Derek Nee@DerekNee
GPT-5.6 Sol is already a very capable model.
but its highest-leverage role may be as the CEO of models built by its competitors.
put Sol in the CEO Office.
give Fable 5 a permanent Strategy + Review department.
let Grok 4.5, Luna, Codex, Claude Code, and browser workers execute inside the departments that own the work.
Sol alone is a model.
Sol + Fable + low-cost workers + Proof + Memory is a company.
done right, the company can feel 10x smarter than Sol alone - and cost less per verified deliverable.
the 10x is not hiding in another benchmark chart.
it is in the architecture.
the useful metric is not intelligence per API call.
it is verified work per human operator.
the 10x does not come from spending 10x more tokens.
it comes from division of cognitive labor:
> Sol chooses direction and allocates ownership
> Fable attacks the plan's blind spots
> durable departments preserve domain context
> worker fleets execute scoped work in parallel
> independent models catch correlated mistakes
> Proof rejects output that only looks finished
> Memory makes the next loop start ahead
one model no longer has to plan, execute, criticize, remember, verify, and scale at the same time.
that is where the intelligence multiplier comes from.
and the cost advantage is just as important.
at current API list prices per million input / output tokens:
> GPT-5.6 Sol: $5 / $30
> Claude Fable 5: $10 / $50
> Grok 4.5: $2 / $6
> GPT-5.6 Luna: $1 / $6
do not pay a $30/M-output CEO to do extraction.
do not pay a $50/M-output strategist to sit in every hot path.
and do not ask a $6/M-output worker to make the few decisions where one mistake changes the company.
the right split is:
frontier intelligence where judgment changes the outcome.
low-cost intelligence where the work is clear and parallelizable.
cross-vendor review where correlated mistakes are expensive.
proof everywhere.
this is the company architecture underneath it:
Workspace
-> CEO Office / GPT-5.6 Sol
-> durable department hierarchy
-> Strategy + Review / Fable 5
-> Product / Engineering / Growth / Research departments
-> same-owner worker seats / Grok, Luna, Codex, Claude Code, browser
-> Criteria / Proof / Check-in
-> department outcome reply
-> CEO synthesis
-> state + memory update
-> next wake
The CEO Office is the primary department and the user's entry point.
Sol does not become a god-agent with every file, tool, permission, and task.
It becomes the executive layer.
It resolves ownership, routes work, creates an owner when none exists, arbitrates conflicts, follows up, and synthesizes the company-level result.
Fable 5 is not a stateless side call.
It becomes a durable Strategy + Review department with its own memory, skills, Key Results, task history, and accumulated taste.
Product, Engineering, Growth, Research, and child departments can each choose the model that best fits their work.
inside those departments, workers are temporary execution seats:
> Grok 4.5 for efficient scoped execution
> Luna for high-volume fan-out
> Codex for repo-native GPT coding work
> Claude Code for Claude-native coding and review
> browser / computer workers for workflows that need visible proof
the key distinction:
a department message moves ownership.
a worker parallelizes the current owner.
if CEO Office creates Engineering and then secretly performs Engineering's work with CEO workers, that is not delegation.
it is org-chart theater.
Engineering should own the Key Result, decompose it into Tasks, dispatch its own workers, judge the returned artifacts, attach Proof, and send the outcome reply.
workers return artifacts and traces.
departments return outcomes.
CEO Office returns one coherent company answer.
that is the loop:
company direction
-> Workspace Objective
-> department ownership
-> Key Result + proof-bearing Tasks
-> multi-model worker execution
-> Criteria + Proof + Check-in
-> outcome reply
-> CEO synthesis
-> memory update
-> next wake
the best model should not do all the work.
it should make sure the right work is owned by the right department, executed by the right workers, and rejected until the proof passes.
a one-model app gets smarter when its provider ships.
an agent company gets smarter whenever any provider ships.
a better OpenAI model can upgrade the CEO seat.
a better Anthropic model can upgrade strategy and review.
a better xAI or open model can upgrade the worker fleet and lower the blended cost.
the model mix changes.
the company compounds.
that is loop engineering for an agent company.
