Start an AI Company AI Model Config Tiers
Tune size, data, and personality so every launch feeds users, contracts, and sell value.
Every run of Start an AI Company begins on a laptop: you pick size, data, and personality, train, then launch. That trio is the product. GPUs and staff only scale what you shipped. This tier list ranks configuration habits — not fake model brand names — so you stop launching random mush.
Deep walkthrough of the UI lives in Train and Launch. Come back here when you need priority calls mid-train.
The three knobs
| Knob | What it tends to gate | Failure mode |
|---|---|---|
| Size | Capability ceiling, train time, compute cost | Too big too early → unfinished training & empty launch |
| Data | Accuracy, trust, contract readiness | Too thin → virality spike then collapse |
| Personality | Brand, niche fit, crisis vibes | Meme personality with enterprise contracts = whiplash |
S-tier setups
Balanced starter (medium ambitions). Moderate size, solid data investment, personality that matches a clear niche (helpful assistant, coding buddy, creative partner). This is the default S-tier for bedroom and co-working stages.
Data-forward specialist. When you are aiming at contracts (Contracts & Crises), bias budget into data quality even if size grows slower. Contract boards punish unreliable models.
Prestige-boosted flagship. After a strong sell (Sell and Restart), you can afford larger size earlier. Flagship configs become S only when restart bonuses cover the train time.
A-tier setups
Small + excellent data. Great for proving a niche quickly and feeding marketers something real (Staff Tier List).
Personality-led consumer apps. Strong when you already have GPU fills (GPUs) to survive a viral wave of messy prompts.
Iterative refreshes. Relaunching an improved version of a known model often beats inventing a sixth unrelated brand.
B-tier setups
Max size vanity trains. Fun screenshots, slow cycles. Park them after campus-scale compute exists (Expand Offices).
Copycat clones of rival labs. Learning from rivals is smart; cloning without data depth is B at best.
C-tier setups
Huge size + poor data. The classic trap. Looks impressive in the trainer, disappoints in user metrics.
Contradictory personality vs marketing. If marketers sell “enterprise safety” while personality is chaotic prank bot, expect crises.
D-tier setups
Spam launching unfinished trains. Wastes cooldown focus and confuses your own metrics.
Ignoring personality entirely. A blank vibe still is a vibe — usually a bad one for likes and shares.
Launch checklist (apply tiers)
- Pick a niche sentence you can say out loud.
- Allocate data until the trainer stops screaming poverty.
- Choose the smallest size that can hold that niche.
- Align personality to the niche — not to a meme you saw in another lobby.
- Launch when GPUs can absorb early users.
- Hire marketers only after retention looks sane.
- Log results before changing all three knobs at once.
Use the Growth Checklist to keep this honest during long sessions.
How models interact with the rest of the loop
- Users & virality love clear personalities with enough data not to implode.
- Contracts love boring reliability — bias data.
- Crises / rival labs punish brittle flagships; keep a stable breadwinner model online.
- AI chip arcs often assume you already understand training tradeoffs; do not rush chip content on D-tier mush.
- Offline earnings continue on whatever you left running — leave your S-tier producer active before logout.
Learning from creators
Community videos (linked from Getting Started and Train and Launch) show real HUD pacing. Watch how long they linger on data versus size. Do not copy their endgame size on your first bedroom run.
Meta shifts
If a patch retunes training, we will update this page and call it out in Launch Notes. Until then, data-respecting balanced launches remain the S-tier habit for Start an AI Company by Kairo Experiences.
Related pages
Frequently Asked Questions
Quick answers about codes, GPUs, staff, and selling your company.
What matters more: size, data, or personality?
Early on, data plus a clear personality usually beat raw size. Size becomes the limiter once GPUs and prestige bonuses can fund longer trains.
Should I launch many small models or one big model?
Prove one balanced model first. Spam launching unfinished trains is D-tier and muddies your metrics.
How do model choices affect contracts?
Contracts favor reliable, data-forward setups. Save chaotic personality experiments for consumer virality runs.
Can I change a model after launch?
Plan on iterative refreshes. Treat each launch as a version; do not expect one perfect train forever.
Do codes change model tiers?
Codes like UPDATE mainly add resources. They do not make huge empty models good — spend the gift on data and early GPUs.