Aber Whitcomb

The slowdown hides in the handoffs.
Teams have strong models and plenty of data; momentum slips when coordination lags.

What helps:
- A unified data plane so public and private signals work in the same model paradigm
- An orchestration layer for ensembles, versions, and swaps
- Standard interfaces with lineage and governance across the pipeline

When the system runs like a conductor, context holds and throughput rises.

Where do your handoffs fail?

#LifeSciences #AIInfrastructure #DataPlane #SystemDesign

At Myspace and Jam City, progress followed sound orchestration: Data consistent, interfaces standard, feedback loops tight.

Health follows the same pattern.
When discovery, trials, and care follow the same structure, work moves faster and the results are clearer.

AI adds new levers—a unified data plane to concentrate signals, a focused ensemble tuned to the decision, and orchestration that keeps decisions moving through the pipeline with context intact.

That’s why I'm focused in life sciences. Each gain adds up, creating real impact for real people.

#LifeSciences #HealthTech #AIInfrastructure #SystemDesign #AIinBiotech

Most companies don’t lack AI tools.
They lack the structure to use them well.

New models launch every week. But integrating them into real systems—across data sources, teams, and compliance environments—is still the hard part.

Salt provides that on-ramp.

We’ve built a platform layer that helps teams turn fragmented AI tools into composable, production-ready systems. It sits inside your environment, behind your firewall, and adapts to your use case.

AI evolves fast. Salt makes it operational

#SaltAI #AIInfrastructure #SystemDesign #EnterpriseAI #OrchestrationLayer #AgenticWorkflows

I’ve always leaned toward high-leverage decisions.
Ones that come with risk, but also meaningful outcomes.

That mindset shapes how I build teams too. Across the companies I’ve helped start, the constant has been structure:
– Independent leaders
– Shared vision
– Clear roles

Many of us have worked together for years. That trust lets us move fast without micromanaging. That alignment is embedded.

You see the value of that approach in technical domains too.

Take AI in life sciences. The models are often managed by ML engineers—but the people who actually understand the biology rarely touch the tools.

I’ve seen what happens when that changes.

In one case, researchers working on protein design—experts in spike binding, immunogenicity, toxicity—could finally interact with the models directly. No coding required. They could adjust variables, test changes, and explore outcomes on their own.

That shift turned a months-long back-and-forth into a responsive, real-time loop.. Discovery moved faster because the people with context were finally in control.

That’s the kind of system I aim to build. One where the right people lead, and the infrastructure doesn’t slow them down.

#AIInfrastructure #Leadership #SystemDesign #LifeSciences #AgenticWorkflows #TeamDesign

At scale, the barrier isn’t intelligence.
It’s integration.

The models are good enough.
The handoff breaks.

#AIInfrastructure #LifeSciences #SaltAI