We keep trying to make AI smarter by letting it remember more.

Bigger models. Bigger context windows. More tokens.

It feels right, the way carrying a bigger backpack feels right before a long trip.

But intelligence isn’t measured by how much you carry.

It’s measured by how much you can throw away and still know where you’re going.
That’s the part we’re missing.

There’s a rare condition called hyperthymesia.

People with it can recall nearly every day of their lives. Nothing fades. It sounds like a superpower. It isn’t.

They struggle to move on because nothing becomes background. Every regret stays sharp. Every detour stays loud. The present never gets a clear stage.

Perfect recall makes living harder, not easier.

We’re doing a version of this to our machines. We celebrate longer context windows as if wisdom were just a larger buffer. It only benefits the platform providers: bigger context windows means more tokens to charge for (more utilisation).

But a longer scroll isn’t more insight. It’s just more scroll (for humans and for machines).

When you solve a hard problem, you don’t replay your entire past. You reach for a pattern. A principle. A sharp example. Everything else you forget on purpose.

That’s not a flaw; it’s the mechanism.

Intelligence compresses. It keeps the essence and discards the rest.

Call it systematic compression of context

• Summarize the idea to its transferable core.

• Drop the specifics that don’t change the decision.

• Keep the map, not every footstep.

Humans do this instinctively. The best thinkers compress best. Machines will need to learn it deliberately.

The frontier isn’t bigger context. It’s better compression.

Now widen the lens.

Individuals drown in their GPT/Claude chat sessions, slack and endless meeting transcript emails. GenAI amazingly creates lots of stuff quickly because it can. Teams forget why a decision was made. Companies lose the reasoning that still shapes their way forward.
Most organizations mistake volume for progress. They dump old PDFs, HR policies, and board decks into an AI layer and call it transformation. In reality, they’ve just given enterprise search a new UI. “Search” is one small part of information discovery — not a device of intelligence.

This is not a storage or indexing problem. It’s a compression problem.

Artificial intelligence turns into institutional intelligence only when an organization can compress its experience into usable, durable context — and then make that context easy to retrieve at the moment of choice.

Without compression, knowledge is a junk drawer.

With compression, it’s a playbook.

Systematic compression of context is a habit with three moves:

1. Collect broadly (machines).

Pull the raw material from docs, chats, tickets, repos. Don’t judge yet.

2. Compress aggressively (humans).

Summarize the decision, the why, and the transferable pattern. One paragraph beats ten. Name the principle. Tag the context. Delete the rest.

3. Maintain lightly (rhythm).

When reality changes, update or archive. Stale context misleads more than no context.

Most teams stop at step one and call it “knowledge”

The compounding starts at step two.

Curation isn’t overhead. It’s the multiplier.

At the personal level: your notes stop being transcripts and start being tools.

You keep the argument, not the meeting.

At the team level: decisions come with a short “Decision + Why + When It Breaks” block.

Future you can reuse the reasoning without replaying the thread.

At the organization level: every shipped feature, incident, experiment, and sales win gets compressed into patterns the whole company can use.

Support answers get cleaner. Product bets get bolder. New hires ramp using prior judgment, not tribal lore.

This is how knowledge starts to compound.

Not by remembering everything, but by remembering the right shape of things.

Someone has to own the quality of compression.

Call them the Context Manager.

They aren’t a librarian counting documents.

They’re an editor of intelligence.

• They enforce the “one-paragraph why.”

• They kill fluff and stale pages.

• They wire the system so AI pulls from compressed, trusted context — not raw noise.

In a world chasing more tokens, their job is to teach the machine what to forget.

Here’s what changes when you get this right:

• Speed: Fewer rediscoveries of old answers.

• Quality: Decisions carry forward the best prior judgment.

• Onboarding: New people inherit patterns, not puzzles.

• AI performance: Retrieval isn’t random; it’s from vetted, compressed context.

You start to feel it in weeks, not quarters.

The org thinks with a memory that doesn’t bog it down.

Everyone can buy the same models.

Almost no one will build the same context.

The advantage won’t be the size of your context window.

It will be the sharpness of what’s inside it.

Systematic compression of context is how artificial intelligence becomes institutional intelligence.

It’s how a company learns faster than it forgets.

We don’t need bigger models.

We need smarter forgetting.

We keep trying to make AI smarter by letting it remember more.

Bigger models. Bigger context windows. More tokens.

It feels right, the way carrying a bigger backpack feels right before a long trip.

But intelligence isn’t measured by how much you carry.

It’s measured by how much you can throw away and still know where you’re going.
That’s the part we’re missing.

There’s a rare condition called hyperthymesia.

People with it can recall nearly every day of their lives. Nothing fades. It sounds like a superpower. It isn’t.

They struggle to move on because nothing becomes background. Every regret stays sharp. Every detour stays loud. The present never gets a clear stage.

Perfect recall makes living harder, not easier.

We’re doing a version of this to our machines. We celebrate longer context windows as if wisdom were just a larger buffer. It only benefits the platform providers: bigger context windows means more tokens to charge for (more utilisation).

But a longer scroll isn’t more insight. It’s just more scroll (for humans and for machines).

When you solve a hard problem, you don’t replay your entire past. You reach for a pattern. A principle. A sharp example. Everything else you forget on purpose.

That’s not a flaw; it’s the mechanism.

Intelligence compresses. It keeps the essence and discards the rest.

Call it systematic compression of context

• Summarize the idea to its transferable core.

• Drop the specifics that don’t change the decision.

• Keep the map, not every footstep.

Humans do this instinctively. The best thinkers compress best. Machines will need to learn it deliberately.

The frontier isn’t bigger context. It’s better compression.

Now widen the lens.

Individuals drown in their GPT/Claude chat sessions, slack and endless meeting transcript emails. GenAI amazingly creates lots of stuff quickly because it can. Teams forget why a decision was made. Companies lose the reasoning that still shapes their way forward.
Most organizations mistake volume for progress. They dump old PDFs, HR policies, and board decks into an AI layer and call it transformation. In reality, they’ve just given enterprise search a new UI. “Search” is one small part of information discovery — not a device of intelligence.

This is not a storage or indexing problem. It’s a compression problem.

Artificial intelligence turns into institutional intelligence only when an organization can compress its experience into usable, durable context — and then make that context easy to retrieve at the moment of choice.

Without compression, knowledge is a junk drawer.

With compression, it’s a playbook.

Systematic compression of context is a habit with three moves:

1. Collect broadly (machines).

Pull the raw material from docs, chats, tickets, repos. Don’t judge yet.

2. Compress aggressively (humans).

Summarize the decision, the why, and the transferable pattern. One paragraph beats ten. Name the principle. Tag the context. Delete the rest.

3. Maintain lightly (rhythm).

When reality changes, update or archive. Stale context misleads more than no context.

Most teams stop at step one and call it “knowledge”

The compounding starts at step two.

Curation isn’t overhead. It’s the multiplier.

At the personal level: your notes stop being transcripts and start being tools.

You keep the argument, not the meeting.

At the team level: decisions come with a short “Decision + Why + When It Breaks” block.

Future you can reuse the reasoning without replaying the thread.

At the organization level: every shipped feature, incident, experiment, and sales win gets compressed into patterns the whole company can use.

Support answers get cleaner. Product bets get bolder. New hires ramp using prior judgment, not tribal lore.

This is how knowledge starts to compound.

Not by remembering everything, but by remembering the right shape of things.

Someone has to own the quality of compression.

Call them the Context Manager.

They aren’t a librarian counting documents.

They’re an editor of intelligence.

• They enforce the “one-paragraph why.”

• They kill fluff and stale pages.

• They wire the system so AI pulls from compressed, trusted context — not raw noise.

In a world chasing more tokens, their job is to teach the machine what to forget.

Here’s what changes when you get this right:

• Speed: Fewer rediscoveries of old answers.

• Quality: Decisions carry forward the best prior judgment.

• Onboarding: New people inherit patterns, not puzzles.

• AI performance: Retrieval isn’t random; it’s from vetted, compressed context.

You start to feel it in weeks, not quarters.

The org thinks with a memory that doesn’t bog it down.

Everyone can buy the same models.

Almost no one will build the same context.

The advantage won’t be the size of your context window.

It will be the sharpness of what’s inside it.

Systematic compression of context is how artificial intelligence becomes institutional intelligence.

It’s how a company learns faster than it forgets.

We don’t need bigger models.

We need smarter forgetting.