Michael Louis

Building @cerebriumai - making it easy to deploy real-time AI at scale. Prev exit to Walmart. Kind of a big deal

[cerebriumai @cerebriumai]

Today we’re sharing how we built Thalamus: Cerebrium’s highly available distributed router for global AI workloads.

It helps power low-latency, resilient AI applications across fragmented compute for companies like
@tavus, @superwhisper, @useCamb_AI

Read more: https://t.co/QKX7iwNzTI

[Michael Louis @MichaelLouis_za]

@shensi @JeanelleAgent Congrats team - Awesome work here

[Michael Louis @MichaelLouis_za]

We are six weeks into the year and I have heard “paradigm shift” every week - we get it

[Michael Louis @MichaelLouis_za]

This is awesome to see! I always wondered about the appetite for Governments in Africa for an @anduriltech type company.

[TechCrunch @TechCrunch]

These Gen Zers just raised $11.75M to put Africa’s defense back in the hands of Africans https://t.co/i9BdGTwuOw

[Michael Louis @MichaelLouis_za]

If voice AI is core to your product, treating the LLM as a permanent black box is leaving UX and margin on the table.

Happy to chat if you’re thinking about making this shift.

[Michael Louis @MichaelLouis_za]

Most voice AI teams delay this decision far longer than they should - and eventually hit the same wall.

The LLM ends up being the single biggest driver of both latency and cost.

[Michael Louis @MichaelLouis_za]

This typically only starts to make sense at ~15k+ calls per day.

But that’s also when customers start noticing.
Lower latency shows up immediately in:
• Conversation flow
• Turn-taking
• Perceived intelligence

[Michael Louis @MichaelLouis_za]

The bigger signal....

The winners won’t just be the models with the best benchmarks - but the platforms that deliver:

• Low latency
• Reliability
• Cost efficiency
• Great developer experience
• Production-ready infra

Infrastructure is the moat.
And that’s exactly what we’re focused on solving at @cerebriumai .

[Michael Louis @MichaelLouis_za]

One of the more interesting AI reports I’ve read recently is @openrouter's State of AI - because it’s based on real production usage at scale, not benchmarks or demos.

A few takeaways 🧵👇

You can read the full report here: https://t.co/TSw9FQPr2f

[Michael Louis @MichaelLouis_za]

5. Open-source models now represent ~30% of total usage.

Driven by:
• Cost
• Flexibility
• Faster iteration cycles

I’d be surprised if this number doesn’t grow meaningfully in 2026.

6. Early model fit creates lock-in.

When users find a model that fits their workflow early, they stick with it.

[Michael Louis @MichaelLouis_za]

Given the pace of shipping, infra build-out, and model progress in just 18 months, I’m still bullish.

Execution at this speed and scale is rare.

[Michael Louis @MichaelLouis_za]

@xai just raised a $20B Series E at a $230B valuation - and I’m still bullish.

Back in April 2024, when xAI raised ~$6B at an $18B pre, many questioned the lack of traction and clarity. 18 months later, execution has answered most of that.

[Michael Louis @MichaelLouis_za]

Infra matters. xAI built Colossus, one of the world’s largest AI supercomputers, in record time.
Outside of Google, very few labs own their full stack - OpenAI and Anthropic still rely heavily on cloud partners.

Distribution + data are underrated advantages.
xAI already reaches ~600M active users via X, competitive with ChatGPT at ~900M, with a still-nascent GTM - plus proprietary real-time data no one else can access.