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You can't hand the decision loop to agents — finance needs a human to own the decision

In finance you can't just hand the decision-making loop to a team of agents because the other side of the trade is a client — if it's a commodities trade, it's a physical cargo; if it's a company, it's a team of people. When something goes wrong you need a human to stand up and say 'I made the decision for this reason.' If an agent leaks your API key, all it says is 'oops, I'm sorry, I shouldn't have done that, please rotate it' — unacceptable in finance, because an agent is just self-correcting and one small error exponentially gets bigger.

endorsed as the speaker's argument

AI Engineer · The Investor's Edge Lives in Their Head — Anekha Sokhal, Moshi

Edge is what you can't download — it sits in an investor's head

Every investor is looking for the same thing: edge. In practice edge consists of three things — knowing what's going on before anyone else, knowing your blind spots (the second and third order derivatives of what's happening), and having conviction so you can size up on a trade you believe in. If everyone building in the investment space has access to the same models and systems, the question becomes what actually constitutes edge — and the answer is what you can't download, what sits in an investor's head.

endorsed framing by the speaker

AI Engineer · The Investor's Edge Lives in Their Head — Anekha Sokhal, Moshi

Investors want reasoning, not answers: measure claims, show traceability, expose assumptions

When an investor asks for your opinion, they don't just want to know what you think — they want to know how you came to that reasoning. The approach described: measure hallucination, measure every claim, check how it's traceable, and give the user the assumptions used and how they led to the decision. Moshi leaves the decision-making with the human because that is what humans do best — there's no machine more powerful than the brain.

endorsed; the measurement claims are the builder's own description

AI Engineer · The Investor's Edge Lives in Their Head — Anekha Sokhal, Moshi

Point AI at the right spot in the decision-making cycle — feed the brain instead of replacing it

Anekha Sokhal argues that when building AI technology it matters at which point in the decision-making cycle you point it: if the most efficient reasoning engine sits between your two ears running on 20 watts, you should question where machine learning actually sits in that picture — instead of replacing the brain, why not just feed it.

endorsed as the speaker's own view

AI Engineer · The Investor's Edge Lives in Their Head — Anekha Sokhal, Moshi

Split the grind from the judgment: machines for data, humans for decisions

Work splits into two parts: the grind (reading, gathering, data cleaning) and judgment (the decision). Machines are incredible at the grind while humans are incredible at decision-making, so the two should be split into separate parts. With so much information now, the biggest question people have is what is the truth — what are the different sources saying, what is the divergence of information, and how can I trust what is out there.

endorsed as the speaker's proposal

AI Engineer · The Investor's Edge Lives in Their Head — Anekha Sokhal, Moshi

Prefer tools that push new data to you over pulling data from an LLM

Use tools that push new data to you rather than pulling data from an LLM, so your brain is forced to think about new things and make new connections.

AI Engineer · The Investor's Edge Lives in Their Head — Anekha Sokhal, Moshi

Debating with an LLM is a waste of time — investors want tools that amplify reasoning

Many traders say they don't want to speak to anyone all day — they just want to trade. Using LLMs just to talk to and debate — like asking whether FOMC is today or tomorrow and having the LLM say it's tomorrow when it's today — is a complete waste of time and the opposite of what investors want. Investors want something that gives them the power to reason more and use their brain.

endorsed as the speaker's judgment

AI Engineer · The Investor's Edge Lives in Their Head — Anekha Sokhal, Moshi

AI promised to replace the expensive quant stack — but productionizing is the hard part

Before AI, working in finance meant a team of quants, data scientists, data engineers, and traders all working together to build a decision-making engine — a whole stack of very expensive and very qualified people. AI tools made people believe they don't need a full team, that one person with a few API calls can replace that stack. But once you've spent your weekend building a dashboard, the moment you want to productionize it and share it with one more person you have to think about security, a database, storage, reliability, and most importantly credits — how to actually run the systems. Pricing has changed too: people are no longer pricing by tools but for every API call.

described; the replacement claim is attributed to AI-tool marketing, not endorsed

AI Engineer · The Investor's Edge Lives in Their Head — Anekha Sokhal, Moshi

Build 24/7, reliable, traceable infrastructure — not a weekend tool

Build or choose tools that run 24/7 on every market with big data, understandable in context, reliable and trustworthy — that becomes infrastructure, unlike a weekend tool that others cannot trust or trace back.

AI Engineer · The Investor's Edge Lives in Their Head — Anekha Sokhal, Moshi

New context is worthless without trust — confident-but-wrong answers are worth nothing

New context is worthless if you don't actually know whether it's trustworthy, where it comes from, or whether you value the analyst's opinion. In finance, even a really confident answer is worth nothing if it's wrong.

endorsed as the speaker's principle

AI Engineer · The Investor's Edge Lives in Their Head — Anekha Sokhal, Moshi

In finance AI, the edge is what you build on top of identical models

When everyone has access to the same models and systems, builders in finance AI must answer what their actual edge is — a skills file, MCP connectors, a better prompt, or a better model.

AI Engineer · The Investor's Edge Lives in Their Head — Anekha Sokhal, Moshi

Design tools around what the trader actually does in each tool

When you have two tool types solving different problems — one for context and one for data volume — think about what the trader is actually using and thinking about in each tool.

AI Engineer · The Investor's Edge Lives in Their Head — Anekha Sokhal, Moshi

Debating basic facts with a wrong LLM wastes decision-making time

Debating basic factual questions with an LLM that gives wrong answers — like insisting an event is tomorrow when it's today — wastes time that should be spent on decision-making, the opposite of what investors want.

AI Engineer · The Investor's Edge Lives in Their Head — Anekha Sokhal, Moshi

AI floods finance with data while human attention stays flat

AI systems give everyone the same flood of information while human attention is spread flat across everything, so for financial professionals paid on synthesis the gap between attention and data keeps widening.

described with moderate confidence

AI Engineer · The Investor's Edge Lives in Their Head — Anekha Sokhal, Moshi

Desk apprenticeship and consistency — and the unpriced cost of one-person pods

In finance, when you join a bank or fund you sit next to a trader or team and absorb everything going on — you learn from them, listen to everything, with one ear on the desk and one ear around everything else. Consistency is what pays off in that learning. If everyone runs their own teams as one-person pods, the future generation of context coming in is lost — and that's the real cost people are not pricing in.

described; the cost claim is the speaker's own assessment

AI Engineer · The Investor's Edge Lives in Their Head — Anekha Sokhal, Moshi

Choose tools that amplify your reasoning over tools that answer for you

Choose tools that give you the power to reason more and use your brain rather than tools that just answer questions for you.

AI Engineer · The Investor's Edge Lives in Their Head — Anekha Sokhal, Moshi

Moshi measures hallucination, traceability, and assumptions to earn trader trust

Moshi measures hallucination, measures every claim, checks traceability, and surfaces assumptions and reasoning so traders can trust its output.

AI Engineer · The Investor's Edge Lives in Their Head — Anekha Sokhal, Moshi

Traders reconstruct historical analogues and feed them to their models

Traders compare and understand the historical significance of events happening now — what happened in the past, why it happened, and what the market did — and then run that on their models.

AI Engineer · The Investor's Edge Lives in Their Head — Anekha Sokhal, Moshi

Two kinds of finance tools: pull vs push — and prompting makes you the context

There are two types of tools in finance: tools you pull data from and tools that push data to you. The push type floods you with information, which is a data engineering problem. And when you're prompting a model, you are the context — you're explaining to it, arguing with a model frozen in time.

described with high confidence

AI Engineer · The Investor's Edge Lives in Their Head — Anekha Sokhal, Moshi

The human brain is vastly more power-efficient than AI systems

The human brain runs on about 20 watts (the power of a dim light bulb), has 86 billion neurons making connections we still cannot fully understand, and AI systems built to imitate it require a whole power plant — making the brain multiple millions of times more power-efficient than the systems being built.

described; efficiency multiplier is approximate

AI Engineer · The Investor's Edge Lives in Their Head — Anekha Sokhal, Moshi