Reborn in 2010, the AI ​​in my mind is too powerful.

Martin shook his head: "It's not just that. I'm hoping you can give us some support to help me swallow up the bigger fish and take over Halliburton!"

After hearing Martin's words, Chen Yiran remained silent for two seconds.

"Help Baker Hughes take over Halliburton? That's not a task that a simple robotic dog can accomplish."

Hannah stood up, nodded to Chen Yiran, and walked out first: "You guys talk first, I'll go check on Xiao Ping'an."

After Martin Craighead and his niece left, he explained to Chen Yiran, "I'm not just looking for support by connecting with Hannah and your past. I think this is actually beneficial to the Aurora Group as well."

Robotic dogs are good, I don't deny that. But for a pipeline inspection business alone, the patrols of a single robotic dog are not enough to carry such a significant strategic weight.

Let me first talk to you about the petroleum services industry. To put it simply, we provide technical support to oil companies. We are a super technology supplier with our own petroleum engineering technology. We have mastered core processes such as drilling, logging, fracturing, well completion, and reservoir management.

This is also a result of industry evolution. In the beginning, oil companies such as ExxonMobil, Shell, and BP, which are oil and gas operators, did not directly release these profits.

Initially, they held onto their oil field operations, essentially hoping to seize all the profits. However, after the industry developed for a while, they realized that this was not realistic.

The differences between oil fields are also enormous, ranging from shallow land layers, ultra-deep wells in deserts, thousands of meters underwater in the deep sea, shale, heavy oil, and even permafrost.

The differences between various oil fields are so vast that if oil companies try to cover everything, it will inevitably lead to the dispersion of technology, and the technology developed will ultimately have a very low utilization rate.

So ultimately, it evolved into specialized technology companies like ours. Our investment is more focused, and we have conducted more in-depth research on drilling, completion, and production technologies.

We can reduce the cost per well by aggregating the needs of multiple customers, smoothing the utilization rate of expensive equipment.

Globally, there are only three companies that can become giants. Schlumberger is the recognized leader, strong in well logging, seismic interpretation, and reservoir characterization, and has the most comprehensive software suite. It sets the industry benchmark.

Halliburton excels in well completion, fracturing, and cementing, as well as the ongoing shale revolution in North America, with horizontal wells and staged fracturing being its specialty.

Baker Hughes excels in drill bits, downhole drilling tools, and equipment such as gas turbines and compressors. Pipeline inspection is also our specialty, and the "intelligent pig" was first created by our own people.

Each of the three companies has its own competitive advantage, but I think that adding AI would definitely have a different effect.

I don't understand AI or deep learning, but based on my experience using Doubao and Claude, I can at least find quite a few points that AI can upgrade.

In the seismic interpretation phase, an engineer might spend two or three months meticulously studying a geological profile, and the resulting structural lines would vary from person to person. Delegating this task to a computer, allowing the algorithm to automatically determine the geological structure of the entire block, I believe can save a significant amount of time.

In the drilling process, well leakage and stuck drill bit issues are currently judged primarily based on field experience. Problems are often only discovered after an accident, and a single mis-drilled well can result in losses of millions. If computers could monitor downhole data in real time, indicating the extent of drill bit wear and which formations are prone to leakage, early warnings could be provided, leading to more optimized drilling cycles and a reduction in accidents.

In well workover, pulling up and down the sucker rod is a typical high-intensity, repetitive operation, requiring four to five people to monitor each wellhead. By replacing humans with machines to perform this type of fixed procedure, the number of on-site personnel can be reduced by more than half, thus improving efficiency and reducing the risk of fatigue-related errors.

In the production process, the inspection, maintenance, and pressure records of a well over the past twenty years are currently monitored and adjusted by humans, which inevitably leads to oversights. Managing this historical data using computers would allow one person to manage several times more wells, and anomalies would be detected much earlier.

In pipeline and safety aspects, current pipeline inspections rely on automated systems to haul equipment around and then human analysis of the data curves, while safety checks depend on manual patrols. However, equipment operation data is constantly being generated. If computers could detect abnormalities early on, even minor leaks could be detected immediately, eliminating the need to wait until a disaster occurs before taking action.

In the fracturing process, parameters are currently designed based on experience. Switching to data-driven closed-loop control would reduce the construction cycle from forty days to around thirty days.

Therefore, I hope that Aurora Group can cooperate with us and provide us with certain technical support. We can jointly develop an oilfield service platform.

"How are the profits calculated?"

"It depends on your requirements. Whether it's an annual payment or we could form a joint venture, we can accept either."

After thinking for a while, Chen Yiran said, "Let's forget about joint ventures; it's too much trouble. We have limited energy, and it's impossible for us to set up a company in every industry."

Let's go with a revenue-sharing model. We'll provide you with technical support to help you with your data-driven and AI-based transformation, and offer long-term technical updates. In return, you'll share a portion of the profits from this platform with us.

Of course, there are some additional conditions. To better understand your industry in the future, we need you to provide us with anonymized data from various aspects.

This clause needs to be written into the contract: your data cannot be disclosed to anyone else; from now on, this data can only be given to us.

Martin didn't hesitate: "Of course, I can even promise you that as long as we successfully acquire Halliburton, our data will only flow to Aurora Group and will absolutely not flow to Google."

Chen Yiran laughed: "I like hearing that. With our technical support, is that enough for you to convince the shareholders to take action against Halliburton?"

"That's about it, but I believe the other oilfield service companies will also try their hand at the technology once they notice our movements. Although without the Aurora's help, they probably won't be as fast."

But to give us a first-mover advantage, could you perhaps give us some preferential treatment in terms of the chip? I know that to do this, we need Aurora's chip, right?

Chen Yiran then began to explain to Martin: "That's not what I meant. Even if your technology is upgraded, you don't need to buy chips. Building a data center is very troublesome. I suggest you directly rent our Aurora data center. You probably wouldn't know how to do it yourself."

You only need to deploy a small number of all-in-one machines in some core areas; the number won't be too large.

We certainly can't stop selling chips to other companies; that would easily lead to antitrust investigations. However, strictly speaking, our chip production has always been insufficient, so prioritizing supply to our own data centers is reasonable, isn't it?

"That would be even better, since we don't understand this area anyway."

After they had finished talking, the two stood up and walked out. As they walked, Martin boasted about the collaboration: "Believe me, Aurora Group has definitely made a fortune in this collaboration."

The oil industry is a massive sector with numerous sub-sectors. I believe many industries will utilize AI in the future, and once Aurora establishes a foothold in the oilfield services industry, it will easily expand into other sectors.

"I know, that's why I'm willing to cooperate with you."

After Hannah and Martin said goodbye, Chen Yiran was alone in his study, organizing data from the various wafer fabs under his company.

He's increasingly realizing that there's a shortage of production capacity. Once AI starts to be deployed, the demand for chips will be virtually endless. He needs to quickly integrate TSMC into Aurora's production line.