What Is Squid and Why Does the Electricity Grid Need It?

Squid is an AI-powered grid planning platform that lives entirely in the browser and is designed to solve one of the most overlooked but critical problems in modern infrastructure: how electricity grids are planned, updated, and defended as systems grow more complex. Founded in 2026 and backed through the Winter 2026 batch with Gustaf Alstromer as its primary partner, Squid positions itself around a simple but powerful idea — the electricity grid is the most important machine on Earth, yet the way it is planned still resembles file chaos rather than a modern engineering workflow.

At its core, Squid provides utilities and grid operators with a single, trusted model of their network: a live, versioned representation of substations, transmission lines, and network topology, enriched with assumptions, evidence, and decision context. Instead of grid decisions being scattered across emails, spreadsheets, PDFs, and static GIS exports, Squid consolidates everything into one coherent workspace where planning happens transparently and consistently.

By focusing on unification rather than just analysis, Squid aims to redefine how grid decisions are made — not by replacing engineers’ expertise, but by giving that expertise a shared, defensible foundation.

Why Is Grid Planning Still Broken in 2026?

Despite massive advances in energy generation, electrification, and analytics, the process of planning electrical grids remains fragmented. Most grid organizations already have sophisticated tools for modeling load, capacity, and reliability. The problem is not a lack of computation — it is the absence of a shared source of truth.

In practice, grid planning decisions are made across a patchwork of systems. Technical models live inside specialist software accessible only to certain teams. Supporting evidence is stored separately in slide decks, documents, and spreadsheets. Assumptions — often the most critical part of any planning decision — frequently exist only in people’s heads or informal conversations.

This fragmentation leads to familiar pain points: planning cycles stretch longer than necessary, teams argue over which model version is correct, and decisions become difficult to justify months later when conditions change. When regulators, stakeholders, or internal leadership ask why a particular choice was made, the trail of evidence is often incomplete or inconsistent.

Squid starts from the premise that the grid itself — not the files describing it — should be the center of the workflow.

What Core Problem Does Squid Actually Solve?

Squid addresses the gap between analysis and decision-making. While traditional tools excel at running simulations or producing reports, they do not capture how decisions evolve over time or how evidence supports those decisions.

The platform introduces the idea of a “trusted model” — a live representation of the grid that is always current, versioned, and contextualized. Every change to the network, whether it is a new substation, a modified line, or a planning assumption, is tracked and comparable to previous states.

By anchoring evidence, assumptions, and workflows directly to the grid model, Squid ensures that decisions are not only faster to make but also easier to defend. The question shifts from “Which file is right?” to “Which version of the grid are we approving, and why?”

This reframing transforms grid planning from a fragmented documentation exercise into a structured, repeatable process.

How Does Squid Turn Messy Grid Data into a Single Model?

One of the hardest challenges in grid planning is data unification. Utilities often operate with datasets coming from multiple sources: legacy systems, GIS exports, third-party studies, and internal models built over years. Keeping these synchronized is labor-intensive and error-prone.

Squid tackles this by ingesting and unifying messy grid data into one coherent model that reflects the current state of the network. Rather than treating data imports as one-off exercises, the platform is designed to keep the model continuously updated as changes occur.

This unified model becomes the backbone of all planning activities. Engineers and planners no longer need to reconcile multiple representations of the same network; instead, they collaborate on a shared view that evolves in real time. The result is not just cleaner data, but a clearer understanding of how the grid actually looks at any given moment.

Why Is Versioning Central to Squid’s Approach?

Version control is standard practice in software development, but it has rarely been applied systematically to grid planning. Squid brings versioning to the heart of infrastructure decision-making.

Every change to the grid model is tracked, allowing teams to compare scenarios, review historical states, and understand how a particular configuration came to be. This makes planning discussions more concrete. Instead of debating abstract proposals, teams can point to specific versions of the grid and evaluate trade-offs explicitly.

Versioning also enables safer experimentation. Planners can explore alternative configurations without overwriting existing assumptions, making it easier to test ideas and adapt as conditions change. In an era where electrification is accelerating and uncertainty is the norm, this flexibility is essential.

How Does Squid Attach Evidence and Assumptions to Decisions?

One of Squid’s defining features is its emphasis on context. Grid decisions rarely hinge on raw data alone; they depend on studies, regulatory constraints, forecasts, and engineering judgment. Yet this context is often lost once a decision is implemented.

Squid allows users to attach evidence and assumptions directly to the grid model where decisions are made. Reports, datasets, and explanatory notes become part of the model itself rather than external references. This ensures that anyone reviewing a decision — whether immediately or years later — can see not just what changed, but why it changed.

By making assumptions explicit and persistent, Squid reduces institutional memory loss and supports more rigorous internal and external reviews.

What Planning Workflows Does Squid Enable?

Beyond static modeling, Squid is designed to support repeatable workflows across common grid planning activities. These include long-term capacity planning, new connections, and network changes driven by electrification or policy shifts.

By standardizing workflows around a shared model, Squid helps teams move faster without sacrificing rigor. Planning processes that once required manual coordination across departments can be executed within a single workspace, with clear checkpoints and traceable outcomes.

This repeatability is particularly valuable as grids face increasing pressure to scale. As demand grows and timelines shrink, utilities need planning systems that can keep up with both technical complexity and organizational reality.

Why Is Now the Right Time for Squid?

The timing of Squid’s emergence is closely tied to the broader energy transition. Electrification is accelerating across transportation, heating, and industry, placing unprecedented demands on existing grid infrastructure. In many regions, the grid has become the bottleneck that determines how quickly new technologies can be deployed.

Traditional planning workflows — built around files, handoffs, and static reports — struggle to scale at this pace. Decisions take too long, coordination breaks down, and the cost of mistakes increases.

Squid’s founders argue that modern grid challenges require modern planning tools. By centralizing models, context, and workflows, Squid aims to help utilities respond faster without compromising reliability or accountability.

Who Are the People Behind Squid?

Squid was founded by Conor Jones and George Kolokotronis, engineers who have worked inside organizations such as National Grid, Octopus Energy, and AWS. Their experience spans both large-scale infrastructure and modern cloud systems, giving them a firsthand understanding of the gap between how grids are operated and how they could be managed.

Having “lived the pain” of fragmented planning firsthand, the founders set out to build the system they wished they had. Rather than approaching the problem from a purely theoretical standpoint, Squid reflects practical lessons learned from working within complex, regulated environments.

This insider perspective shapes the product’s focus on trust, traceability, and usability — qualities that matter deeply in infrastructure contexts.

How Does Squid Fit Into the Broader Energy Ecosystem?

Squid does not attempt to replace existing analysis tools or operational systems. Instead, it acts as a connective layer that brings coherence to the planning process. By sitting at the intersection of data, decisions, and workflows, Squid complements specialized tools while addressing their coordination gaps.

This positioning makes Squid relevant not just to engineers, but also to planners, managers, and stakeholders who need visibility into how decisions are made. As energy systems become more interconnected and scrutinized, this shared understanding becomes increasingly valuable.

What Could Squid Mean for the Future of Grid Planning?

If successful, Squid could help shift grid planning from a document-centric process to a model-centric one. Decisions would no longer be buried in files but embedded in a living representation of the network. Accountability would be built into the system, and institutional knowledge would persist beyond individual projects or teams.

In a world where the electricity grid underpins nearly every aspect of modern life, improving how it is planned has far-reaching implications. Squid’s vision suggests that faster, clearer, and more defensible decisions are not just a productivity gain — they are a prerequisite for scaling electrification responsibly.

By treating the grid as the central object of collaboration rather than an output of disconnected tools, Squid aims to modernize one of the most critical planning processes on Earth.