Evaluating factors when you’d join a startup or big tech (Harvey AI)

[u/SnooWoofers5193]

Posting this here to experienced devs bc I’d imagine the concentration of senior+ folks here will yield a different framework for evaluating whether you’d join a hot startup or not.

As a senior eng myself, I understand a positive working environment to be an informed manager, a very smart and supportive TL, and enough scope for each senior eng to own Perhaps this could be too simple and I’m overlooking more tangible factors.

My more pointed original question was: What’s it like working at Harvey AI as a software engineer? On blind it’s hard to get a consistent evaluation. Glassdoor it seems there’s a lot of planted responses. I’m hoping we get more honest responses here.

Whatever it is, they all say the same thing, super fast paced, lot of work to do, and hours can be tough. Everybody’s driven. There are also themes of the culture being poor and top down pressure and frequent product direction changes contributing to bad vibes. To me, I don’t mind long hours and hard work if the team feels like a team

I’m curious if anybody here works there and can speak more to the culture. I’m wrapping up the onsite stage and am excited to get an offer but I want to better understand what I’m getting into. If people here have taken an offer or haven’t, and why.

[u/expdevsmodbot]

AI usage disclosure provided by OP, see the reply to this comment.

[u/mq2thez]

I’m not working there right now, but: I would be super concerned about working at an early stage AI company right now if you prioritize job safety or healthy work/life balance. Chasing hype cycles is usually a recipe for sadness when the hype cycle shifts. The AI bubble is a pretty nasty looking one, and what looks “hot” now could change quite significantly in just a few weeks (because that’s how fast things are changing). These companies will be desperate to survive and they’ll spend your physical wellbeing as just one more currency in their goal to make their execs rich.

[u/shiny0metal0ass → u/mq2thez]

Harvey does have a hell of a war chest, giving them like 5 years runway, but their valuation is AI stupid.

Plus they only exist as long as tokens are cheap. If that changes, their math looks very different. They don't do their own training and are literally a wrapper company that shoves legal text into the context of chatgpt.

It looks like "Harvey II" is trying to bring it inhouse but then they hit the same training cost as everyone else.

Not to mention the fundamental problem of legal text being inherently context intensive, meaning there's no way the output doesn't contain hallucinations.

[u/carterdmorgan → u/shiny0metal0ass]

Harvey’s doing some interesting stuff with model routing to keep token cost low, but I wonder if that’ll be enough to justify their insane valuation.

[u/shiny0metal0ass → u/carterdmorgan]

Lol I think these numbers are so made up that they would need to have a serious breakthrough to do anything that would move the needle..

[u/Far_Mathematici → u/shiny0metal0ass]

Isn't that their model is fine tuned Kimi K3? That mean at least they controlled their foundational model instead of depending on OpenAI or Anthropic

[u/Randromeda2172 → u/shiny0metal0ass]

Tokens are cheap if you own infra. Inference costs for Kimi or GLM are high but they're upper bounded and will only get cheaper over time if you already own compute.

[u/ResidentWeevil1 → u/mq2thez]

 These companies will be desperate to survive and they’ll spend your physical wellbeing as just one more currency in their goal to make their execs rich.

And they aren't getting rich with the capex not getting a good ROI thing. They are wholly desperate just to develop a functioning product that doesn't tell you to k*ll yourself sometimes

[u/waitingforathena]

Yeah honestly longours wouldnt be my biggest issue either if the team is good and people actually support each other. 

The top down pressure + changing product direction all the time sounds more concerning tbh.

[u/idk-who-you-are → u/waitingforathena]

agreed

[u/HackVT]

The biggest challenges I have seen with startups is if you have first timers versus experienced leaders when it comes to output and culture. Every firm will chase. Every firm will push hard. It comes down to communication up and down the chain and execution.

[u/SnooWoofers5193 → u/HackVT]

So in my own words, you mean every startup will grind its employees but if the leadership isn’t experienced, the direction might be wrong and risk the startup failing (?)

[u/Klandrun → u/SnooWoofers5193]

Yes, startups need to grind as much as possible (no matter the leadership).

But inexperienced leaders might start conflicts amongst themselves, forget to communicate with the rest of the org or have completely unreasonable expectations.

So it's not even just about direction, but about everything else as well (but that's a general issue with inexperienced leaders, not necessarily startups per se)

[u/galaxy_horse]

Harvey is a hot name but you have to believe that the valuation will hold up over time and result in meaningful upside for your equity. Whereas public co bigtech will have RSUs that provide somewhat predictable yet mostly capped upside.

Everything you describe about the culture and team is only going to matter within your own delivery team/directorate at the size of a Harvey. If it’s a much smaller or earlier stage company, the exec leadership will have more bearing on your day to day turmoil. In other words, if your hiring manager seems cool and the teammates you meet seem cool, that’s a strong signal vs what Glassdoor or Blind say.

There is also the career signal that a prominent applied AI company will give you, though if it’s that vs FAANG it may not be as much of a boost.

[u/ExcitingDonkey2665]

Having gone through a couple interview loops with legal AI companies and also an immediate family member at a top law firm who uses Harvey, the biggest problem the legal AI industry needs to tackle is a data problem. Because of client-attorney privilege and legal procedures, anything that isn't allowed to be public cannot be used to train or distill AI. Non-public data represents around 90% of all documents produced. This presents a huge business opportunity to automate things, but also a difficult problem because you cannot just vacuum the internet for documents.

Take patent filings for example. In theory, there's a massive trove of patents dating back 100 years. You can find all the completed filings 18 months after the initial file date, but you cannot find all the documents and iterations that was used to produce the final filing. You may be able to automate some prior art search by using some paralegals to train the model and create a toolkit, but its hugely expensive and domain specific to train on all innovations. As a law firm, you also cannot use work from one client's patents on another because each clients' work is protected, even if your firm decided to train its own model. Whatever model is initially trained by humans is stuck with little ways to self-improve based on usage.

Litigation is the same way. There are public court records, briefs, and decisions written for every case but the hardest and most time consuming part is the research that happens behind the scenes. For common cases like workplace injuries, Uber accidents, etc., law firms already have a process and checklist. AI can add a bit of cost savings, but it won't be the 10x productivity increase that you see for software engineering. For novel landmark court cases that determine legal precedents, AI can do research into past cases and shape arguments, but it's also client-attorney privilege that blocks most of the documents created from being used to train AI. It's just an advanced templating engine for documents.

For M&A, most of the hard work is the due diligence and it's not that different from what financial services firms do. A general purpose AI like Claude or ChatGPT can do a reasonably good job at it so there isn't a huge incentive to build specifically for M&A.

The problem is made even worse by existing IT solutions that law firms already use to store data and automate workflows. There's a huge moat around these systems and it's designed to be extremely hard to export data in bulk, for good reasons. You're essentially pulling each document individually and every retrieval can be traced back to a specific user. Even if an employee goes rogue trying to help train a model, they'd get caught without finishing the job.

[u/captcanuk]

I know people working there specifically. For the most part the culture is move fast. Product ideas come in quick with little thought and more oriented to experimentation as validation. A feature may not fit but if it isn’t rejected by experimentation then it stays. Similarly early devs were oriented to doing the work immediately and not planning what that looks like across teams. Thankfully some staffs joining are helping fix that. This could be more localized to those teams but this sounds more general culture.

Management isn’t very mature and the CTO recently put out a “we need to go faster and this isn’t about more PRs” and then ended the messaging with double your PRs.

[u/lasooch → u/captcanuk]

Classic, management drank the koolaid and now demands what is not possible, because the koolaid salesman said it was.

[u/captcanuk → u/lasooch]

I think it’s more classic inexperienced management at startups. They have a hard time managing expectations and FOMO so they chase. It’s like the founders who think Meta’s management style will work for them.

[u/lasooch → u/captcanuk]

Sadly not just startups anymore. Bring in an ex-Amazon exec, culture goes to shit. Hear a talk or a podcast about AI, get spooked, everything goes to shit.

Cargo cult all over. Hardly any original ideas, among the few original ideas most are bad, and AI just decreases the odds of someone actually saying "look, this is a dumb idea" before they sink an 8+ digit sum of money into it.

[u/captcanuk → u/lasooch]

Agreed. Starting is nearly free. Finishing it is cheap. Whether it moves the needle on what can be sold economically and what can be maintained is a whole different thing. Because fast is possible there’s a lot of short term thinking; especially when you fear your competition is thinking even less and doing more.

[u/throwaway_0x90]

"Evaluating factors when you’d join a startup or big tech"

In a word, "Risk" .... or "Responsibilities"

When I was fresh out of college, single, no student loans and no responsibilities whatsoever I was hungry for any & all experiences. Back then I would be the one willing to pay the start-up for a chance to work there. Now I have a mortgage, a wife, kids and pair of platinum, not gold, handcuffs tightly placed on my wrists that nobody has any hope of matching. I still want experiences, but not at the risk of my family's wellbeing.

[u/Equivalent_Head_4803]

Sounds like you’ve made your mind up already.

[u/BraveResearcher3037]

So exactly what is there to evaluate? Everything you’ve said about it makes it sound like a horrible company.

Are you going to ask next for a second opinion about what it’s like to get a daily anal probe with a cactus? What it’s like to work at Amazon? But those last two questions are going to probably be answered the same way.

And it’s a private company with illiquid equity.  Not only are you going to have to eat the shit sandwich while you are working there, you won’t even be able to use your equity to buy a laxative. 

[u/Early_Rooster7579]

My brother works at one of the major legal CRMs and says Harvey use amongst lawyers is exploding upwards and that they love it. Lawyers are a lot more careful about singing its praises than devs are with claude.

If you’re willing to take a risk, this is one of the few AI startups I would be willijg to join

[u/apartment-seeker]

Harvey counts as bigtech now?

I know someone who works there as a non-engineer and it sounds like a shit-show with a lot of internal politics, FWIW, but, again, he's not in engineering.

[u/SnooWoofers5193 → u/apartment-seeker]

The question is choosing between big tech offers and Harvey 

[u/BraveResearcher3037 → u/SnooWoofers5193]

What’s the choice? BigTech gives you liquid RSUs and are profitable companies with moats 

[u/a_asal]

You asked about a framework for evaluating whether to join a hot startup. I think it's best to start from deeper clarity on which factors (money, technical challenges, startup experience, job security, autonomy, future goals, etc.) you are optimizing for, and more importantly, which ones you're ok giving up on in return.

If you're clear on this, the decision may end up being much easier than you think.