Spot on Alborz.
Alborz Geramifard
AI isn't eliminating engineering. It's changing what great engineers do.
A conversation with Ashwin Machanavajjhala and Erran Berger inspired me to think… more
AI isn't eliminating engineering. It's changing what great engineers do.
A conversation with Ashwin Machanavajjhala and Erran Berger inspired me to think about this through the lens of one of computing's biggest transitions: from assembly to Python. I believe we're living through an equally important shift today. Here's my perspective.like 23insightful 3celebrate 1support 1
As AI moves from experimentation to production, discipline is going to matter just as much as ambition.
At LinkedIn we saw a while ago that the cost trends were clear, and that the future of our products would be powered by increasingly compute-expensive AI… more
As AI moves from experimentation to production, discipline is going to matter just as much as ambition.
At LinkedIn we saw a while ago that the cost trends were clear, and that the future of our products would be powered by increasingly compute-expensive AI workloads that are more intelligent, more capable, and more useful. But those workloads have to be ROI positive, and that’s not something you can just wake up and do overnight. So we set ourselves a challenge that sounds counterintuitive in today’s environment: how do we deliver increasingly sophisticated AI experiences while keeping our compute footprint as close to flat as possible?
This is where owning our own data centers and infrastructure as an applied ML company really pays off, because we can put in the instrumentation and make efficiency a long term investment up and down the stack rather than a one-time push. None of it came from a single breakthrough - it’s the outcome of hundreds of improvements, from optimizing GPU utilization and workload allocation to distilling larger models into smaller and more efficient ones to rethinking how work is distributed across training, inference, storage, and systems design. The results of that intentional push are starting to play out in terms of better experiences for our members and customers while giving our engineers the flexibility to keep innovating.
Raghu and I recently sat down with Paresh Dave at WIRED to talk through all of this, check it out!
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Craft still matters in this AI era. At LinkedIn, it’s our agility and craft that’s positioned us to drive innovation on our platform, tame cost and resource challenges, and deliver at unprecedented scale for our members and customers. Our Engineering teams… more
Craft still matters in this AI era. At LinkedIn, it’s our agility and craft that’s positioned us to drive innovation on our platform, tame cost and resource challenges, and deliver at unprecedented scale for our members and customers. Our Engineering teams have leaned into our full-stack technology approach, making improvements at every layer that have set us on a positive path of compute efficiency, faster product iteration, and the integration of even more complex AI and agent capabilities.
Erran Berger and I sat down with Paresh Dave at Wired for a great conversation about navigating the compute and efficiency demands in this AI era, including what we’ve been able to accomplish with engineering innovation to maintain our compute needs but accelerate new and improved product experiences across LinkedIn.
The more disciplined and thoughtful we are with our craft, the more we can do for everyone who relies on LinkedIn to discover and grow their economic opportunities.
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