Principle · MoneyThe last principle is verification: you want a second pair of eyes, and this builds on the principle of accountability because you are accountable for that review as well. The principle of verification is building more feedback loops so you don't have blind spots, but without relegating any of the accountability. Verification is just extra information from a different source to avoid biases and avoid getting stuck on your own thinking and processes, but it doesn't remove any of the responsibility or accountability.
AI Engineer · Stop Chatting With Spreadsheets — Francesco Galletta & Felippe Felisola Caso, Monzo
Principle · MoneyMonzo's approach to AI harnesses is explicitly designed for financial use cases where trust matters, and the recipe may not transfer to less trust-critical contexts.
AI Engineer · Stop Chatting With Spreadsheets — Francesco Galletta & Felippe Felisola Caso, Monzo
Principle · MoneyAsking a stochastic machine to produce a consistent output means you are likely not going to get it. If the agent generates new code for every session, you get non-determinism: same question, different answer.
AI Engineer · Stop Chatting With Spreadsheets — Francesco Galletta & Felippe Felisola Caso, Monzo
Principle · MoneyThe trust users have in your product, agent, and its output is what drives usage, adoption, and improvement through the loops of tracing and evolution — so design harnesses so customers can trust the results.
AI Engineer · Stop Chatting With Spreadsheets — Francesco Galletta & Felippe Felisola Caso, Monzo
Report · MoneyWhen asked where a number came from, agents typically answer with vague references like 'we talked about this' rather than an actual verifiable paper trail — the speakers describe this as the default experience in every agent conversation.
AI Engineer · Stop Chatting With Spreadsheets — Francesco Galletta & Felippe Felisola Caso, Monzo
Principle · MoneyIf you wait for agents to get better instead of building deterministic guardrails, the agent will create stuff on the go — you may get the right code at one point in time, but by run 100 it's inventing code that didn't need to be reinvented.
AI Engineer · Stop Chatting With Spreadsheets — Francesco Galletta & Felippe Felisola Caso, Monzo
Principle · MoneyThe one takeaway the speakers want everyone aligned on: we are ultimately the responsible, accountable people for whatever AI agents do. 'Agents face no real world consequences, you do' — a quote the speaker loved but can never attribute. When someone says 'that's what Claude gave me,' it may feel fine, but we face the consequences of what agents do. The speaker gets hired and paid partially because he is accountable for the results he produces for the company.
AI Engineer · Stop Chatting With Spreadsheets — Francesco Galletta & Felippe Felisola Caso, Monzo
Principle · MoneyAI will likely not be perfectly accurate, so design mechanisms so that whatever process runs, you can trust the results to a degree.
AI Engineer · Stop Chatting With Spreadsheets — Francesco Galletta & Felippe Felisola Caso, Monzo
Principle · MoneyKeep a good-enough grasp of what the machine is doing. Going full black-box mode eliminates your accountability, and blame-shifting on the basis that someone else will review it is a real team failure mode.
AI Engineer · Stop Chatting With Spreadsheets — Francesco Galletta & Felippe Felisola Caso, Monzo
Principle · MoneyWhen you rely on an agent's output without an evidence trail and it turns out to be wrong, you bear the consequences — 'that's what Claude gave me' is not an acceptable justification.
AI Engineer · Stop Chatting With Spreadsheets — Francesco Galletta & Felippe Felisola Caso, Monzo
Principle · MoneyThe speakers argue that evidence is not just about where a number came from but how it was derived, and that curated, labeled data plus gathering as much evidence as possible is paramount. They stress that evidence must be embedded in the code itself, not just referenced externally.
AI Engineer · Stop Chatting With Spreadsheets — Francesco Galletta & Felippe Felisola Caso, Monzo
PrincipleApplying Occam's razor to AI method selection: the simplest explanation is more likely true, and simpler, more interpretable methods are more likely to be robust. If linear regression or decision trees suffice for an application, use them instead of neural networks.
Y Combinator · AI Safety Fact vs. Fiction | YC Paper Club
PrincipleMichael argues there is always a long tail of failure modes whose sum is a high probability, that even ACAS-X has nonzero failure probability in incredibly unlikely situations, and that ICAO/FAA safety standards do not require zero probability. He advises not believing anyone who claims a fully safe real-world system.
Y Combinator · AI Safety Fact vs. Fiction | YC Paper Club
PrincipleClark argues that in a world where writing code is easy and verification is becoming easy, the remaining hard thing is the specification. He distinguishes universal specs (memory safety for C) from domain-specific ones (no loss of funds for a bank), and notes enterprise code often lacks any specification at all, raising the question of how to formally verify it.
Y Combinator · AI Safety Fact vs. Fiction | YC Paper Club
Principle · MoneyWhat people should focus on is the evals — what's your evals and what's the results that lead to it is the right question, not the tuning method.
AI Engineer · The AI Bill Is Too Damn High: Tokenomics 101 — Eugene Cheah, Featherless AI
Principle · MoneyHarish Gaggar stresses monitoring and complete auditing for tool executions, arguing that AI agents cannot be trusted without continuous monitoring, evaluation, and feedback.
AI Engineer · Paving the Road to Production: Governed Enterprise AI Agents — Harish Gaggar, Credit Karma
Principle · MoneyHarish Gaggar recommends keeping a human-in-the-loop process for every action item and every high-risk item, with write and sensitive operations routed through HITL approval before execution.
AI Engineer · Paving the Road to Production: Governed Enterprise AI Agents — Harish Gaggar, Credit Karma
Principle · MoneyHarish Gaggar advises building a full enterprise framework with governance underneath rather than rushing a single agent to production, because teams that ship quickly later realize the frameworks should have been in place from the start.
AI Engineer · Paving the Road to Production: Governed Enterprise AI Agents — Harish Gaggar, Credit Karma
Principle · MoneyYou cannot rely on Anthropic or OpenAI to stop AI incidents from happening to you; they can make smarter models but LLMs have qualities that make them impossible to fully trust. Large language models are non-deterministic, meaning there is always some randomness or chance associated with what they do — you can ask it to do the same thing 10 times and it could do it 10 different ways. What you need to control is the infrastructure that your AI is running on and the environments it works inside, rather than relying on the model itself. Controlling infrastructure rather than the model is the difference between telling someone don't leave a room versus putting a lock on the door and actually using a key. Agents act at machine speed, so if a mistake is made it can make it 20 times or more more
AI Engineer · Your AI Isn’t the Problem. Your Infrastructure Is. — Jiachen Jiang, Coder
Creator perspective · MoneyThe core argument of the episode: what you need to control is the infrastructure that your AI is running on and the environments it works inside, rather than relying on the model itself. Paying five times more per token for a smarter frontier model is not the solution to agent mistakes; infrastructure controls are.
AI Engineer · Your AI Isn’t the Problem. Your Infrastructure Is. — Jiachen Jiang, Coder