The Peculiar State of AI
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The AI industry is in a peculiar state. Benchmarks are being broken weekly. Investment is at historic highs. And yet the nagging question persists: what problem, exactly, are we solving?
Somewhere along the way, the metrics became the point. Model performance scores, token throughput, parameter counts - these stopped being instruments of progress and started being the goal. The result is an industry that is extraordinarily good at measuring itself and increasingly vague about why.
This talk argues that AI is, at its core, a data problem; and not in a technical sense. The real problem is that we've imported data's oldest failure mode into AI: optimising for what's easy to count rather than what actually matters. The perfect storm of business pressure, technological novelty, and data complexity has made it harder, not easier, to stay anchored to the original question.
The invitation is simple: go back to the problem.
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Presenter bio:
Mike Le Galloudec is a Principal Engineer Advocate at Oakland, dividing his time between engineering agentic AI solutions and translating them for non-technologists. He recently steered Network Rail’s first production grade AI application, leveraging the Microsoft Azure stack and custom agent frameworks to turn raw data into real time decisions. A former data scientist with a Physics degree from the University of Oxford – and a neural network tattoo to keep him honest, he still enjoys fine tuning predictive models at ridiculous hours.
Beyond delivery, MLG hosts Oakland’s YouTube channel and a fast-growing tech TikTok, distilling dense data topics into 90 second aha moments for thousands of viewers. Whether advising rail, transport or utilities giants, he balances builder pragmatism with tech clarity, championing agentic AI that makes enterprise datasets think for themselves.
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