• The constraint has shifted from compute → coherence.
• Phase 4 introduces four architectural challenges: attention, integration, consistency, and privacy.
• Coherence challenges are harder than compute challenges because they require new mental models, not more GPUs.
• The next wave of value will go to teams who solve these coherence bottlenecks invisibly and reliably.

Why did the constraint shift from compute to coherence?

Phases 1–3 operated under a simple rule:
More GPUs = more capability.

Scaling parameters, data, and compute delivered predictable gains.
The problems were solved with capital: expand clusters, add hardware, scale up.

Phase 4 breaks this pattern.
The limiting factor is no longer computational brute force — it is the system’s ability to:

• maintain attention across massive contexts
• integrate new memory with old memory
• preserve consistency across time
• enforce privacy rules in a persistent system

These are cognitive bottlenecks, not infrastructural ones.

Coherence becomes the new constraint.

What is the first coherence challenge: Attention Problems?

As context windows exceed 200K+ tokens, the agent must decide:

• What deserves attention?
• What should be deprioritized?
• How to maintain focus when signal-to-noise drops?

Distributed attention leads to diluted reasoning.
The model can lose track of relevance when too much context is present.

The architectural question:
How do you maintain sharp focus across massive working memory?

Attention management becomes a non-linear engineering challenge — not something solvable by brute compute.

What is the second coherence challenge: Integration Problems?

Memory is only useful if it blends seamlessly with current context.

Key difficulties:

• avoiding context pollution
• preventing stale data from overriding fresh information
• merging long-term memory with real-time reasoning
• arbitrating priority between new and historical data

The agent must integrate memory with precision —
not too much, not too little.

This requires new mechanisms for memory interpolation, gating, and structured retrieval.

Integration is one of the hardest coherence challenges because it introduces temporal complexity:
the present must always negotiate with the past.

What is the third coherence challenge: Consistency Paradoxes?

Persistent intelligence must reconcile contradictions across time:

• user preferences evolve
• facts change
• earlier statements may conflict with current knowledge
• trust erodes if the system behaves inconsistently

The agent must determine:

• Which version of the truth should be authoritative?
• How should the agent update itself when reality changes?
• How do preferences override old patterns without breaking continuity?

This is not a prompting problem — it’s an identity-level constraint.
Consistency is the core of trust.

What is the fourth coherence challenge: Privacy Architecture?

Memory introduces new responsibilities:

• What should the agent remember?
• For how long?
• Under what governance rules?
• With what user controls (keep, forget, expire, delete)?
• How do retention policies coexist with long-term reasoning?

Regulatory and ethical constraints must now be baked into memory architecture.

Privacy architecture is no longer an external policy —
it becomes a first-class system component.

This challenge is amplified by the fact that persistent memory directly affects identity, trust, and compliance.

Why is coherence harder than compute?

Compute problems are solved with money:

• buy more GPUs
• scale horizontally
• build bigger data centers
• apply well-understood engineering

Compute scaling is linear, predictable, and based on established playbooks.

Coherence problems require something entirely different:

Architectural Innovation

There are no blueprints. The patterns for memory + context systems are just emerging.

Non-linear Complexity

Memory integration and identity continuity behave unpredictably.

Emergent Failure Modes

Coherence failures can be subtle but catastrophic — drifting identity, corrupted memory, inconsistent reasoning.

Trust Fragility

Once trust is lost, no amount of compute can restore it.

User Expectations

People expect human-like consistency as soon as memory appears.

This is why coherence cannot be brute-forced.
It requires conceptual breakthroughs, not capital expenditure.

What does this shift imply for the future of AI?

The next major gains will come from solving the four coherence challenges:

1. Attention across massive context
2. Integration of persistent and active memory
3. Consistency across evolving states
4. Privacy baked into the memory architecture

Teams who solve these challenges become the architects of Phase 4 intelligence.

In the compute era, money bought progress.
In the coherence era, architecture determines who wins.

Final Synthesis

Phase 4 transforms the bottleneck from compute to coherence. Attention, integration, consistency, and privacy challenges define the new frontier — problems that cannot be solved by hardware escalation. They require new conceptual frameworks, memory-aware architectures, and trust-centric design. The next wave of value will accrue to those who solve coherence invisibly, reliably, and at scale.

Source: https://businessengineer.ai/p/the-four-ai-scaling-phases