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The speaker does not want to leave the audience with a vague, ambitious notion like 'do evidence, it's awesome' — you need to specify what about evidence you take care of.
The speakers use AI coding examples in their harness discussions because they are close to them and are tools many people use daily — not because that is how Monzo approaches things, which has a different context.
During a one-hour drive, financially related planning kept failing in voice mode; the speaker had the same conversation three times because something would go wrong each time.
anecdotal
Anekha describes that she saw a shift while trading where data preceded something happening in the market, which prompted her to leave JP Morgan. She had always loved data and wanted something fast paced, which drew her to trading.
When the speaker was on the buy side, banks would set up meetings where a CFO would meet five funds at once in a round-table room.
When the AI wave happened, the speaker realized they needed to move very, very quickly to establish themselves in the field. They responded by doing the AI Engineering post, starting the conference, and doing media work, considering themselves a media person at heart.
The speaker confesses to having the most expensive Anthropic subscription and maxing it out, with Anthropic 'definitely on the red' because of his usage.
anecdotal
An audience member asked whether the wedding planning task had been tried with a multi-agent approach (e.g., a venue agent). An audience member notes that every time AI is involved, there's an expectation it will be 100% correct, and asks how to make that point clear to leadership. An audience member asks how trust is demonstrated in the UI — do you just ask it to show how it calculated something, or does it show you the code?
Felippe Felisola Caso describes that Monzo's data team builds internal AI-driven solutions for internal productivity, primarily for the data team, including an in-house remote agent capability called agent chip. He notes the presenters are not representing Monzo in the workshop example but using knowledge from Monzo to build a generic example, and that Monzo is growing very fast, sitting between a scale-up and a big enterprise company.
There is not a single silver bullet to solve every problem faced when building AI systems. The speakers are not claiming this is the definitive model; they are not going to the big stage saying 'forget everything you know, this is how to do it'.
Back in the day, there were no people validating your SQL queries — a comparison point for AI-generated analysis.
Anekha describes coming to the US, doing deep machine learning research, publishing at ICLR with Stability AI, and working in collaboration with NASA, which led her to Moshi. A fun fact is that most of the team has, in some capacity, worked with NASA.
Anekha describes thinking about how she could be a founder in the best country to be a founder in, identifying the US as that country. Fulbright gave one data scholarship in the whole of the UK, which she identified as her way to get to the US — and it worked. In her Fulbright interview she talked about the global energy market crisis and how data relates to it, which everyone could relate to because gas prices and electricity bills were going crazy.
Francesco Galletta describes the financial analyst job description as spanning from doing research for investments at investment firms and banks all the way to personal use cases.
The speaker's conference aims to be a high-signal conference for attendees, and they plan to make next year's conference the definitive event for the intersection of AI and finance, a market they knew from their hedge fund background.
Anekha Sokhal reflects that moving from screen-based work like trading to people-facing work has been more fulfilling, because she gets to meet real people instead of staring at a screen all day. She also notes most people she speaks to are overwhelmed by how fast-moving the current time is, which is why sharing experience helps.
Personal reflection with low confidence in the source atom.
Anekha explains that in commodities a lot is about the fundamentals, the different news, and the physical happening, and she was always fascinated by building out from zero. Because she has a data background, she always looked at the market wanting to break it down into all the different parts.
Anekha states this has been the best organized conference she's been to, citing good quality guidelines, a clear speaker team, breakout rooms, and the speaker lounge.
Anekha describes that she wanted to be an entrepreneur and founder, but specifically wanted to have a data company. She wanted to be a founder who can talk about the technology, thinking about this back in 2020-2021.
The host describes his own career path: trading currency options in London, then spending time in rates, starting with EMEA and going into G10 for currencies, then moving to the buy side. He left the hedge fund world and moved into tech, and was one of the very first few at Ballyasny to do the ex-Firehose or Twitter Firehose, a data feed costing around $300,000 a month.
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