EA has made remarkable progress—but a few structural challenges continue to hold us back. I’ve been working on a system called KnowledgeTensors that offers a way to address some of these directly. Here’s how it lines up with four persistent problems in the movement:
1. Evaluative Ambiguity We ask “What does the most good?”—but comparing across causes (e.g. malaria nets vs. AI safety) often feels philosophically unstable. KnowledgeTensors use a common evaluative metric called LifeScore, which quantifies expected impact on well-being across time, geography, and demographics. It makes cross-domain comparisons more consistent and transparent.
2. Cognitive Bottlenecks & Attention Scarcity Much of EA discourse lives in blog posts, podcasts, and reports—formats that rely on limited human attention and are prone to impression-based reasoning. KnowledgeTensors remove this bottleneck by letting users query directly for the highest-impact actions. Causes are ranked by metrics, not branding or salience.
3. Epistemic Fragility A lot of EA reasoning is built on informal natural language arguments. That makes them hard to verify, audit, or replicate—especially at scale. With KnowledgeTensors, claims are stored as structured, code-backed “KnowledgeCells.” Every result is traceable, testable, and version-controlled.
4. Organizational Inertia Large EA orgs do a lot of good—but they also shape the funding landscape, which can lead to path dependency. Once evaluative frameworks are in place, they’re hard to shift. KnowledgeTensors decentralize evaluative authority. As new expert knowledge enters the system, impact scores update automatically—no need to overhaul institutions or wait for organizational buy-in.
How KnowledgeTensors Could Help Solve Key Problems in the EA Ecosystem
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EA has made remarkable progress—but a few structural challenges continue to hold us back. I’ve been working on a system called KnowledgeTensors that offers a way to address some of these directly. Here’s how it lines up with four persistent problems in the movement:
1. Evaluative Ambiguity
We ask “What does the most good?”—but comparing across causes (e.g. malaria nets vs. AI safety) often feels philosophically unstable.
KnowledgeTensors use a common evaluative metric called LifeScore, which quantifies expected impact on well-being across time, geography, and demographics. It makes cross-domain comparisons more consistent and transparent.
2. Cognitive Bottlenecks & Attention Scarcity
Much of EA discourse lives in blog posts, podcasts, and reports—formats that rely on limited human attention and are prone to impression-based reasoning.
KnowledgeTensors remove this bottleneck by letting users query directly for the highest-impact actions. Causes are ranked by metrics, not branding or salience.
3. Epistemic Fragility
A lot of EA reasoning is built on informal natural language arguments. That makes them hard to verify, audit, or replicate—especially at scale.
With KnowledgeTensors, claims are stored as structured, code-backed “KnowledgeCells.” Every result is traceable, testable, and version-controlled.
4. Organizational Inertia
Large EA orgs do a lot of good—but they also shape the funding landscape, which can lead to path dependency. Once evaluative frameworks are in place, they’re hard to shift.
KnowledgeTensors decentralize evaluative authority. As new expert knowledge enters the system, impact scores update automatically—no need to overhaul institutions or wait for organizational buy-in.
Please let me know your thoughts on this.