Jake Harding
speaker
500 appearances
6 recordings
1 series
first heard May 2026
last heard 11 Aug
Jake Harding’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 — 6 in all, peaking in Jun 2026 with 3.
Appearances
And so there's a second dimension which exponentially increases the amount of input that you need to process to come up with kind of reliable output.
And then I think a third point that you touched on is asking a human to do that.
We are prone to recency bias, as you say, like one year's worth of slightly different data is going to color how we view the next 10 years.
But in reality, it needs to be a lot more of that, a lot more than one year to impact a model substantially.
It needs to be a decade out of 100 years, probably at least.
So yeah, totally makes sense.
And
On that, I think we know that risk intelligence requires complex multi-layered data, as we've just covered.
The context and understanding comes from the interplay of a wide variety of data types from various sources and inputs.
Operationally, a human underwriter can't manually process that volume and that volume of information without losing context.
So could you talk a little bit more on how the emergence of LLMs and agentic AI tools create a perfect synergy with GeoScope's complex data sets to deliver instant but also actionable intelligence?
Yeah, and it's a good point.
We talk about AI and LLMs and how we can use them almost in a vacuum, but there's two pieces to that puzzle.
There's the AI, the LLM and the output, but then there's the human interaction with it.
And as you say, I mean, to really kind of drive progress in that region, there's a reframing of approaches needed.
But equally for humans to interact with that in a way where they trust the output, it needs to be explainable.
They need to understand the rationale and the reason and the inputs that have led to a certain conclusion, which is, I mean, I totally agree that highlights why black box type approaches work.
where they're not going to cut it in the coming years because we need to understand what's going on under the hood to truly engage with it as humans and i guess on these new ai capabilities many large carriers have technical teams that want to build their own ai agents or data integrations entirely in-house and from scratch which is understandable these teams they're passionate about this and it's what they enjoy doing but from your perspective
What are the inherent risks and limitations of that pure build approach?
And why should insurers instead be looking to specialist partners for their risk intelligence strategies?
Showing 241–260 of 500 · page 13 of 25
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