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Squid 🦑
AI powered grid planning in your browser
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Inside The Issue
Spotlight
What if the six year wait to connect a solar farm was a software problem, not infrastructure?
Quick Pitch: Squid is building a browser based grid planning platform that helps utility operators manage network data and decisions in one place. It centers on a live, versioned model that enables AI driven planning workflows.
The Problem
Fragmented Data: Grid data sits across models, mapping systems, PDFs, and spreadsheets with no single source of truth.
Manual Coordination: Planning teams must reconcile conflicting data across tools before making decisions.
Long Delays: Over $5 trillion in projects are stalled, with average connection wait times of six years.
Snapshot
Industry: Energy infrastructure software
Headquarters: London / San Francisco
Year Founded: 2026 (YC W26)
Traction: Partnered with National Grid Electricity Distribution DSO within 60 days, with additional deals in procurement
Founder Profiles
Conor Jones, Co-Founder, CEO: Former youngest Director at National Grid Transmission, where he led a £10M+ annual team of 60+ engineers, and previously worked at Octopus Energy.
George Kolokotronis, Co-Founder, CTO: Cambridge Engineering graduate, former Head of Technology at Octopus Energy, and prior experience at AWS.
Funding
Current Round: Raising $3M (Seed)
Lead Investors: Y Combinator
Total Raised: $500K (Pre-Seed)
Revenue Engine
Land and Expand Model: Starts with a single planning workflow, then expands to multi million contracts as more workflows are added.
Enterprise Customers: Targets large grid operators with long term infrastructure budgets.
What Users Love
Unified grid model that brings together substations, lines, assets, and planning evidence
Version tracking that shows what changed, when, and why
AI agents that draft connection offers and surface investment decisions
Browser based access for cross team collaboration
Playing Field
Siemens PSS E: Core grid modeling tool but limited to specialists and siloed workflows
IBM Maximo: Asset management system without integrated planning workflows
Esri GIS: Spatial data layer without decision orchestration
Squid’s Edge: Brings these tools into one live model with AI agents on top.
Why It Matters
Global grid spending is rising fast, with over $470B expected in 2025 and more than $1.1T planned by US utilities through 2030, driven by data centers, EVs, and electrification. Planning systems built decades ago are not designed for this scale, creating delays that slow economic growth.
What Sets Them Apart
Deep Domain Expertise: Founders have 15 years of experience shipping technology inside major grid organizations.
Rapid Commercial Velocity: Secured a global grid leader as a customer within 60 days of launch.
Strategic Moat: Once Squid holds the live model of a grid, it becomes the foundation for other tools.
Analysis
Bulls Case 📈
Founders have strong market fit with former peers at major grid operators
$1.1T in committed US grid investment through 2030 supports demand
Serves as the infrastructure layer for AI agents, creating switching costs
Starts below procurement friction to move faster than typical utility sales cycles
Bears Case 📉
Long enterprise sales cycles in utilities slow adoption
Integration across legacy systems can delay deployment
Risk averse operators require high approval for workflow changes
Rate based approval for software can limit pricing and pace
Verdict
Squid is targeting a hidden choke point in grid planning where decisions stall not from capacity, but from coordination across fragmented systems. By owning the system of record for grid data, it positions itself upstream of both planning workflows and future AI applications.
The risk is less about demand and more about control. Utilities may resist centralizing decision making in a new layer, especially one that sits across existing tools. If Squid becomes the place where planning decisions are made and justified, it shifts from a tool to critical infrastructure.
Operator Notes
Investor Lens: Value accrues to the system that becomes the source of truth for decisions
Founder Lens: Start where decisions are audited, then expand as that record becomes hard to replace
Who's Hiring — 16 AI Startups That Raised $585M
This is the tier right below the headlines. Headcount plans are set, roles are still open, and early hires still get meaningful equity.
Mintlify — AI developer docs — hiring across engineering and product
Bluefish — AI brand visibility — hiring across engineering and GTM
Steno.ai — AI legal workflows — hiring across engineering and GTM
Tava Health — AI mental health — hiring across engineering and operations
Gather AI — Autonomous warehouse drones — hiring across engineering, operations, and field
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The Startup Pulse
Another happening week in startup funding. Three signals from this week:
Capital is shifting from models → into compute and energy
AI companies are locking in infrastructure at massive scale
Strategic deals are starting to matter more than traditional rounds
Recursive Superintelligence — Raised $500M+ at a ~$4B valuation, led by GV with Nvidia participation, to build self improving AI systems — pushing toward continuous learning beyond static model training.
Blue Energy — Raised $380M to develop modular nuclear reactors for data centers, targeting one of AI’s biggest constraints: reliable, colocated power.
Reliable Robotics Corporation — Raised $160M from investors including Lightspeed and Coatue to scale autonomous aircraft systems, starting with cargo and controlled routes.
Written by Ashher
