Thesis

The Context We Give Machines Determines What They Become

Machines operate continuously. What they ingest—and how that information is structured—determines how well they can judge, infer, and act. Context is not auxiliary. It is the substrate of machine intelligence.

Machines now consume more text than any human system ever could. They ingest books, papers, websites, and archives at internet scale. Almost all of this material was written for human readers. Much of it is optimized for clicks, persuasion, or distribution rather than accuracy.

We now rely on machines for judgment, synthesis, and decision support. Their outputs are constrained by their inputs. It's a property of learning systems, not philosophy.

Human progress accelerated once knowledge became durable and cumulative. Machine reasoning follows the same logic, but at a different scale. Their capacity to consume information is effectively unbounded. Our ability to curate it is not.

Synorb produces content for machine reasoning and delivers it as continuous streams built on a single ontology with shared taxonomies. The underlying premise is simple: most real-world questions reduce to people, organizations, and data—and how they change over time.

We model these primitives using a shared ontology and consistent taxonomies, then generate and maintain structured, high-signal content at machine scale.

• Discovery Streams maintain durable, source-grounded records about people and organizations, designed for recall and verification rather than traffic or engagement.
• Narrative Streams translate structured and time-series data into precise, machine-readable text, explicitly linked to entities, relationships, and events.
• Research Streams assemble citation-ready analysis from trusted inputs, operating on top of the same shared structure.
• For operators, the gaps show up as concrete failure modes: stale context, conflicting claims, citations you can't defend, and brittle reasoning under distribution shift. Better models help, but the ceiling is set by the context layer.

These streams exist because unstructured, human-written prose doesn't hold up under analysis or planning.

Systems capable of forming hypotheses, designing therapies, or engineering new materials require continuous access to structured, reliable knowledge. That doesn't come from fragmented text or engagement-optimized content.

Synorb organizes claims, data, and activity across people and organizations into a continuously updated knowledge base for machine use.

Our Machines Read Everything. Most of It Wasn't Written for Them.

Engineered signal that turns the world's largest library into usable knowledge—and expands it by translating numbers into narratives.

In the past five years, frontier models have ingested a large fraction of what's publicly available in text. A single large training run now spans roughly 15 trillion tokens—on the order of 11 trillion words.

The corpus is immense. Common Crawl adds billions of pages each month and has indexed hundreds of billions overall. The Internet Archive's Wayback Machine is approaching a trillion snapshots. This is the largest public, machine-readable library humanity has ever assembled.

But its apparent vastness is misleading. Most publishing concentrates around a narrow set of attention-bearing topics, and much of the marginal output is duplication: variations of the same summaries and takes, tuned for ranking and persuasion. The ceiling is human attention-hours—what publishers believe human audiences will click, read, and share.

The web is also an economic system. In the United States alone, digital advertising clears hundreds of billions of dollars annually. Incentives reward ranking, clicks, and retention. Pages are tuned to win attention, not to support reasoning.

In that economy, "coverage" is a mirage. Production clusters where attention monetizes, and the marginal page is usually recombination: duplicated explainers, recycled commentary, SEO-shaped templates. You get more pages without getting more decision-relevant facts.

For models, the web mostly arrives as prose with weak structure—an undifferentiated mass of text where who said what, about whom, when, and with what evidence is often missing or implied. Retrieval then returns clusters of near-duplicates, so systems spend tokens re-reading instead of updating beliefs.

Even curated corpora carry boilerplate and duplication, and filtering choices change what gets represented. The open web is a noisy prior, not a knowledge base.

Public text is finite, and training demand is catching up to it. You can see it in practice: better-curated, better-structured corpora beat raw scrape volume. The leverage is in curation, provenance, and refresh.

Search helps machines find pages. Synorb begins after retrieval. We rewrite what the web and trusted datasets say into a single, structured, machine-native corpus anchored to people, organizations, and data.

Machines don't have query windows. They can read continuously, accumulate context over time, and benefit from coverage that expands beyond what human attention economics will ever fund. A corpus built for humans is large, but structurally limited in scope for infinite-attention consumers.

That requires a foundation designed for machine use.

We Taught Machines to Read. Now We Give Them Something to Build On.

Machine progress follows the same rule as human progress: durable knowledge compounds.

Civilization advances when knowledge becomes durable and cumulative. Clay tablets carried laws across generations. Paper made ideas portable. The printing press multiplied access to books. Computers compressed centuries of correspondence into seconds. The web gave billions access to the same bodies of knowledge.

Each shift changed how people reasoned, planned, and built. Knowledge began to compound. Progress stopped restarting from zero.

Machine intelligence follows the same pattern. Systems we ask for judgment, synthesis, and original output won't reach their potential if they rely only on a corpus built for human consumption.

That corpus is constrained by human attention, human lifespans, and human economic incentives. It was built around human query moments—when someone searches, reads, and moves on.

To move beyond those limits, machines need a foundation designed for machine use. They require structured, high-signal knowledge that extends beyond what any human team can assemble or maintain.

With that foundation, machines operate differently. They reason over decades instead of documents. They detect weak signals across complex systems. Progress shifts from retrieval to construction.

The choices we make now decide whether machine intelligence compounds on verifiable truth—or on drift.

Building Machine-Scale Libraries

Scale without verification is a liability.

Building for machines means maintaining structured, high-signal knowledge that can be verified and revisited. Without it, scale amplifies error.

In practice, "building a library" looks like data contracts: versioning, lineage, refresh schedules, backfills, quality gates, and observability. If you can't trace a claim, reproduce it, and monitor drift, you don't have a knowledge layer—you have a risk surface.

One Ontology: People, Organizations, Data

Most real-world knowledge reduces to people, organizations, data, and their relationships.

Synorb uses a single ontology with shared taxonomies across these primitives. Filings, research papers, earnings calls, blog posts, and structured feeds are normalized into the same backbone.

This lets machines traverse the world—from people to organizations to data and back again—across domains and over time.

Discovery: Durable, Verifiable Knowledge

Discovery Streams prioritize durability and verification. Instead of ranking information by visibility or engagement, they organize knowledge around what holds up: traceable sources, clear attribution, and stable entity resolution.

Human-oriented search rewards attention. Discovery Streams reward provenance.

Narrative: Data Made Legible

Modern systems generate data continuously. Machines can't reason over it until it becomes legible.

Narrative Streams translate structured and time-series data into explicit, citable statements linked back to underlying measurements. They're built for continuous ingestion: new measurements arrive, narratives update, and downstream systems receive attributable deltas instead of re-reading static documents.

Research: Analysis With Provenance

Traditional research was written for human readers, with limited cadence. Machines require the same depth at higher frequency.

Research Streams assemble citation-ready analysis from trusted inputs, drawing directly on Discovery and Narrative Streams. Sources are preserved. Assumptions are stated. Refresh is explicit.

Together, these streams form a coherent knowledge layer for reasoning systems. Because they share a single ontology, models can reason across them as one unified corpus rather than a collection of disconnected documents.

The Future We Feed Machines Is the Future We Inherit

A machine library isn't an archive. It's a maintained system: versioned, queryable, and refreshed.

It grows when new questions appear. It pulls from sources you can point to and defend. It stays current because stale context produces confident, wrong answers.

Most context stacks today assume a retrieval + process loop: fetch a small set of documents at query time, then generate an answer. That matches human query windows. Deployed agents change the posture. With effectively infinite attention-hours, the mode becomes listen + process: continuously ingest changes, maintain state, and reason between queries. That demands incremental, attributable updates—deltas you can inspect—not repeated reprocessing of the same documents.

Scale isn't "more pages." It's coverage, structure, and refresh that human publishing can't sustain. Where the web is thin—where truth lives in tables, time series, and raw feeds—the library has to translate measurement into explicit claims.

If you're building a corpus machines will rely on, the requirements are operational: verified sources, consistent structure, traceable claims, explicit assumptions, scheduled refresh, and fast access. In production, context must be cacheable, diffable, testable, and refreshable—because reasoning systems inherit whatever latency, inconsistency, and drift your pipeline allows.

Otherwise you're not building a library. You're accumulating drift.

As models take on more planning and decision work, the bottleneck isn't retrieval. It's trust. Systems fail when context is noisy, stale, or unaccountable.