Companies Nearing IPO Scale
1
1
1
1
1
1
1
1
This presentation includes proprietary information of Battery Ventures
AI is on the cusp of unlocking a multi-trillion-dollar market
AI outcomes will be larger than those of any prior platform shift
18 Sources: Gartner, Battery Ventures estimates.
($ in billions) Application Software Infrastructure Software
$490
$431
$1,719
$1,460
$921
$4,100
Cloud Software + Services
Automation
+ Incremental
Human Labor
Displacement
AI Software
Software TAM Additional Disruption Opportunity with AI AI Software TAM
This presentation includes proprietary information of Battery Ventures
This presentation includes proprietary information of Battery Ventures
Operational Best
Practices
19
This presentation includes proprietary information of Battery Ventures
Pick the right entry wedge and expand into adjacent products and verticals
20
Application Proprietary Models
Proprietary Models Application
Code
Completion
CI/CD, testing,
code review, etc.
Claude Sonnet
Models
More
Models
01
Wedge
(Land + Distribution)
Moat
(Durability)
02
Open source/proprietary models to diversify
away from AI Lab models Claude Code
Horizontal Expansion (Product Breadth)
Vertical Expansion (Product Depth)
A targeted land motion with quick time -to-value unlocks distribution while product depth and breadth create durable moats
This presentation includes proprietary information of Battery Ventures
Align your KPIs to the modern AI operating metrics
21
SaaS 1.0 AI - NativeCommentary
AI businesses are growing faster than SaaS 1.0 as immediate productivity gains
and faster adoption cycles compress what used to take years into months.
Early -stage AI companies often trade growth and distribution for margins,
which makes validating long - term scalable unit economics essential.
Gross retention is critical in AI because churn signals experimentation, while
strong retention shows real adoption and product stickiness.
Value is no longer tied to seats or licenses. Real usage is the clearest measure
of whether customers are consistently realizing value.
Magic number and burn ratio still matter because they validate whether the
business can scale efficiently.
Metric Target
ARR Growth 2x –3x
NDR 130%+
Magic # 0.8x+
Burn Ratio < 3x
Metric Target
ARR Growth 5x –10x
Gross Margin 20% -40%
Gross Retention
80%+
Usage DAU/WAU/MAU
Magic # 1.0x+
Burn Ratio < 2.0x
As AI software shifts from tools that assist work to agents that autonomously complete it, company metrics must evolve as wel l, capturing
not just revenue growth and efficiency but also product usage, customer value, and unit economics
Sources: Battery Ventures estimates.
This presentation includes proprietary information of Battery Ventures
Benchmark your GTM to a more efficient post-AI framework
22
Rep Quota
$1.0M - $1.2M $1.2M - $1.5M
Quota Measurement Period
Annually/Quarterly Annually/Quarterly/Monthly
Ramp Time
9-12 months 6-9 months
Conversion Rate (Oppty
-
> Close)
20% - 30% 40% - 50%
Attainment Targets
70% attainment/25% time selling 80% attainment/50% time selling
Team Structure
AE:SE | 2:1
AE:SDR | 3:1
AE:SE | 3:1
AE:SDR | 5:1
CAC Payback
12-18 months <12 months
LTV:CAC
3:1 4:1
S&M % of Revenue
50% - 60% 30% - 40%
Pre -AI Post - AI
AI is reshaping GTM by driving higher productivity, faster ramps and fundamentally better unit economics
Sources: Battery Ventures estimates.
This presentation includes proprietary information of Battery Ventures
AI - Powered ICP Targeting and Qualification
Unlock a new era of demand generation with AI powered strategies
23
AI is unlocking a new growth blueprint for modern demand generation
1. Multi-dimensional segmentation: Blend first -party and third -
party signals, including firmographics, growth rate, web traffic,
product usage and technographics to generate higher -quality,
higher -intent leads.
2. ICP learned from real wins: Models train on closed -won deals to
score account fit and intent, instead of relying on static
personas.
3. Every win triggers a lookalike loop: When a deal closes, agents
find and enrich lookalike accounts, identify key buyers, and hand
reps a prioritized list with research and messaging.
4. Agents cover every step of the demand -gen funnel: build and
audit pipeline, score, engage and route leads, analyze
closed -lost reasons and update CRM fields automatically,
allowing reps to focus on closing deals.
AI in Demand Generation
New data and intent signals to surface
higher quality leads
Always-on agents to qualify inbound
Personalized engagement: right person,
right message, right time
Agents for deeper account research
and automated plays and sequences
✓
✓
✓
✓
This presentation includes proprietary information of Battery Ventures
Price for value and outcomes
24
Application Software Value -Based Pricing Infrastructure Software
Seat -based pricing
Predictable, stable revenue
Easy for buyers to understand
Simple to implement and measure
# of seats flat / going down as AI
does the work
Human productivity value not captured
Seat -based fee
+ pay per action or outcome
Captures AI value
Aligns pricing with successful
customer outcomes
Greater willingness to pay
Revenue naturally scales with
customer maturity / growth
Minimum platform fee
Consumption -based pricing
Low -friction land
Aligns pricing with usage
Volatile revenue
Complex billing and unpredictable
costs for customers
Under monetizes small customers
and early usage
Note: 1. Denotes a past or current Battery company. For a full list of all Battery investments, please click here.
11
As AI products continue to drive greater human productivity, application and infrastructure companies are adopting value -based p ricing to
align customer spend with successful business outcomes
1
This presentation includes proprietary information of Battery Ventures
Build enduring moats
Moat Importance by Company Stage
25
Early-Stage Growth-Stage Late-Stage
Team + Domain Expertise
Sticky Workflow
Proprietary Data Capture
Product/Workflow
Expansion
Wedge
Data Flywheel
Product Velocity
Team +
Domain Expertise
Integrations
Data
Workflows
Product Velocity
Workflow and
Integration Depth
Customer Usage &
Feedback
Moats aren’t static, and AI startups must continue to deepen them as they scale —or risk erosion
This presentation includes proprietary information of Battery Ventures
Application Layer
Deliver best-in-class product experiences with tightly coupled infra and apps
In SaaS 1.0, infrastructure lived behind the product, but in AI -native products it becomes the product experience
26
SaaS 1.0 –Infrastructure Behind the Scenes AI Native –Infrastructure as the Core Product Experience
Application Layer
Infrastructure Layer
UI / UX
Business logic, workflows
Dashboard, reports, analytics
Compute
Storage
Networking
Data Infrastructure
User only sees the
application layer
Infrastructure Layer
UI / UX
Agent workflows & tools
Context & memory
Compute
Storage
Networking
Data Infrastructure
Evals
Data retrieval
Intelligent routing
Models & inference
Infrastructure choices are
now part of the product
experience
This presentation includes proprietary information of Battery Ventures
Rewire your org chart for intelligence
In AI -native orgs, every function becomes technical as teams build and maintain AI systems that influence product and user value
27
Product
Engineering
Sales / GTM
SaaS 1.0 AI-Native
The Shift
CS / Support
Function Focus Function Focus
Product Manager Managing roadmaps, user stories,
feature backlogs, defining UI/UX
workflows
AI PM, Context
Engineering
Managing evals, system prompts and
design, coding prototypes, steering
model and agent behavior
Frontend /
Backend Developer
Building deterministic business logic,
CRUD APIs/Apps, web services/
UI, and state management
Applied AI, Research,
Inference Engineer
Building and tuning models,
integrating AI system (tools, memory),
inference speed and reliability
SDRs / BDRs Manual pipeline generation,
lead qualification,
high - volume outbound
GTM Engineer Programmatic lead gen, data
enrichment, building automated
agents for targeted outbound
Customer
Success/Support
Relationship management, expansion,
renewals, and churn prevention
Forward
Deployed Engineer
Ties together engineering, product
and customers to deliver value and
expand use cases
This presentation includes proprietary information of Battery Ventures
17.3x
37.3x
49.4x
10.0x
0.0x
10.0x
20.0x
30.0x
40.0x
50.0x
60.0x
Seed Series A Series B Pre-IPO
Pick the right partners and capital structure
28
Illustrative Forward ARR Multiples by Deal Stage
Transitioning from venture -market premiums to public -market discipline is a critical evolution that requires the right long -term partners and
capital structure
Note: Based on illustrative Battery Ventures analysis. 1. Denotes a past or current Battery company. For a full list of all Battery investments, please click here.
Sources: CapIQ, Battery Ventures data and Pitchbook.
Unicorns Growing Into Public Market Valuations
1
1
1
1
1
1
1
1
1
1
1
1
This presentation includes proprietary information of Battery Ventures
Navigate strategic partnerships
29
Distribution Capital Product/Technology
Value
•
Access to a scaled customer base
•
Credibility and brand validation
•
Co -marketing/co -selling
•
Balance sheet strength without
heavy dilution
•
Market validation
•
Greater organizational access
•
Accelerated product development
•
Access to technical resources
•
Preferential support and access
Stakeholder
•
GTM
•
Corporate development / finance
•
R&D
Risks
•
Reduced brand ownership
•
Over -dependence on
partner -led deals
•
Perceived loss of independence
•
Potential negative signaling for
future investors and acquirers
•
Platform lock -in
•
Roadmap dependency
•
Exposure to partner product
competition
Example
/ / /
1
Note: 1. Denotes a past or current Battery company. For a full list of all Battery investments, please click here.
Leverage strategic partners to accelerate distribution, unlock new capital sources and drive product velocity
This presentation includes proprietary information of Battery Ventures
This presentation includes proprietary information of Battery Ventures30
Themes
of Interest
This presentation includes proprietary information of Battery Ventures
Runtime AI infrastructure
31
Hardware
Infrastructure
Software
Infrastructure
Applications Business Workflow Applications
Middleware
Enablement/
Dev Workflow
Tool Usage &
Agent Auth
Web
Access
Post -
Training
Evals/
Observability
Execution
Sandbox
Frameworks &
Libraries
Model
Routing
Note: 1. Denotes a past or current Battery company. For a full list of all Battery investments, please click here.
1
1
Technical Applications
Foundation Models Inference & Training Databases
1 1
1
This presentation includes proprietary information of Battery Ventures
AI-native software development lifecycle
32
Secure
Code Gen/Agents
Build
Vibe Coding
Sandboxes
Deploy/Run/Monitor
Databases
Runtimes
AI SRE
Testing
Test/Review
Code Review
Note: 1. Denotes a past or current Battery company. For a full list of all Battery investments, please click here.
1
1
1 1/
This presentation includes proprietary information of Battery Ventures
Securing the agentic attack surface
33
SIEM
Artemis
SOC Automation
Endpoint Security
Agent Auth
Identity
Product Security
Email Security
Vulnerability Mgmt.
Training
Offensive Security
Note: 1. Denotes a past or current Battery company. For a full list of all Battery investments, please click here.
1
1
1
1
This presentation includes proprietary information of Battery Ventures
Vertically integrated AI solutions for key technical personas
34
Security Operations CenterITSM
Software Development
Observability
11
1
Note: 1. Denotes a past or current Battery company. For a full list of all Battery investments, please click here.
1
1
