Emilia Javorsky
speaker
1,653 appearances
2 recordings
1 series
first heard Mar 2026
last heard 20 Mar
Emilia Javorsky’s voice in public audio — every appearance, attributed to the second.
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recordings per month · last 12 monthsRecordings per month over the last 12 months — 2 in all, peaking in Mar 2026 with 2.
Appearances
Like those companies and work should be celebrated at the same level as we are, our sort of big tech friends developing their LLMs and resourced, right?
And I think the resourcing is a really important piece here.
And this is where the ASI promise
does actually have costs beyond the narrative costs and beyond the staking our hopes on the future, which is it's consuming an unprecedented amount of capital, right?
It is sucking up all the capital in the room because it is very expensive to train these models in an unprecedented way.
And in a world where there is a finite amount of private dollars available for investment and a finite amount of dollars in VC funds, fundamentally that's money that is being diverted away from these AI tool companies, biotech research, cancer companies, and into ASI.
And we know that there's not, you know, it's a reasonable assumption
But you can look at the correlation of the amount of money going into AI and the fact that biotech is at a 10-year low in its investment cycles.
And companies are really competing to actually get capital to resource some of these breakthrough ideas, tools, and technologies.
So how do we get them more money to do the good work that they're doing on the AI side?
Also, how do we get more money, attention, resources,
to all of these promising areas in oncology, right?
Like the bread and butter of biomedical research, like there are areas we know have a lot of promise and just need more resources, more engagement to be able to push them across the finish line.
So that's kind of the second piece is like,
How do we celebrate resource promising areas within oncology?
And then the last piece is we've been talking about is how do we restructure incentives?
How do we restructure institutions to make all of this current efforts and future efforts go better and faster and reward the right things?
So there's the data piece of this, which we've talked about, data and measurement.
How do we...
get serious about new models to enable the creation of large scale data sets.
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