what have I done to help? garbage plate native. 👩🏻👧🏼👦🏻🐶. partner at rallyday (lmm pe). tweets my own.
Denver, CO
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Some good stuff in here re: How to do a networking call if you’re a young one…
I’ll add:
- do your research! No excuse today to not properly prep. If you ask obvious questions you will look unprepared
- set the context. “I’m super green and trying to learn what an investment banker does” is very different than “I’m dead set on investment banking”. This preps the person on the line and can give you grace if you’re super early
- your story / pitch should be perfect. Practice practice practice. If you’re super green you can even ask the person to help you with the pitch.
- be incredibly easy to schedule. Send your resume beforehand. Don’t dress like a slob.
- “what do you think got you to position xyz” is a great question, imo. Lots to learn from success stories.
- “given my resume, pitch, and goals, what advice do you have for me?” This should unlock some gems
I got my job in PE out of business school doing this stuff. It works. Good luck!
On this note, I wanted to highlight some of the things you should be doing if you want the call to go well:
1) When the call starts and the person you're talking to asks you something along the lines of "How's your day going," do not just reply with good. Say what you've been up to so far or have planned for the day, lie if you have to. If you just rolled out of bed at noon tell him you went to the gym when you woke up and just got back from class.
2) Keep the small talk going for a bit, but once that runs its course, find a way to transition and take charge of the call. It's weird to go from small talk to firing straight into questions about the job. I'd recommend a transition that thanks them again for taking the time and lays out a framework for the rest of the call.
3) It's always good to have questions prepared to fall back on, but its important that you ask good follow ups that show you pay attention and have some understanding of the industry. If you just go down your list of prepared questions that don't flow together well, it's going to come off as unnatural.
4) Check on time at the 30 minute mark. Say something like "I see we're getting pretty close to (whatever time it's about to be 30 minutes after the start), do you have a hard stop or do you mind if I ask a couple more questions? Only do this if the call is going well. If it's going poorly, you should just kill it there by acknowledging that you're coming up on 30 minutes and thanking them for the time.
At the end of the day, it just comes down to reps. Your first couple are going to be bad. Once in a while, someone is going to be having a bad day and be a dick to you on the call. Never forget that its a numbers game.
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one of the things we hear about ai usage is how "scattered" all the tools & documents & folders are - drives disorganization and adoption issues
we built most of our use cases in an app, which stitches a lot of the tools and plumbing together in one place. highly recommend.
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The data center buildout has driven a wave of dealmaking in power & utilities, from Big Tech, private equity and strategics.
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Haven’t gotten anything done and some thoughts we put together 👊🏻
we've poked around the power engineering space and you can read our most recent POV here:
docsend.com/view/353mjibswws…
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Working in finance for long enough will make you realize just how many people are successful in their careers, purely because they know how to build and keep relationships, rather than because they have any merit to offer
Almost every major firm you can think of has a few senior leadership folks and investment committee members who are simply there because they know the right people and can bring in the right deals.
Some of them have horrible investment acumen, tend to be narrow-sighted, and often suffer from analysis paralysis. No one actually respects their judgement when it comes to making the right decision on an investment.
But at the same time, the firm can't let them go because they act as the sourcing engine for the business. They are extroverted, know the right bankers and management teams, and can bring in bespoke deals that others could not have sourced
Makes you realize that there are truly two ways to be extremely good at your job in finance
1. Actually be good at your job, invest well, be an expert at an industry, and add value based on merit.
2. Be the person that everyone calls when they need a capital solution
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The majority of our associate candidates over the last few years have had a tough time with the model test (score less than 5 out of 10). It’s a 3 hour test.
Claude scored a 10/10 in 4 minutes today. 😳
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I actually had to test mine vs Claude when hiring so I knew what to look for to catch obvious cheating. Live walkthrough immediately after helped but I’m sure many candidates used it without me knowing (30 mins model test though, so more straightforward overall)
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banker event at our office in denver tomorrow with some hands-on ai work, dinner, drinks, etc. shoot me a note if you fit the description and want to come by - 3pm start 🤘
we are hosting a banker event with nimble gravity at our place on 10/1 around "ai in investment banking".
dinner, drinks, hands-on use-case / demo stuff - we'll have a big team from NG, including CEO, engineers, etc.
if you're a lmm / mm banker and want to attend, we have a few slots left. shoot me a dm.
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Had an similar experience on the interview task I did for my last HF role, although required a bit more engineering to get the right agentic setup to get it to work...
substack.com/home/post/p-207…
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To Jon’s point…Would you hire Claude for the role you are hiring? Would you hire a human who could use AI tools to do their job better? You said yourself things are changing quickly. won’t you need human judgement/delivery of the results of the model?
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Interesting.
Do you think that supports doing the model test or suggests maybe not?
Not sure I've ever seen model tests / assessments at that level.
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we are about 9 months into our ai project - probably ~4 more to go. we have a fully dedicated engineering team from nimble gravity + a consultant from NG. azure > databricks > anthropic is the bulk of our stuff.
biggest thing for me: will this help us do more great deals? fwiw, I'm a believer that the two biggest areas of differentiation for any firm are sourcing and building. I'm also generally a tech skeptic. some observations / thoughts:
1. if your data is poorly organized / if your firm doesn't already have decent people / process / tools infrastructure around managing data, start here (this is a heavy lift imo and is more cultural than technical)
2. be great about the tedious discovery and process mapping to start (this took 10 weeks for us)
3. be great about building a proper change mgmt program (set up a show & tell cadence, set up the slack channel, etc. do we measure usage? is it part of performance reviews?). be thoughful about measuring ROI
4. I'm pretty blown away by what's possible. this job will change a good bit in the next 12-24 months. if your firm is not building you are behind. for more senior folks, if you are great at process but can't draw a straight line to returns and / or fundraising, you need to figure it out = get on the road and shake more hands (which all this work should enable)
Our original use-case mapping is here (has changed a bit as we've gone through the process): docsend.com/view/4furdqmifm7…
Detailed use-case stuff here: padraicmcconville.com/writin…
I'm polling hundreds of finance professionals on how they use AI at work
It's a no bullshit survey on what vendors bankers and investors actually like and use, and I'm extremely excited to share the results in a couple weeks
Survey form is below but curious to hear from you
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I'd love to know the answer to this one too.
This seems like an exciting project, with a lot of room for potential overengineering.
But if done right it could change the game for a small firm like Rallyday. I'm eagerly reading all your posts about it now.
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Hey Henry, Asrar
1. thinking about rdp as an operating company and constantly asking How do we do this better?
2. Nimble Gravity is a portco, too; deep, trusted relationship. we've had a front row seat to their work for a couple of years now - don't think we would have gone down this road without them as a partner + the quality of the relationship
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Good advice here
we are about 9 months into our ai project - probably ~4 more to go. we have a fully dedicated engineering team from nimble gravity + a consultant from NG. azure > databricks > anthropic is the bulk of our stuff.
biggest thing for me: will this help us do more great deals? fwiw, I'm a believer that the two biggest areas of differentiation for any firm are sourcing and building. I'm also generally a tech skeptic. some observations / thoughts:
1. if your data is poorly organized / if your firm doesn't already have decent people / process / tools infrastructure around managing data, start here (this is a heavy lift imo and is more cultural than technical)
2. be great about the tedious discovery and process mapping to start (this took 10 weeks for us)
3. be great about building a proper change mgmt program (set up a show & tell cadence, set up the slack channel, etc. do we measure usage? is it part of performance reviews?). be thoughful about measuring ROI
4. I'm pretty blown away by what's possible. this job will change a good bit in the next 12-24 months. if your firm is not building you are behind. for more senior folks, if you are great at process but can't draw a straight line to returns and / or fundraising, you need to figure it out = get on the road and shake more hands (which all this work should enable)
Our original use-case mapping is here (has changed a bit as we've gone through the process): docsend.com/view/4furdqmifm7…
Detailed use-case stuff here: padraicmcconville.com/writin…
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Got this today.
Unfortunately, I think that we are at a point where AI has gotten good enough that new graduates add very little value to most jobs in finance
This was always the case, but the rationale for hiring young talent before AI was two-fold
1. They soak up grunt work around the office, including tedious number entry into Excel, logo aligning, calendar invites, and administrative tasks on a deal
2. Train up the next generation of talent who can go on to become senior leaders in the future
The first one has been pretty much already solved by AI to a large degree. Even if not fully replaceable today, the state of progress tells me that almost all finance grunt work will be handled by AI in the next 5-10 years. Probably even sooner
The second one still creates incentive for companies to hire young graduates, but at a much slower pace than before.
> AI leads to higher productivity across senior management and mid-level employees, freeing up their time to focus on bigger picture
> Need fewer senior management and mid-level employees in the future due to this productivity boost, which pushes down the need for young graduates even further
> Even if you dont completely shut down the analyst program, the number of analysts you need on the job certainly shrinks down by 20-30% at least, likely in the next 3-5 years
> New analysts coming in are lower quality due to being reliant on AI during their college years and requiring AI to do any sort of critical thinking. This in turn hurts their ability to get promoted down the line
All of these factors combined likely lead to a very difficult hiring environment for students studying finance in college today.
The best piece of advice I can really give is to embrace the tools as fast as you can and try to keep your critical thinking and imagination skills intact while doing so.
This is going to be much tougher than it sounds, especially as AI progresses in the next 5 years
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I do think it places a larger component on the actual due diligence. Deals will both look "better" and simultaneously exactly the same. Customer service, responsiveness and reputation will become significantly more.
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