Energy Crue
Welcome to 'Energy Crüe', the podcast that dives deep into the heart of industry innovation, entrepreneurship, and personal growth. I'm your host, JP Warren, and each episode, we embark on a journey to uncover the passions and motivations that fuel industry leaders as well as industry trends. We're not just talking business here; we're exploring the personal drives, the triumphs, and the challenges that shape today's pioneers.
Energy Crue
From the Table: Leveraging Emerging Technology to Increase Profitability
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Leveraging Emerging Technology to Increase Profitability
What happens when you put a group of seasoned oilfield operators in a room and ask them how they actually feel about the Generative AI revolution? In this exclusive, behind-the-scenes look at a recent Crue Club roundtable discussion, we strip away the Silicon Valley hype to reveal the gritty reality of deploying technology in the dirt.
While tech executives talk about the "speed of light" advancements of Gen AI, oil and gas operators frequently feel "two steps behind". But in an industry where being 90% accurate can mean a catastrophic financial or operational failure, skepticism isn't just a preference—it’s a risk-management strategy. Tune in as we discuss why operators refuse to put AI on "autopilot", where the real efficiency gains are currently happening (from driverless sand trucks in West Texas to auto-frac fleets), and how desktop tools are quietly saving hours of administrative time
Why This Roundtable Matters
SPEAKER_02Welcome to Energy Crew Podcasts with me, JP Warren. Pull up the seats, because you were about to join a very important conversation that Dugan Hughes, who's a senior planet engineer over at Eventive, led a crew club operator roundtable last fall in 2025. And what this is, pretty much we bring in operators around a room, no solicitations, and we get around the table and we have some candid, strategic, insightful conversation on current challenges, bottlenecks, and opportunities facing the oil and gas industry today. And Dugan brought such a cool topic to the table. And I he crushed the roundtable. He was very good and excellent at facilitating this conversation, bringing insights, experience from various different operators around the table. And we discussed how do you leverage current technology to increase your profitability. And now, listening, this kind of, I'm gonna go high level what this episode is uncovering. We're talking about trusting the equation and the digital twin. We're talking about physics versus pure AI. We're talking about the threat of the industry data silo. How do we get the data silos? And then we're talking about how the fast follower workforce will be of the future. And these crew club roundtables is a way for operators to come together to get together and to exchange ideas, knowledge, and experience so they can take it back to their team, so they can improve themselves and all without solicitations. And I love the topic Dugan brought to the table. One of the sharpest guys out there love this cat, and I really do appreciate the topic he picked and the insight around the table. So hope you enjoy this Energy Energy Crew podcast. If you want to learn more about Crew Club, head over to Crew Club.com. That's C-R-U-E-Club.com. And what we did with this um uh episode, we took the transcript from that round table, we shoved it into uh some uh some AI, and we spit out a great of uh kind of a digestible um 20 minutes or less insights from the table. And so sit back, uh, sit back, we'll pull up the seat, sit back, and all that fun stuff, and we hope you
Automation Already Hitting The Field
SPEAKER_02enjoy.
SPEAKER_01Picture this. You're uh you're standing out in the swirling dust of West Texas.
SPEAKER_03Oh man, it gets hot out there.
SPEAKER_01Right. The Permian Basin sun is just beating down on you, and this massive, heavy-duty sand truck rumbles past. But as you look up at the cab, the hair on your arms stands up because there's absolutely no one behind the wheel. None at all. Yeah. We are talking fully autonomous delivery operating in, you know, one of the most rugged, chaotic, high-stakes industrial environments on the planet. And the thing is, the automation doesn't stop at the road.
SPEAKER_03No, it really doesn't.
SPEAKER_01Right now, automated frac fleets are seeing this jaw-dropping 17% jump in operational efficiency.
SPEAKER_03Which is huge.
SPEAKER_01It's massive. And out in the freezing waters of the North Sea, offshore decommissioning costs have just been slashed by like 35%. I mean, that is saving millions upon millions of dollars.
SPEAKER_00Yeah.
SPEAKER_01And these are not wild 10-year projections buried in some glossy corporate brochure. This is happening out in the mud and the dirt of the oil field today.
SPEAKER_03It's just, it represents a massive operational paradigm shift. And, you know, if you're sitting there wondering how you missed the memo or how your own operation is supposed to keep up with that kind of technological whiplash without breaking something critical, well, you are exactly who this deep dive is for.
SPEAKER_01Exactly. Because today, you are getting exclusive access to a conversation that is usually heavily guarded behind closed doors. We uh we got our hands on the raw, unfiltered notes from a highly exclusive crew club operator roundtable.
SPEAKER_03Aaron Powell Yeah, this was not your average networking event.
SPEAKER_01Not at all. We are talking about a room packed with senior oil and gas operators, technology leaders, the heavy hitters who actually sign the checks and carry the risk.
SPEAKER_03Aaron Powell The ones making the actual calls.
SPEAKER_01Right. And our mission today is to slice right through the Silicon Valley corporate jargon, toss the theoretical fluff out the window, and uncover how the energy sector is actually grappling with artificial intelligence on the ground. So, okay, let's unpack this. Because reading through the notes from this round table, the mood in that room wasn't exactly a victory lap,
Why Leaders Feel Two Steps Behind
SPEAKER_01was it?
SPEAKER_03Aaron Powell Oh, not by a long shot. I mean, the overriding emotion. And you have to keep in mind, these are veteran executives, right? Hardened engineers admitting this out loud. Yeah. The feeling was a deep, pervasive sense of intimidation. Like the phrase that kept echoing around the table was feeling two steps behind.
SPEAKER_00Wow.
SPEAKER_03Which, yeah, it's a fascinating admission from leaders of an industry that practically powers the modern world.
SPEAKER_01Aaron Powell Which is wild to me. I mean, this is an industry that routinely drills lateral wells miles into the Earth's crust, right? They manage explosive downhole pressures. They operate multi-billion dollar platforms in deep oceans.
SPEAKER_03Yeah, the physics involved are insane.
SPEAKER_01Aaron Powell So lines of software code are what's making them sweat. I have to push back a little on this premise. Are these operators just getting overwhelmed by tech bros throwing around jargon like neural networks? Or is the technology genuinely moving too fast for a heavy industrial sector to safely integrate?
SPEAKER_03It is definitely leaning heavily toward the technology moving too fast for their traditional safety protocols.
Traditional AI Versus Generative AI
SPEAKER_03Yeah, I mean, to understand the anxiety in that room, we have to look at the fundamental difference between the traditional AI the industry has used for years and the new era of generative AI.
SPEAKER_00Right.
SPEAKER_03For a long time, the oil field has used traditional AI and machine learning. That type of technology is highly analytical. You know, it looks at millions of data points to perform predictive maintenance.
SPEAKER_01Aaron Powell So what does that actually look like out in the field then?
SPEAKER_03Aaron Powell Well, instead of a human walking up to a pump every day to check a pressure cage, traditional AI correlates data in the background.
SPEAKER_01Oh, I see.
SPEAKER_03Yeah, it notices that like a slight temperature drop at two in the morning combined with a microscopic vibration spike at four in the morning means a specific valve is going to fail by noon.
SPEAKER_01Aaron Powell So it's math.
SPEAKER_03Exactly. It's math. It's physics based, it's predictable. Generative AI, however, is entirely different.
SPEAKER_01Aaron Powell That is the uh conversational prompt-based technology, right? How is the industry utilizing that?
SPEAKER_03Well, one technology leader in the room gave this stunning example of how fast Gen AI is moving. He pointed out that today, a service company rep can take a web link to a SuperMajor's hour-long quarterly earnings call.
SPEAKER_00Okay.
SPEAKER_03They grab another link to their own company's technical product catalog. They drop both links into a Gen AI prompt and just type act like a seasoned oil and gas CFO.
SPEAKER_01Seriously?
SPEAKER_03Yeah. Write a highly tailored value proposition connecting our specific software to the exact drilling pain points the CEO mentioned in this earnings call.
SPEAKER_01Wait, how long does a hyperspecific strategic email like that actually take to generate?
SPEAKER_03About three seconds.
SPEAKER_01Three seconds. I mean, that is easily a whole morning's worth of research, synthesis, and drafting for a senior sales director. Well, easily. It's like it is like they spent decades perfecting how to drive a train on a fixed track, highly analytical, you know exactly where it's going, and suddenly someone just dropped them in an off-road buggy.
SPEAKER_03That is a perfect way to frame it.
SPEAKER_01Right. Like it can go absolutely anywhere at top speed, but you can also drive it right off a cliff if you don't know what you're doing.
SPEAKER_03Yeah, because operators are used to building physical infrastructure. They build with steel, concrete pipelines. They're not software developers.
SPEAKER_00No, right.
SPEAKER_03And if we connect this to the bigger picture, the entire industry right now is in this chaotic figuring it out together phase. Multiple people in the room compared it directly to the late 90s dot com boom.
SPEAKER_00Oh, wow, the Wild West.
SPEAKER_03Totally. Nobody has the perfect, universally accepted textbook on how to deploy generative AI yet. And that unknown is terrifying when you are managing billions of dollars of volatile physical assets.
SPEAKER_01But that brings up an incredible contradiction. Because if these companies feel like they are driving an off-road buggy near a cliff and they are that intimidated by the speed of the shift, the natural corporate reaction should be to pump the brakes.
SPEAKER_03You think so, yeah.
SPEAKER_01Lock it down until it's safe. But they aren't doing that. They are pouring massive amounts of capital into this space. Why are they accelerating into the unknown?
SPEAKER_03Because
ROI And Operate By Exception
SPEAKER_03the operational return on investment is undeniable.
SPEAKER_00Oh, really?
SPEAKER_03Oh, yeah. At the round table, it was noted that one major service company recently attributed a full 10% increase in their total revenue strictly to the implementation of their AI and cloud-based platforms.
SPEAKER_0110%.
SPEAKER_03Yeah, that is a staggering financial figure for a massive publicly traded enterprise. You just can't ignore that kind of growth without your shareholders demanding answers.
SPEAKER_01And I imagine it is fundamentally altering the physical day-to-day reality of the workers out in the dirt.
SPEAKER_03Oh, absolutely.
SPEAKER_01There was a huge discussion in the notes around this concept of operate by exception. Break down how that mechanism actually changes the life of, say, a pumper or a field operator.
SPEAKER_03Aaron Powell Sure. So traditionally a field operator has a physical route, right? They wake up, get in their truck, and drive to site A, then site B, then site C.
SPEAKER_01The daily routine.
SPEAKER_03Exactly. They visually check gauges, look for leaks, listen to the machinery. It is a fixed geographical analog loop. AI completely dismantles that model. By pulling scatter and telemetry data from all these well sites 24-7, the AI system constantly looks for deviations from the norm.
SPEAKER_01So they don't have to check everything manually.
SPEAKER_03Right. When the field operator wakes up, the AI tells them, skip sites A, B, and C entirely today. They're running perfectly. Drive immediately to site D because the telemetry indicates a high priority anomaly that will cost us production by three o'clock this afternoon.
SPEAKER_01So they are only deploying human bandwidth where the algorithm detects an exception to the normal operation.
SPEAKER_00Exactly.
SPEAKER_01That is incredibly efficient. But here's where it gets really interesting. If AI is perfectly optimizing the physical routes, and if it's successfully automating frack fleets and running driverless sand trucks in West Texas, do human jobs
AI Does Laundry Not Art
SPEAKER_01start disappearing tomorrow? Like does the role of a mudlogger or a directional driller just become obsolete?
SPEAKER_03That anxiety was a pivotal point of discussion at the round table. But the consensus from the operators yielded a really sharp clarifying insight. The goal of AI in the oil field right now is not replacement, it is elevation. The leaders in the room made a brilliant distinction. They said, we want AI to do our laundry, not our art.
SPEAKER_01Aaron Powell Okay, I really like that framing. The laundry and the art. Let's dig into the mechanics of that. What exactly is the laundry?
SPEAKER_03The laundry represents the tedious, highly repetitive tasks that drain a brilliant engineer's day.
SPEAKER_01Like paperwork.
SPEAKER_03Yeah, answering standard internal emails, pulling standard operating procedures from a database. It is digging through a 500-page technical manual to find one specific torque tolerance, or just staring at massive spreadsheets trying to spot a basic numerical pattern.
SPEAKER_01Right, the busy work.
SPEAKER_03Yeah. Those tasks are rules-based. AI can do the laundry infinitely faster and more accurately than a human ever could.
SPEAKER_01Which leaves the human completely free to do the art.
SPEAKER_03Exactly that. The art is the out-of-the-box creative problem solving. It is the mad scientist engineering that pushes the industry into new frontiers.
SPEAKER_01Aaron Powell Okay, give me an example.
SPEAKER_03Aaron Powell Like designing a completely unprecedented piece of downhole equipment or navigating a bizarre geological challenge that no one has ever drilled through before.
SPEAKER_01Stuff that requires real intuition.
SPEAKER_03Right. Because AI fundamentally cannot innovate in areas where no historical data exists. If a technique hasn't been done before, there is no data set to train the model on.
SPEAKER_01Oh, that makes sense.
SPEAKER_03Yeah. The AI is blind in that scenario. It requires human ingenuity to break new ground.
SPEAKER_01Aaron Powell So the AI handles the 101 level physics and the data sorting, and the human actually gets to be an engineer again, rather than just a glorified data entry clerk.
SPEAKER_03Exactly.
SPEAKER_01That sounds fantastic. But look, if AI is so incredibly good at doing the laundry and it's already optimizing these complex logistical routes, why isn't it just running the whole operation on autocilot right now? Why is there still a human in the loop
The Trust Gap And Black Box Risk
SPEAKER_01at all?
SPEAKER_03It comes down to a massive industry-wide lack of trust. The technology might be capable, but the operators flatly refuse to hand over the keys.
SPEAKER_01And from the notes, this sparked a pretty sharp disagreement in the room between the tech developers from Silicon Valley and the actual oil and gas operators who work in the field.
SPEAKER_03It was a fascinating clash of cultures, honestly. You had tech leaders arguing that modern large language models are so incredibly advanced that they can instantly read and digitize decades-old handwritten cursive well logs.
SPEAKER_00Okay, yeah.
SPEAKER_03The tech developers argue that their AI has been trained on the entire corpus of human writing. So translating a little cursive on a scratched piece of paper from the 1970s is trivial to them.
SPEAKER_01Right. They think it's a solved problem.
SPEAKER_03Aaron Powell Yeah, they claim it can pull that historical data and instantly start optimizing your future drilling programs.
SPEAKER_01But the operators threw cold water on that immediately. Why?
SPEAKER_03Because the operators are the ones carrying the catastrophic financial risk.
SPEAKER_00Ah.
SPEAKER_03They pointed out that oil and gas language is incredibly niche. A 1970s well log from Oklahoma uses localized shorthand.
SPEAKER_01Oh, like abbreviations.
SPEAKER_03Aaron Ross Powell Exactly. An S could mean sand or shale or specific tool. If there's a smudge on the paper and the AI hallucinates that a four is a nine, it might think the casing shoe is set at 9,000 feet instead of 4,000 feet. And if you base your drilling mud weight on a hallucination like that, you fracture the rock, you lose all your drilling fluid and potentially cause a blowout.
SPEAKER_00Oh man.
SPEAKER_03Yeah. In high-stakes environments, if an AI miscalculates the pressure and screens out a well-meaning handsack, it accidentally packs the wellbore completely solid with frack sand, choking off production entirely. You have just burned millions of dollars and potentially ruined the asset.
SPEAKER_01Aaron Powell So they just don't trust it with the final say.
SPEAKER_03Exactly. Operators want the AI to suggest actions. They want it to flag anomalies, but they draw a hard, non-negotiable line at letting it make the final unreviewed decision.
SPEAKER_01Aaron Powell Wait, I have to call out the double standard here. Because humans make catastrophic mistakes all the time.
SPEAKER_03Aaron Powell They do.
SPEAKER_01One operator in the room even pointed out that you might have a human engineer who screws up and screens out 30% of the time. And companies will often just let them keep running things into the ground because management doesn't want to admit they made a bad hire.
SPEAKER_03Aaron Powell It happens more than you'd think.
SPEAKER_01Aaron Powell So why is a human allowed a 30% failure rate? But AI is held to a standard of absolute 100% perfection before anyone trusts it to do the job.
SPEAKER_03It is a profound double standard, and it all boils down to the black box nature of artificial intelligence.
SPEAKER_01Aaron Powell Meaning what exactly?
SPEAKER_03Well, if a human engineer makes a critical error, you can pull them into an office, sit them down, and ask them to explain their logic. Right. You can understand their thought process, and then you can either retrain them or fire them. With an AI, you are dealing with a computer moving at the speed of light, doing exactly what its complex neural network decided was optimal.
SPEAKER_01And you can't just ask it what it was thinking.
SPEAKER_03Right. If it makes a disastrous leap in logic, you cannot easily unpack why it made that decision.
SPEAKER_01So if operators demand absolute perfection, but AI is inherently a black box, how do you ever bridge that trust gap? Like how do you prove the AI is safe?
Digital Twins As The Proving Ground
SPEAKER_03Aaron Powell The primary solution proposed in the room was the concept of digital twins. Operators stated they will only begin to trust an AI if it is integrated into a digital twin environment first.
SPEAKER_01What does setting up a digital twin actually require from the operator?
SPEAKER_03Aaron Powell It basically means running a shadow operation. The AI is fed the exact same live telemetry that the real human crew is looking at.
SPEAKER_00Okay.
SPEAKER_03But the AI runs completely in the background. It's making its own hidden decisions alongside the humans without actually controlling any physical machinery.
SPEAKER_01Aaron Powell Oh, I see. It's like a simulation running in parallel.
SPEAKER_03Aaron Powell Exactly. And it has to do this for months. Only when an operator can look back over a six-month period and definitively prove that the AI's background decisions outperform the best human engineers in real time. Every single time will they consider handing over the autopilot keys?
SPEAKER_01Aaron Powell But setting up a shadow operation like that requires an ocean of historical and real-time data.
SPEAKER_03Aaron Powell It does.
SPEAKER_01If you don't feed the model enough data, it can't learn how the basin behaves. Doesn't that hit a massive roadblock in an industry known for keeping it secrets?
SPEAKER_03Oh, that is the data starvation problem, and it is a massive structural flaw in the industry's
Data Silos Create Data Starvation
SPEAKER_03plan.
SPEAKER_01Because they don't share.
SPEAKER_03Oh, exactly. Oil and gas companies are notoriously siloed. An operator in the Permian might have well right next to a competitor, but they treat their subsurface data like the nuclear launch codes because they are competing for leases. Right. Furthermore, even within the same company, the drilling department might use entirely different software systems than the production department. And those systems don't speak to each other.
SPEAKER_01Aaron Powell So they are effectively starving the exact technology they are demanding perfection from.
SPEAKER_03Yeah, pretty much.
SPEAKER_01It is a total catch 22. If companies are terrified to feed their own data into these models and they are demanding six-month shadow operations, how are they supposed to get any value out of AI today without taking on massive
Stop Waiting For Perfect AI
SPEAKER_01risk?
SPEAKER_03This leads to one of the most critical, actionable takeaways from the roundtable, the overlooking good-for-great syndrome.
SPEAKER_01Okay, what is that?
SPEAKER_03Operators are so hyper focused on waiting for this flawless, all-encompassing sci-fi AI platform to magically run their entire production facility that they are completely ignoring simple, highly effective tools that are commercially available to them right now.
SPEAKER_01Ah, they are looking for the holy grail and walking right past immediate operational upgrades.
SPEAKER_03Exactly.
SPEAKER_01There is a great example of this regarding how companies handle vendor calls and digital meetings, right?
SPEAKER_03Oh yeah. Think about the friction of a 45-minute digital negotiation over casing prices. Normally, an engineering manager has to listen to the entire recording to figure out what was agreed upon.
SPEAKER_01Which takes forever.
SPEAKER_03Right. But right now, there are AI meeting trackers that don't just transcribe the call, they analyze context. Oh, wow. Yeah, you can set a tracker for specific keywords, like a competitor's name or the phrase 5% discount. The AI instantly pin drops the exact three moments those topics were discussed.
SPEAKER_01That's incredibly useful.
SPEAKER_03It is. You click the pin, you hear the tone, the context, the exact exchange. It turns five hours of tedious call review into 20 minutes of high-level strategy.
SPEAKER_01But because it isn't an AI that automatically drills a two-mile lateral well, people just dismiss it as not being real AI.
SPEAKER_03And that is a massive blind spot. The takeaway for tech developers and service companies
Adoption Culture And A Hard Mandate
SPEAKER_03is stark. You are building in a bubble.
SPEAKER_00Yeah.
SPEAKER_03If you sit in Austin or Silicon Valley and build an AI platform for the oil field without integrating veteran oil and gas operational experts into your software design, operators will never adopt it.
SPEAKER_00They just won't trust it.
SPEAKER_03Right. You have to speak their specific operational language, you have to understand their physical risks, not just write elegant Python code.
SPEAKER_01Aaron Powell And on the leadership side, there was a standout moment in the room, a story about a tech CEO's mandate that was absolutely brutal, but honestly, it might be exactly the wake-up call the industry needs.
SPEAKER_03Oh, that was intense.
SPEAKER_01Yeah.
SPEAKER_03This CEO realized that generative AI was the most significant technological shift since the invention of the internet. So he gave his entire workforce a strict mandate.
SPEAKER_01Which was.
SPEAKER_03He said, you have a few weeks to figure out exactly how generative AI can improve your specific daily job and make you more efficient.
SPEAKER_00Okay.
SPEAKER_03If you figure it out and integrate it, you get a 20% bonus. Wow. If you ignore it, you will be part of a 10% workforce layoff.
SPEAKER_01That is intense. I have to wonder though, doesn't that just force people to use a tool they might not actually need? Like if someone's job is heavily physical, forcing them to use an LLM just creates digital busy work to justify the bonus. Right.
SPEAKER_03That was the initial fear, yeah. But it actually forced the workforce to look critically at their own laundry. Even the most physical jobs have safety reports, compliance documentation, and scheduling.
SPEAKER_00All true.
SPEAKER_03He essentially told them, look, I don't have the textbook for this either. We are learning together. But if you refuse to learn, you will be left behind.
SPEAKER_01And did he actually do it?
SPEAKER_03He followed through. He paid out the bonuses to the adapters, and he executed the layoffs for the holdouts. It proved to the room that passive curiosity isn't going to cut it anymore.
SPEAKER_01Man, so what does this all mean? It means the energy industry needs to stop waiting for a perfect technological savior. They need to start aggressively experimenting with the tools that exist today. Exactly. Take small bites out of the elephant, use the AI note takers to reclaim hours of your week. Use basic machine learning to flag your temperature anomalies.
SPEAKER_03Yeah. Build that muscle now.
SPEAKER_01Right. Build the culture of digital adoption right now. So when the massive operation-altering models do finally clear the digital twin proving grounds, your workforce isn't paralyzed by the learning curve.
The Real Revolution Is Connected Data
SPEAKER_03And that leads perfectly into the forward-looking conclusion from the room. Because as impressive as an automated frack fleet is, or you know, a driverless sand truck out in West Texas, those are still just examples of isolated automation.
SPEAKER_00Right.
SPEAKER_03The true revolution, the horizon that genuinely thrilled the leaders at the table is when all of these silo data streams finally merge.
SPEAKER_01Ah, the compounding effect.
SPEAKER_03Exactly. When your geological drilling data, your live production telemetry from the pumps, and your real-time financial market data all speak to each other instantaneously through an integrated AI model that changes everything. The speed of optimization will be unlike anything the heavy industrial world has ever seen. We are talking about identifying and solving complex, basin-white inefficiencies in a matter of minutes rather than months. That is the future that they are sprinting toward.
SPEAKER_01It is an incredible look behind the curtain of an industry in massive transition. And if you want to dive deeper into these kinds of exclusive closed-door operator insights or check out schedules for future discussions, you definitely need to head over to www.crewclub.com. There is so much more to this operational shift than we could ever fit into one deep dive.
SPEAKER_03Oh, absolutely. It is a landscape that is evolving by the week, not by the year.
SPEAKER_01But before we wrap up, I want to leave you with one final thought to mull over.
The Accountability Question Nobody Answers
SPEAKER_01We talked a lot today about the trust gap, right? And why operators are terrified to put AI on autopilot. They are running those shadow operations, guarding their data, waiting for the AI to prove it is mathematically perfect. But let's look down the road to when they finally do hand over the keys because the financial efficiencies will eventually be too great to ignore.
SPEAKER_03It is an inevitability.
SPEAKER_01So here's the scenario. The AI is now driving the heavy sand truck. It is steering the drill bit miles underground. It is operating the high pressure valves. And then the unprecedented happens. The AI encounters a bizarre physical variable it was never trained on, hallucinates a response, and makes a catastrophic multimillion dollar operational error in the field. Maybe it causes a massive blowout or permanently ruins a reservoir. My question to you is when the dust settles, who takes the fall? Is it the field engineer who trusted the model and didn't hit the manual override in time? Is it the C-suite executive who bought the software to chase a 10% efficiency gain? Or is it the programmer sitting in an air conditioned office a thousand miles away who wrote the code?
SPEAKER_03That is the ultimate accountability black hole we are hurtling toward.
SPEAKER_01Because until the industry can answer that specific question, that driverless sand truck out in West Texas is carrying a lot more than just sand. It is carrying the liability of an entire industry's future. Thanks for joining us on this deep dive. We'll see you next time.