They Just Revealed Their Internal Process for Becoming an AI FDE
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Transcript
00:00:00if you're someone who knows how to use ai there's a huge opportunity in front of you right now and
00:00:04interestingly enough even though it's one of the roles companies need the most they can barely find
00:00:09anyone who can do it that opportunity is a role called forward deployed engineer or an fde and
00:00:14it's not that the work is hard to learn it's just that people don't know this role exists in the
00:00:19first place and over the past month this role has gotten important enough that the people running
00:00:24these teams at openai anthropic and cursor have all come out and given talks on it and you don't
00:00:29normally get to hear any of this because they're walking through real projects inside real
00:00:33businesses so we've gone through all of them and pulled out the parts you can actually use now if
00:00:38you're new to the channel then welcome we're a software company and this is ai labs where we show
00:00:43you how to optimize a business with ai using proven methods from our own team and in this video we're
00:00:48going to go over what an fde is how the big companies actually do this work and how you can start doing
00:00:53it yourself but before we go into anything let's start by defining what an fde actually
00:00:58is a forward deployed engineer or an fde for short is basically someone whose whole job is getting a
00:01:03business to actually use ai and that doesn't mean going in and advising them or handing over some
00:01:09strategy document it means getting ai running inside the systems they already use so that the work
00:01:14their people were doing by hand just gets done by ai instead no matter what kind of process it is
00:01:20there are three things working in it either it is a human who's doing the task or it is software that
00:01:25follows hard set rules to do the task or it is an ai and an fde is someone who actually knows which of
00:01:30those three should handle which task in the process for example a company put an agent on its refund
00:01:36requests and on paper it worked it read each request checked it against the refund policy and turned
00:01:41down the ones that didn't qualify but a few weeks in the company started losing customers they'd had for
00:01:46years and nobody could work out why because every refund the agent turned down was one the policy said to
00:01:52turn down so the fde went and sat with the person who used to do that job and turned out they had a step
00:01:57of their own which wasn't documented they checked how the order had been paid for and if the purchase
00:02:02was on a company card they would just approve it without reading any further because those come
00:02:06from businesses that buy every month and arguing over one refund costs you the account the policy
00:02:11said nothing about that they'd worked it out themselves years ago so that check went into the
00:02:16workflow as a fixed rule ahead of anything the model decided now when you're working with people there is a
00:02:22problem most of them can't tell you what they need they'll describe something they've already imagined
00:02:26instead of the problem they're actually having and that's the biggest issue of getting a business to
00:02:31adopt ai here's why the people that actually do the work always just need to complete their job they
00:02:36just want that the output comes out as easily as it can they don't look at the process itself so it's
00:02:41really hard to build something that they would adopt and that's exactly where most businesses are right
00:02:46now ever since ai got popular they've been adding it into everything they do without actually knowing
00:02:51if they even need it in the first place so that's why everyone's suddenly hiring someone who can
00:02:55actually sort this out for them job postings for this role are up 729 in a year aws put a billion
00:03:02dollars into building a whole department of forward deployed engineers and openai's own fde team went
00:03:08from two people in january to 39 and all of that happened really fast because three years ago basically
00:03:13nobody needed an fte and now there are over a hundred startups hiring for that role inside y combinator
00:03:19alone which is where a lot of the biggest startups today got started colin jarvis runs the forward
00:03:24deployed team at openai and he says there just aren't a lot of fdes out there so the demand turned
00:03:29up before the people did before we dive deeper into this role it would be great if you subscribe to
00:03:34the channel and hit the hype button this small gesture of support goes a long way for us so the demand is
00:03:40there but that still doesn't explain why an fde has to be a separate job because the tools aren't the
00:03:45hard part anyone can sign up for them and most businesses already have last year mit ran a study
00:03:51on exactly this they looked at 300 ai projects and surveyed hundreds of people inside the companies
00:03:56that ran them and they found that 95 of those projects produced no measurable return at all and mit's
00:04:03own conclusion was that the companies themselves caused the failures not the models the ones that got
00:04:08nothing were using generic tools that looked good in a demo and fell apart the moment they were used on
00:04:13real work and nothing they built around those tools ever learned how their business actually ran now
00:04:18vasuman moza does this kind of work for a living he was an engineer at meta and now his company gets ai
00:04:24working inside big companies essentially he is an fde so he's watched a lot of these projects go wrong
00:04:30up close and he says ai is just getting slapped on top of broken processes because nobody actually looks at
00:04:36the process first one executive he spoke to burned through a 10 million dollar budget in three months
00:04:41and it was meant to last a year his company had handed the tools to everybody and just left them
00:04:46to it so everyone spun up whatever they felt like the money went and none of it made the business any
00:04:51better at anything it was already doing and the reason that work has to be somebody's whole job is
00:04:56that nobody already inside the business is going to do it the engineers have their own work to get
00:05:00through and the people running the process have been doing it for so long that the workarounds in it just
00:05:05look completely normal to them and palantir lost a whole year to one of those workarounds a move
00:05:10onto a new file format was stuck because one engineer kept saying the new format was worse and nobody
00:05:15could work out why until someone actually watched her work turns out she'd been checking the data
00:05:20by double clicking the files open and the new format had nothing you could double click so the team built her
00:05:26something that night that let her open the new files the same way and she approved the move two days later
00:05:31so that's why it's worth having somebody whose whole job is to work all of that out before anything
00:05:36gets built because the alternative is a waste of all your cost and efforts a lot of big companies like
00:05:42anthropic and open ai have recently given talks about how they handle this whole process internally
00:05:47we compiled the best of those talks and when we analyzed them we found that the same patterns kept showing
00:05:52up in every single workflow the first step is to pick the process that's actually costing the business
00:05:57something and with ai that basically means volume so you're looking for the person who handles the
00:06:02same kind of message 50 times a day because automating one message saves nobody anything but automating
00:06:08a message that needs to be sent a thousand times a week is where the business actually becomes more
00:06:13productive and earns more and that job is easy to find now because a business already has its whole past in
00:06:19its support history you hand that to a model and it tells you where the volume is open ai's team did
00:06:24exactly that at one of the biggest banks in the world they went after the one job thousands of
00:06:29advisors did every day and around 98 of them ended up using what they built the second step is to build
00:06:35on top of whatever the business already runs and with ai that matters way more than it used to
00:06:40because an agent is only worth what it can reach so if a team already keeps all their work in notion you
00:06:45don't build them a separate system and move all of that across just so an agent can read it more easily
00:06:50you connect the agent to their notion with an mcp and everyone carries on working the way they always
00:06:56did that's how most of them work because they don't want to give up a system they've already put a lot
00:07:00of time and money into one of moza's clients had spent five million dollars and five years getting onto their
00:07:05finance system so moving them off of the system was never going to happen the whole job was getting
00:07:10everything else in that business talking to it the third step is to not change the way people work more
00:07:16than you have to if somebody's been running an 11 step process for years and you hand them a one-step
00:07:21version they stop using it they used to check the work as they went and now the middle is gone and
00:07:26they're being asked to trust an answer that just appeared so moza says you need to leave the process
00:07:31looking like the one people already know you let the agent do the work inside each step but you keep
00:07:35the steps there so people can still have something they can see which would let them know the answer
00:07:40is right and the last step is to budget way more time for trust than for building at that same bank
00:07:45open ai went into the technical side was finished in six to eight weeks and then it took another four
00:07:50months of pilots and testing before the advisors would actually rely on it because if you're doing
00:07:55something every day the same way you've done it for the past year then any change to that process has
00:08:00to earn its way in whether you're handing it to 5 000 people or to five but before we move forward
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00:08:55pinned comment so that's how the big companies get ai into a business now if you want to actually run
00:09:00this yourself the roadmap is five steps and the first one happens before you build anything first you need
00:09:06to watch how a job is really done and write down every single step of it in the order it actually
00:09:11happens then you need to ask why each of those steps is done the way it is and if nobody can give
00:09:16you a concrete reason that's usually a workaround somebody put in years ago that nobody has questioned
00:09:21since second you have to decide which parts of that process should actually become ai and which parts
00:09:26shouldn't because if you hand the whole thing over to a model that's exactly how you end up in that 95
00:09:32of ai projects that never produce anything so you need to run every step you wrote down through three
00:09:38filters the first is whether that step follows a fixed rule and has to come out right every single
00:09:43time and if it does it just stays as ordinary software and doesn't need to be automated with ai the
00:09:48second is whether somebody has to read something messy and make a judgment call on it because that's
00:09:53the part the model is actually for and the third is what it costs when it goes wrong because if getting
00:09:58it wrong is expensive that step stays with a person even when a model could do it and once you've done
00:10:03that you'll almost never find that the whole process should be run by ai some steps stay as software
00:10:08some go to the model and some stay with a person in moza's example of an eight-step process four of the
00:10:14steps ended up running on their own another three ran with a person checking the output before it went
00:10:19anywhere and the last one stayed fully human and the reason for that step being fully human is because
00:10:24it's a business call which ai cannot do yet so either the cost of getting it wrong is too high to hand
00:10:29over or the step doesn't come up often enough for a model to be worth the trouble third you need to
00:10:34build for the ways the system can fail and not just for the case where everything goes right and moza says
00:10:39that when there's only one way something can go right there are also a thousand ways it can go wrong
00:10:44so if you only build for the way it goes right what you built is worth nothing and with ai that mostly
00:10:50means handling the places where the model isn't sure because it'll hand you an answer either way
00:10:55even when it has no idea fourth you need to make sure the system actually works before you let it near
00:11:00anything real now a model gives a slightly different answer every time even if you ask it the same
00:11:05question so if you only test the system a few times you're going to miss the rare ones where it falls
00:11:09over what you do instead is take real examples out of the process you wrote down where you already know
00:11:14the right answer then you run the system across all of them and count how many it got right and then you
00:11:20go and read the ones it missed and fix those and last you have to put a number on what it was actually
00:11:25worth and you should be able to say whether it brought money in or took a cost out or made a risk
00:11:30smaller because nothing else counts now cursor had somebody complained to them that an agent was
00:11:35costing him two thousand dollars a day and when they asked him what that agent was actually doing
00:11:39turns out it was just picking which engineer to send out to fix broken equipment so then they asked him
00:11:44what it was costing him to send the wrong person out and it was more than two thousand a day and he
00:11:49agreed straight away because all he'd been looking at was what the agent was costing him and never what
00:11:54it was saving him and those five steps are how we built the internal chatbot in our own software
00:11:59company now if you've seen our previous videos you'll know it's basically just a chat interface
00:12:03that lets the non-technical people on our team use claude code and everything it can do we built it
00:12:09because the people in hr and accounts aren't engineers and everything around claude code assumes
00:12:14you are one before claude code could do anything useful for their work it'd have to be connected to
00:12:19slack and email and the rest of what they use every day and that's a setup job none of them were ever
00:12:24gonna do so the chat interface does all of that connecting for them and they just type what they
00:12:29need but we didn't start building our engineers went and sat with the people in hr and accounts
00:12:34first because engineering had no real idea how those departments actually spent their days so they spent
00:12:39proper time in those departments and wrote down every single repetitive thing that was being done by hand
00:12:45and then worked out which of those a model could reasonably take over and then that document turned
00:12:50out to be most of the build those written up processes became the instructions the agents follow
00:12:54and the knowledge the chatbot answers from then we handed the first version back to those departments
00:12:59and had them use it on their own real work and they came back and told us it was actually saving them time
00:13:05now the last question is what you actually need to be good at to run any of this one of the companies
00:13:09that hires for exactly this job wants people who are wide across business process and technology with
00:13:15one area they're genuinely deep in and they say the business half gets taught on the job while the
00:13:20technical half doesn't but for you that's probably backwards because you just have to focus on the
00:13:25business half because the building is handled by agents now and that's fine because a palantir exec says
00:13:30that the person who fails at this job is the careful engineer who wants code that still holds up in 10
00:13:36years the job is getting something rough in front of a real user quickly which is basically what an
00:13:41agent like claude code hands you on the first pass anyway so now that you know what an fte does and
00:13:47how the work actually gets done let's look at what you go and do with it the place to start is the
00:13:51smallest business you can physically walk into so wherever you work or somewhere a friend runs then you ask
00:13:57whoever runs it if you can spend an hour sitting next to whoever does the most repetitive job in
00:14:02the place while they're actually doing it and then you just run the five steps on what you saw what
00:14:07comes out the other end is called an audit and it's a real thing businesses actually pay for moza who
00:14:12runs those ai projects inside big companies starts every single one of them with an audit before anything
00:14:18gets built and he says that the first phase is the biggest bottleneck in the whole job and that's what
00:14:23you put in front of people whether you're applying for an fte job or pitching a business directly
00:14:28because inside it you've got one real company's process written down the steps you'd hand over to
00:14:32ai and what each of those is worth to them in money now we have curated roadmaps and guides for those
00:14:38who want to dive deeper into the role of fte which can be found in ai labs pro which is our community so
00:14:44if you've found value in what we do and want to support the channel this is the best way to do it the
00:14:49links in the description that brings us to the end of this video if you'd like to support the channel
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00:14:59always thank you for watching and i'll see you in the next one