During the Super Bowl, ai.com got its major prime-time spotlight: a national TV ad driving huge traffic in seconds. Estimates in tech media varied on exact spend, but all reporting pointed to one thing: enormous money was spent on attention.
Result?
Traffic came in, users clicked ai.com, and the platform began failing under onboarding pressure, with reports of inaccessible pages and broken sign-up loops.
CEO Kris Marszalek stated on X that they had prepared for scale, but not for this level of demand, citing Google auth limits as a key bottleneck.
In short:
They invested massively in domain + brand + media, but core infrastructure failed at the exact peak attention moment.
Where the stack actually broke
Based on public statements, one central failure point was onboarding auth:
New-user flow was heavily centered around Google sign-in.
When millions of users attempted entry in a short time window, rate limits became a practical blocker.
Two major issues emerge:
1. Single Point of Failure (SPOF)
Onboarding depended on one auth provider path. If that path is rate-limited or unavailable, growth stops.
2. Scaling modeled for gradual growth, not traffic shock
They likely prepared for high growth curves, not instant Super Bowl spike patterns with extreme concurrency.
From the outside, likely gaps included:
- no realistic Super Bowl-grade stress scenario simulation
- insufficient safety margin in capacity planning
- no robust fallback when external auth became constrained
Why this is a lesson for every founder and marketer
Every team wants breakout launch moments: premium domain, high-budget media, massive awareness.
The ai.com case highlights a basic truth:
Attention is expensive. Unstable infrastructure destroys paid attention value immediately.
If you are founder, CTO, or growth lead driving major campaign spend, the order should be:
- Ensure your systems can absorb success.
- Then pay to bring success to your front door.
If you reverse it, you pay twice:
- first in media spend
- second in reputation damage
Technical and business lessons
1. Never launch major campaigns without serious load testing
Before TV campaigns, homepage takeovers, or high-reach influencer activations, know exactly:
- throughput and latency thresholds
- failure modes when traffic exceeds thresholds
- whether degradation is graceful or catastrophic
Practical tools:
- k6
- Locust
- JMeter
The goal is not to "run one test." The goal is realistic traffic pattern simulation:
- aggressive bursts
- rapid ramp-up
- third-party dependency stress
2. Avoid SPOFs, especially in login
A single "Continue with Google" path is convenient UX but risky architecture under surge events.
More resilient auth setup:
- multiple login paths (email/password or magic link + Google/Apple/etc.)
- fallback logic when one provider returns rate limits
- caching/retry strategies to reduce external dependency pressure
3. Autoscaling must be real, not checkbox-level
"We are on cloud" does not automatically mean resilient at Super Bowl scale.
Autoscaling handles gradual demand better than instant traffic shock unless pre-warmed.
Minimum checklist:
- tune autoscaling policies for fast response on CPU/RPS/latency
- pre-scale before campaign launch
- validate account quotas and concurrency limits for all dependencies
4. Do not blindly depend on external limits
Most modern products rely on cloud + auth + AI APIs + payments + CDN.
For each critical dependency ask:
- What if it rate-limits us?
- Can we queue and replay requests safely?
- Can we degrade function instead of going hard-down?
5. Degrade gracefully, do not implode
If systems strain, users should not only see generic 500 errors.
Graceful fallback examples:
- waitlist capture when onboarding is overloaded
- lite product mode when backend AI is saturated
- temporary cache-first pages while databases recover
Even in incident windows, transparent UX preserves trust.
Practical pre-launch checklist
1. Load and stress testing
- tested at 2-3x optimistic traffic estimate
- identified actual breakpoints (latency, error rates, dependency failures)
- resolved obvious bottlenecks (slow queries, missing caching)
2. Capacity and autoscaling
- raised baseline capacity before launch
- set autoscaling limits high enough for peak assumptions
- validated external provider quotas/rate limits
3. Architecture without critical SPOFs
- login does not depend on one provider
- fallback paths exist for auth, payments, messaging
- non-essential features behind flags during spike windows
4. Observability and alerting
- dashboards for latency, errors, RPS, CPU, DB health
- alerting triggers before total failure
- clear incident ownership and on-call flow
5. Emergency UX and comms
- prepared heavy-load/waitlist pages
- friendly incident messages ready
- communication plan for social/email channels
Conclusion: do not repeat ai.com
The ai.com case is a textbook "what not to do" in launch architecture:
- premium domain and media investment
- massive audience capture
- critical failure at first high-intent interaction
For founders and marketers, the takeaway is direct:
Scalable marketing without scalable infrastructure is not growth. It is an expensive way to destroy first impression.
If your team is preparing a major launch, use this as a rehearsal scenario: stress test hard, remove SPOFs, design fallback paths, and never rely on optimistic assumptions.
