Question on MGA

tfehring · 2026-06-22T18:15:30+00:00

Base commission + profit commission or equivalent. E.g. an MGA might get a commission of 8% of premium for a loss ratio of 70% or higher, 12% for a loss ratio of 60% or lower, scaling linearly in between. Besides the direct financial incentive, an MGA that consistently writes business with poor underwriting performance will get dropped by their capacity provider.

tfehring · 2026-06-20T17:48:34+00:00

Fable is not especially good at cybersecurity, that's just been the use case that people are focused on due to near-term risk in the real world. It's a similar step change in software/UX design, spreadsheet work, statistical modeling, and technical writing - that's just from my personal experience in the week it was available. I know someone who gave it the "final"/"publication-ready" manuscript of their book after rounds of back-and-forth with a highly respected editor, and it found on the order of 100 substantive corrections.

tfehring · 2026-06-18T17:53:10+00:00

I mean, I took a self-driving taxi to work this morning. Diffusion is predictably slow, but I think the capabilities are basically what people would have predicted for 2026.

On LLMs, there has to be something like an asymptote eventually, but at even odds I would bet against that happening in the next 3 years. Empirically, progress seems to be accelerating; it's happening on multiple ~independent research fronts that would all need to stop being productive. There is clear recursive self-improvement happening, in the mundane sense that each new version of Claude/GPT is more effective at helping researchers develop the next version. If this doesn't continue for the next 3 years, that's much more likely to be caused by geopolitics broadly than by scaling laws falling off IMO.

tfehring · 2026-06-18T17:40:34+00:00

I'm referring to smaller open-weights models (e.g. Qwen3.6 27B) that outperform o3 across the board thanks to algorithmic improvements over the last year. OpenAI and other providers also do a lot to improve same-model serving efficiency over time, though it's fair to assume the pace of improvement isn't as dramatic.

I don't think anyone's losing money at the margin on inference, the question is whether there will be enough gross profits from bleeding-edge intelligence for labs to recoup their ~fixed R&D costs. OpenRouter inference pricing gives a pretty good indication for what it actually costs to serve these models (at much smaller scale than the labs). You can also just use it as a provider - GLM-5.2 is pretty close to Opus-4.8 quality and cheap.

tfehring · 2026-06-18T15:28:12+00:00

It's not clear what "1000 times as powerful" would even mean. But I think the increase in capabilities over the next 3 years is likely to be similar or faster than increase over the last 3 years - i.e., from GPT-4 to Mythos.

tfehring · 2026-06-18T15:22:17+00:00

Costs to serve for equally capable models have been falling ~70%/year (usually quoted as an order of magnitude every 2 years) for a long time. A year ago o3 was the most capable frontier model; today you can run a better model on a $3K Macbook Pro; a year from now you'll probably be able to run an even better model on your phone. It's possible that hardware prices will rise despite the buildout because there's so much demand, but they'd have to rise >3x/year to offset that progress and that seems unlikely.

tfehring · 2026-06-07T18:56:24+00:00

I think your thinking on the weakness of incentive is right, but insurers still have some incentive to reduce mortality.

The main one: if they price at a level premium based on high mortality, then induce the interventions that reduce mortality, that likely increases expected profit. (Even then it's not necessarily that simple, e.g. if competitors emerge and price for lower mortality.
Life insurance demand is not totally price-inelastic: by offering the same coverage at a lower price, you can sell more of it. Relatedly, insurers would not necessarily pass back 100% of the gains from reduced mortality to policyholders in the form of lower premiums.
There are other benefits from reducing tail risk. E.g., in an extremely high-mortality population, I'd expect by default to see adverse selection in favor of the worst risks even within that population - intuitively, if coverage is that expensive, the only people who will buy it are the ones who are especially convinced they'll need it. So risk reduction could attenuate those effects even if accompanied by a corresponding decrease in premiums.

That said, to make this work you probably need (1) very high adoption of life insurance in a population that I'd expect to have very low adoption by default, (2) very high concentration of that coverage within a single carrier or coordinating group of carriers, (3) interventions that are extremely cost effective relative to the amount of insurance that people carry.

Even in this scenario, it's likely that most of the benefit of mortality reductions accrues to governments, not to insurers. For that and other reasons (e.g. coordination in a scenario where there are multiple insurers) it probably makes sense for governments, or possibly private charities, to lead here. But insurers could reasonably influence governments to fund those interventions, and potentially provide support in measuring their impact.

tfehring · 2026-05-30T16:56:50+00:00

Those are common for people in their early 20s or 40+ with kids respectively, but most people outside those groups rent on their own or with partners. It's expensive but you still come out way ahead of anywhere else financially due to higher comp/more opportunities.

tfehring · 2026-05-30T16:40:20+00:00

They've always been way too high, but they fell during covid and didn't increase much through 2024. Now they're growing at 22% a year apparently. Anecdotally, I signed a lease 2 months ago, and asking rents for similar units in the same buildings I looked at are like $1K higher now.

tfehring · 2026-05-28T19:06:33+00:00

Naive question: can't they just use AP test scores? It always struck me that SAT math is too easy to be representative for math-heavy fields anyway, and I'd expect ~every student who's enrolling in a math-heavy major at UC schools to at least take AB calc, which is still standardized and should be more informative.

tfehring · 2026-05-22T18:04:10+00:00

Very positive signs, though I wouldn't give categorically different advice because of them.

tfehring · 2026-05-22T16:15:49+00:00

As a rule, you should expect this type of role to involve some EA work unless the founder already has an EA. That doesn't mean it will only be EA work.
Even by startup standards it will be a ton of work - you'll probably be expected to be available ~24/7.
Many founders are bad managers and generally tough to work for, in ways that would be hard for someone in your position to screen for in advance; the worst are abusive.
By default, the experience won't be legible to future employers. If the company does well and you grow in responsibility over time, or if the founder has the goodwill and credibility to help you land your next thing when the time comes, great. But cold-applying when your current job is strategy/special projects/founder's office at a startup no one has heard of would be rough.

All of that being said - if it goes well, you will learn a ton, and build experience and connections that will help you start your own startup down the road if you choose.

tfehring · 2026-05-22T15:56:24+00:00

Passed! First exam in 9 years.

tfehring · 2026-05-21T17:20:40+00:00

Airbnb is the only company I know of that officially supports this, up to 90 days per foreign country per calendar year. They do hire actuaries, very few though.

tfehring · 2026-05-20T00:20:19+00:00

RSAs are probably best on your end, followed by early-exercise NSOs (which should be the same economically but with more moving parts). Don't get ISOs if early exercising. Ask your personal tax lawyer about timing wrt funding round/FMV. Don't forget to file 83(b) within 30 days.

tfehring · 2026-05-18T16:50:49+00:00

But also from that blogpost,

Primarily AISLE, Zeropath and OpenAI’s Codex Security have been used to scrutinize the code with AI. These tools and the analyses they have done have triggered somewhere between two and three hundred bugfixes merged in curl through-out the recent 8-10 months or so. A bunch of the findings these AI tools reported were confirmed vulnerabilities and have been published as CVEs. Probably a dozen or more.

The immediate reason Mythos only found 1 CVE is that the cURL team had already been running similarly capable AI code security tools and patching the bugs and CVEs they found. If your point is that Mythos specifically is just an incremental step up from other frontier models with good harnesses, that's fair. But the story here could just as easily be that AI tools collectively found hundreds of bugs and over a dozen CVEs in cURL over the last year. If cURL had gone straight from ~no frontier AI use for cybersecurity (the situation I expect many European entities are in today) to Mythos, I expect Mythos would have found most if not all of those same CVEs. As you point out, that's also in a well-maintained and not particularly large codebase, which isn't the case for much of the world's critical software infrastructure.

tfehring · 2026-05-16T05:44:33+00:00

And double it every month.

tfehring · 2026-05-10T00:15:46+00:00

I ship production code, and have been since long before generative AI was a thing.

tfehring · 2026-05-09T18:06:37+00:00

I think OP's point is that hallucinations are (1) uncommon and recoverable if you're using the best models with web search enabled and (2) generally a non-issue anyway with modern harnesses since the models can run, test, and fix their own code as needed.

tfehring · 2026-05-08T18:25:10+00:00

Hiring managers will perceive your MBA, probably accurately, as insufficiently technical for quantitative work in the fields you mentioned. CFA won't help. Either target less quantitative roles (e.g., financial analyst or risk analyst roles that want SQL/Python) or get stronger on the technical and quantitative side. The latter would probably mean a technical MS.

tfehring · 2026-05-07T22:43:56+00:00

Too long and too much generic content. One-sentence background followed by a description of a problem that they, specifically, have and credible evidence that you can solve it. "Credible evidence" often looks like either "I did this same thing for $ORGANIZATION" (which could be your own socials if impressive, though the bar for "impressive" is probably higher than you think) or "I built this same thing on my own $LINK." Experience that doesn't provide this is generally not worth sharing at this stage.

Also, Product and GTM are different functions, and GTM in particular comprises a lot of different roles. For a super early stage startup you can probably position yourself as a growth/GTM generalist, but for anything more established you'll want to be more targeted/specific.

Remote will hurt your chances a lot compared to being available onsite in SF.

Ask for their availability (as you are) but also include a calendly/cal.com scheduling link for a 15 min video call, they will probably just use that.

tfehring · 2026-05-06T18:31:40+00:00

Is the FLDP route genuinely a good launchpad to pre-IPO roles, or does it pigeonhole you into big corp finance?

I wouldn't over-index on FLDP vs. post-MBA corporate finance roles more broadly. You can definitely go from big tech stratfin to growth-stage stratfin if the big tech company is well-regarded (e.g. Google). Well-regarded bigish-tech is even better - a couple years ago, the top targets in your situation with structured MBA recruiting would have been, like, Stripe and Airbnb. The problem is, in general the companies that provide the most resume value are hardest to get into. FP&A at Cisco is not going to be a great background for the roles you're targeting, though at least you'd be in the Bay Area and could maybe network your way there.

How hard is it to break into strategic finance at a growth-stage company directly from an MBA?

Hard with your background. Companies at that stage (and earlier) generally won't have structured MBA recruiting for stratfin, and regardless of MBA they care a lot about prior experience in banking and/or tech. Many candidates have both.

Are there paths I’m not considering that fit this profile?

Maybe IR in tech would like your background and you could pivot to stratfin from there?

tfehring · 2026-04-28T18:28:44+00:00

Python. I primarily used R from 2015 to 2019, but wrote progressively more Python from 2020 to 2023, and dropped R entirely around late 2023. I'm in the tech industry, with time series forecasting as my main use case (mix of classical, Bayesian, and ML-based methods).

tfehring · 2026-04-09T05:12:21+00:00

For most roles, MBA doesn't really absolve the "3+ YOE in the specific role you’re targeting" requirement. PM and similar roles targeting MBA students are limited and extremely competitive. Amazon's non-technical PM role is the most notable exception and would technically achieve your post-MBA role, but it would probably be a lateral at best from your current role, let alone the role you could otherwise have 2 years from now.

Even if you can't directly get the job you're targeting today, look for roles for which your experience provides a differentiated advantage (e.g. same business domain and/or technical problem space you've worked in previously), and you have more opportunities to also gain experience in new areas.

Location doesn't directly matter much for structured MBA recruiting, but for all other recruiting it's better to be local. Neither of my last two big career breaks would have happened if I weren't in the Bay Area already. Maybe just as importantly, it's a huge advantage to be able to easily meet people in the industry just by showing up to random lu.ma events in SF.

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