The Rise of the Technology Services Industry: From Background Support to the “Super Engine” of the AI Era
Hello everyone, I’m your financial journalist. Today, we’re going to talk about an industry that often goes unnoticed but is undergoing tremendous changes—the technology services industry.
When we think of technology, we usually picture chip manufacturers, codewriters, and smartphone developers. However, there’s a group of “invisible” individuals who don’t directly create products; instead, they help others with experiments, data analysis, and research and development (R&D) outsourcing. In the past, they were often jokingly referred to as the “backstage support” or “intermediaries” of the tech world.
But things have changed. With the explosion of artificial intelligence (AI), especially “AI for Science” (AI-powered scientific research), this once low-profile industry is becoming the “heart” and “brain” of the entire innovation ecosystem. Shanghai is at the center of this transformation.
Below, I’ll break down this news into five key points in plain language to help you understand what’s really happening.
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1. A Complete Reversal of Roles: From “Casual Workers” to “Cost-Saving Experts”
Core Logic: The value of the technology services industry lies not in how much money it earns, but in how much it helps others save and how much time it speeds up their processes.
In the past, the technology services industry was seen as simply handling high-tech certifications or taking on outsourcing tasks, somewhat like intermediaries or “nannies” in the tech world. This view is too simplistic.
Today, especially with the integration of AI, its core value is to reduce the overall cost of innovation for society. For example, if you wanted to develop a new car, you’d have to buy expensive testing equipment and hire top engineers, spending hundreds of millions on trial and error. Now, you can use shared laboratories and AI algorithms provided by technology services companies, spending a fraction of the cost and time to complete the same tests.
The news mentions that from January to July 2026, the revenue of such companies in Shanghai increased by 15.4%. But this is just the surface. The deeper value lies in the fact that it has made advanced R&D, once only accessible to giants, available as standard services for small and medium-sized enterprises (SMEs). This is why it’s called the “foundation system”—a stable base that enables innovation to flourish.
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2. The Magic of AI4S: Saying Goodbye to “Edison-style” Luck
Core Logic: AI doesn’t just make calculations faster; it makes R&D less reliant on luck and more on logic.
For the past 30 years, pharmaceutical and material R&D has been like playing the lottery. Scientists had to synthesize tens of thousands of compounds, test them, and discard those with low activity before starting over. This was known as “Edison-style” trial and error, which was costly, time-consuming, and inefficient.
Now, “AI for Science” (AI4S) has changed this. AI can optimize multiple parameters simultaneously based on vast amounts of data. It can not only identify potential drugs but also generate new molecular structures and even target molecules that were previously thought impossible to develop into drugs.
Case Study: Insilico Medicine
This company used an AI platform to develop a new drug for pulmonary fibrosis, from identifying the target to selecting a preclinical candidate compound in just 18 months for a few million dollars. In contrast, the traditional approach would have taken over four years and cost tens of millions of dollars.
Significance: This is more than just speed improvement; it reduces the “marginal cost” of drug development by several orders of magnitude. It’s about shifting from guessing to precise calculation.
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3. The “Dry and Wet” Closed Loop: AI Makes Accurate Predictions, but Machines Are Needed for Execution
Core Logic: Having AI algorithms (for theoretical experiments) is not enough; real robots are also needed in the lab to carry out the experiments.
Many people think that once AI generates a molecular structure, the drug is ready. Wrong! The molecular structure is just a digital code that must be verified through actual chemical experiments. If the verification process remains manual, efficiency bottlenecks persist.
Enter companies like CrystalTech, which have created “AI-powered laboratories”:
1. AI Brain: Plans the experimental procedures and determines the amount of reagents used.
2. Robotic Hands and Feet: Logistics robots move around, and robotic arms perform precise measurements and dispensing.
3. Data Feedback: Experimental results are immediately fed back to the AI, which adjusts the next steps.
Benefits:
- Standardization: Robots are free from human errors and fatigue, providing cleaner and more reliable data.
- High Throughput: While a single lab technician used to do a few experiments a day, a robot cluster can handle hundreds.
- Scientists Liberated: Scientists can focus on formulating hypotheses and making decisions, rather than spending time on mundane tasks.
CrystalTech’s AI4S-related revenue soared by 136% in the first half of 2026, indicating that the market values this “AI + robotics” model greatly.
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4. The Hidden Multiplier Effect: Don’t Just Look at Revenue—Look at the “Two Sets of Accounts”
Core Logic: To evaluate the technology services industry, we need to consider not only its own earnings but also the additional value it creates for its clients.
This is the most profound insight of the article. The value of the technology services industry is reflected in two aspects:
1. Explicit Revenue: The income and profits of the service companies themselves, such as CrystalTech and Horizon Biotech.
2. Implicit Value: How much money clients save on R&D and how much time it takes them to get products to market, as well as the increased success rates.
Case Study: Horizon Biotech (a CXO representative)
Horizon Biotech specializes in the “engineering transformation” of genes and cells, turning lab samples into market-ready products. This process is highly complex and requires stability. They used AI to develop various intelligent systems that handle everything from document translation and report writing to process development.
Value: They not only save clients money but also reduce risks. If the process is unstable, the drugs may fail to be produced or may be substandard. By improving operational efficiency through AI, the technology services industry contributes to the overall stability of the industry chain.
Therefore, policymakers and investors should look beyond just revenue. They should ask:
- Can SMEs afford the high-end testing they couldn’t before?
- Are innovation outcomes being realized more quickly?
- Has the amount of unnecessary trial and error in the industry decreased?
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5. Shanghai’s Strategic Ambition: Building a Global Innovation “Operating System”
Core Logic: Shanghai is not just developing an industry; it’s building an efficient innovation ecosystem.
Why does Shanghai place such importance on the technology services industry? It has unique resources:
- Diverse Use Cases: Top hospitals, research institutions, and headquarters of multinational pharmaceutical companies.
- Clear Goals: By 2028, Shanghai aims to have over 3,000 technology services companies with a revenue of 750 billion yuan.
Shanghai’s strategy is systematic:
- Front End: Using AI4S to accelerate scientific discoveries (like with Insilico Medicine).
- Middle End: Using automated laboratories to speed up verification (like with CrystalTech).
- Back End: Using intelligent CXO services to accelerate engineering transformation (like with Horizon Biotech).
Together, these components form a complete “innovation pipeline.” Shanghai is aiming to create the world’s most affordable, fastest, and reliable “operating system” for innovation. Any innovative company that connects to this system can achieve rapid growth from scratch.
In summary: The technology services industry is no longer just a supporting role in technology; it’s the “amplifier” and “accelerator” of new productivity in the AI era. Through AI and automation, it makes the once expensive, slow, and risky innovation process standardized, cost-effective, and efficient. For consumers, this means that new drugs, materials, and technologies will be developed faster and at lower costs. For investors and policymakers, this is a sector that has been underestimated but is now poised for a significant revaluation.