Aristotle Beta
Role · Product Designer 0 → 1
Timeline · Oct 2025 - Feb 2026
Team · 2 designers + 1 engineer
The Problem
Scientists don't trust AI. Not because they're resistant to technology — because they're trained to question everything. Most AI tools respond with false confidence. Aristotle needed to do the opposite: be skeptical with the scientist, not just answer them.
What I Started With
0 → 1 No UI. No structure. No design system.
The Core Tension
How do you show uncertainty without losing confidence?
Every decision ran through this. Transparency features had to feel rigorous, not apologetic. The interface had to hold complexity without becoming a cockpit.
Key Decisions
Sidebar as the primary layer.
Started minimal — contained footprint, clean. Wrong call. As Double Check, System Thinking, and source previews came in, minimal collapsed. Pivoted to an expanding sidebar: Aristotle's reasoning lives there, separate from the research canvas. Two things visible, neither competing.
Transparency as the feature
Four models, one mental model
Where We Started
The first version of Aristotle had three models: Explore, Generate, and Verify. Scientists didn't choose between them — Aristotle chose for them, routing each prompt to the right model automatically. The logic made sense: reduce friction, hide complexity, let researchers just ask.
No sidebar. Clean canvas. Minimal footprint.
It worked until it didn't.
Where It Broke
As the product deepened, the invisible routing started working against us. Researchers couldn't see why Aristotle responded the way it did. When a response felt off, there was nothing to interrogate. The model choice was a black box, and scientists trained to question methodology couldn't accept a black box.
Two things had to change.
The Pivot
We gave researchers control of model selection. Not as an afterthought — as a signal. Choosing a model is choosing a research posture. Making that choice explicit gave scientists agency over how Aristotle was thinking, not just what it was saying.
We introduced the expanding sidebar as a dedicated reasoning layer. Aristotle's thinking lives there — separate from the research canvas, visible without competing with it. The sidebar didn't just solve a layout problem. It became the foundation for everything that came after.
What Shipped
X1 model responses · Projects · Double Check · System Thinking · In-line definitions · Source previews
Live to qualified researchers — February 2026
X1 Spark
Hypothesis generation through self-skeptical reasoning. Scientists don't always arrive with a fully formed question — Spark is built for that earlier, messier phase. It surfaces cross-disciplinary directions that wouldn't come up in a standard literature search, and it does it by challenging its own outputs as it goes.
X1 Search
Literature synthesis at depth. Search structures evidence across sprawling research areas and makes every claim traceable. The sidebar carries the reasoning trail — steps like "Scoping the Question," "Structuring the Approach," "Checking the Numbers" — so researchers can follow the methodology, not just read the conclusion.
X1 Verify
Skepticism as the primary mode. Verify challenges its own reasoning before delivering an answer. The sidebar surfaces this visibly: Answer Confidence, Source Verification, Mathematical Validation, Causal Reasoning — each as a discrete, inspectable layer. For scientists who need to know not just what Aristotle concluded, but whether it held up.
X1 Instant
Focused questions, fast. Source-backed answers in seconds, without the overhead of a full research workflow. The interface stays lean — no expanded sidebar, no reasoning trail — because the use case doesn't need it. Instant trusts the researcher to know when a lighter touch is enough.
Double Check
AI that sounds confident is a liability in science. Double Check automatically verifies factual claims inline and flags weak assertions before a researcher builds on them. Three states — Verified, Uncertain, Flagged — each with supporting evidence visible on expand. The design problem was making this feel like a tool for rigorous researchers, not a disclaimer for a nervous product.
Inline Definitions
Dense scientific language creates friction across disciplines. Inline definitions surface context exactly where it's needed, without breaking reading flow. The key decision was keeping them genuinely inline — no modal, no navigation away. Present when needed, invisible when not.
Projects
Research isn't a single query. Projects create dedicated memory spaces where related work stays together — chats, files, context — so Aristotle maintains coherent recall across sessions. The structure mirrors how researchers actually work: sustained lines of inquiry, not one-off searches.
Why This Paper
Every citation carries an implicit question: why does this source matter here? Why This Paper answers it directly — surfacing the goal, the contribution, the methodology, and the limitations for each source. It closes the gap between AI output and primary source verification without making the researcher leave the interface to do it themselves.
What Comes Next
Aristotle Beta was the foundation. Establishing trust, transparency, and the reasoning layer that scientists needed before anything more ambitious could be built.
But the goal was never a better research tool.
Autopoiesis is building AI that can do science autonomously. Not as an assistant that responds when asked, but as a system with the judgment to know which directions are worth pursuing before the evidence arrives. One that sustains inquiry under uncertainty, follows weak signal through long silences, and doesn't exit when the answer isn't obvious.
Beta proved that skeptical scientists could trust an AI co-scientist. That trust is the prerequisite for everything that comes next.
Every design decision in Beta, the sidebar, the model picker, the transparency features, was in service of that longer arc. Building the scaffolding for a system that eventually doesn't need a human to keep it moving.
Live to qualified researchers — February 2026