In 2025, AI applications will be deeply integrated into our work and life, becoming indispensable “digital partners”. From AI assistants for programmers to digital humans for e-commerce live broadcasts, AI is reshaping every corner of the internet. This article will provide an in-depth analysis of how AI applications drive the evolution of the Internet from four dimensions: technology underlying, industrial ecology, business model, and career development.
Whether it is an intelligent AI assistant on call on a mobile phone or a creative artifact used for design on a computer, AI applications have already penetrated into every corner of work and life like air.
According to the latest data from the QuestMobile AI Industry Research Institute in March,Mobile native apps, mobile app plug-ins, and PC web appsOn the three types of AI application sides, the monthly active users have reached 591 million, 584 million, and 209 million respectively, and the number of applications in the three tracks accounts for the highest proportionAI comprehensive assistant, AI search engine, AI creation and designIts monthly active users reached 547 million, 338 million, and 111 million respectively – which means that one in every two Internet users is using AI tools.
For Internet practitioners, this is both an opportunity and a challenge. We must answer: How will the industry evolve when AI becomes a “symbiotic partner”? How should individuals reconstruct their competitiveness? The following will be followedTechnology underlying, industrial ecology, business model, career developmentFour dimensions to analyze this ongoing “industry revolution”.
1. Reconstruction of the underlying technology: from tools to “digital partners”
1. Evolution of large models: from “parameter competition” to “understanding people’s hearts”
Just half a year ago, the industry judged whether an AI model was powerful, and it was still looking at how many trillion parameters it had and how many data centers it needed. But now, the in-depth R1 model has already taught the industry a profound lesson – this “lightweight player” who does not blindly stack parameters, with its exquisitely optimized algorithm, allows mobile phones to run AI with complex reasoning capabilities smoothly, serving tens of millions of users every day.
B-end product manager’s ability model and learning improvement
The first challenge faced by B-end product managers is how to correctly analyze and diagnose business problems. This is also the most difficult part, product design knowledge is basically not helpful for this part of the work, if you want to do a good job in business analysis and diagnosis, you must have a solid …
View details >
The ancient poetry analysis function of Baidu Wenxin Yiyan is no longer limited to literal translation, but can analyze the poet’s emotional context in combination with the creative background; In medical scenarios, iFLYTEK Spark model can not only accurately interpret CT images, but also adjust communication strategies according to the emotional changes described by patients. Huawei Xiaoyi realizes “non-inductive service” by learning user habits for a long time: it automatically adjusts the screen color temperature and pushes customized content according to the changes in ambient light when the user reads at night.
Revelation:The essence of this change is the qualitative change in AI’s “cognitive ability”. In the past, AI could only process clear instructions, but now large models have begun to understand human emotions, context and potential needs, just like a child who grows up and learns to observe words and expressions.
2. Multimodal interaction: from “typing and speaking” to “full-sensory communication”
Do you remember the scene where you shouted “Hey Siri” into your phone to wake up your phone assistant? Today’s AI assistants have long “evolved”. Xiaomi’s “Super Little Love” supports “mixed input of graphics and text”, you take a picture of the menu, circle the dishes you want to eat, and it can help you place an order.
SenseTime’s visual model realizes the closed-loop interaction of “image-language-action”, where users take photos of clothing, and AI can not only recommend matching plans, but also demonstrate the effect of dressing through body movements; DJI’s intelligent obstacle avoidance system, combined with visual recognition and flight attitude data, realizes the seamless connection between environmental perception and operation instructions. This technological advancement has made it possible for “machines to adapt to humans” – users no longer need to learn specific interactive languages, but collaborate with AI through daily communication.
Revelation:The core of interactive innovation is shifting to “naturalization” and “non-inductiveness”. When AI can understand your gestures, understand your tone, and even understand your expressions, it can truly enter the world of users – future interactive innovation must shift from “adapting users to technology” to “adapting technology to users”.
3. Computing power architecture innovation: from “cloud dependence” to “device-cloud collaboration”
In the past, when we talked about AI computing power, we thought of huge data centers and servers that consume amazing power. But Huawei Xiaoyi has rewritten the rules – it integrates a large device-side model into the mobile phone, which can write code, process documents, and even process sensitive medical data locally and securely. This “device-cloud collaboration” model is like installing a “mini brain” on the mobile phone, which can not only respond quickly, but also protect privacy.
This architectural change has brought two key breakthroughs: first, “localized processing” allows AI to better understand “private needs”, so that when writing diaries and processing financial data, there is no need to worry about uploading information to the cloud; The second is that “device-cloud complementarity” makes complex tasks more efficient, simple operations are completed on the device side, and complex calculations are handed over to the cloud, just like the division of labor between the brain and the cerebellum.
Revelation:Computing power is no longer a money-burning game, but a “smart distribution”. Future AI applications need to “walk on two legs on the end and cloud” – the device side solves “immediacy” and “privacy”, and the cloud handles “complexity” and “openness”.
2. Industrial ecological reconstruction: from “traffic melee” to “ecological game”
1. The new battlefield of giants and start-ups: from “grabbing users” to “building an ecosystem”
Baidu’s AI application plug-ins cover 291 million users, Douyin’s AI search answers 200 million questions every day, and Internet giants use “traffic + plug-ins” to quickly enclose the ground – users can call various AI services in WeChat and Douyin without downloading new APPs, just like the outbreak of mini programs back then. However, hard technology start-ups have found another way: in-depth search to open source the R1 model, attracting global developers to use it to develop various applications, forming a “technology open source + commercial closed-loop” ecosystem, similar to the success path of the Android system.
The essence of this game is the struggle for “entrance control”. Giants rely on traffic advantages to “attract into the pool”, and start-ups rely on technical barriers to “build nests and attract phoenixes”. Just like the ecological dispute between Apple and Android, the entrance to the AI era may be a super APP, an underlying model, or a hardware terminal.
Revelation:Traffic is no longer the master key, ecology is the “moat”. Internet giants should learn to be open and win-win, and don’t always think about trapping users in their own “traffic pool”; Start-ups should find the right technical fulcrum and quickly expand their ecological influence through open source and cooperation.
2. The counterattack of mobile phone manufacturers: from “selling hardware” to “creating an ecosystem”
Mobile phone manufacturers, which were once suppressed by Internet giants, have recently counterattacked in the AI assistant track. Huawei Xiaoyi has 157 million monthly active users and OPPO Xiaobu has 148 million, surpassing the AI products of many Internet companies. The secret of this lies in the combination of “hardware + service”: as a natural “end-side entrance”, mobile phones are pre-installed with AI assistants from the factory, and can also open up smart speakers, watches, cars and other devices.
Xiaomi’s HyperConnect technology realizes the semantic interoperability of 2000+ smart devices, and users can trigger chain reactions such as turning off lights, energy saving of air conditioning, and car warm-up with a single sentence of “ready to leave home”. vivo’s “personal data space” stores user preferences on a local security chip to ensure that private data is not uploaded to the cloud and solves users’ core concerns. This transformation marks the upgrade of hardware from a “functional carrier” to an “ecological connector”.
Revelation:This counterattack marks a change in the logic of the industry, and hardware is no longer a “low-end coolie”, but an “ecological connector”. Just like when Apple redefined smartphones with the iPhone, now mobile phone manufacturers are redefining “smart terminals” with AI assistants.
3. The new role of policy and capital: from “scattering money subsidies” to “setting up a stage to sing”
Hangzhou’s “three 15%” policy has become a model for industrial upgrading: the government will form a joint force of 15% of fiscal revenue, 15% of enterprises’ R&D expenses, and 15% of social capital investment, focusing on hard technology fields such as AI chips and robots. This collaborative model of “government-industry-university-research fund” not only avoids capital bubbles, but also provides long-term support for core technology breakthroughs.
The wind direction of the capital market is also changing, Sequoia China and Tencent have continuously invested in Unitree Technology, focusing on its ten-year technology accumulation in the field of robot motion control; Shenzhen Venture Capital has set up a 10-billion-level hard technology fund to clearly focus on the research of AI basic algorithms. The “two-way precision” of policy and capital is reversing the speculative mentality of the Internet industry and cultivating the industrial soil of deep technology cultivation.
Revelation:Industrial upgrading requires “patient capital” and “precise policies”. The government should shift from “paternalistic management” to “platform builders”, capital should shift from “making quick money” to “accompanying runners”, and enterprises should seize this “policy dividend period” to convert short-term funds into long-term technology accumulation.
3. Business model reconstruction: from “traffic monetization” to “value symbiosis”
1. The rise of the subscription system: from “one-hammer trading” to “lifetime service”
iFLYTEK’s Spark model has created a new model for the medical industry: no longer selling individual AI diagnostic software, but providing hospitals with full-process medical technology cloud services through “hardware + subscription services”. County-level hospitals can use the top AI diagnostic system for very little money, and they can be continuously upgraded, covering 2,700 hospitals in five years, helping to diagnose 630,000 people every day, just like a “digital version of the family doctor”, charging an annual fee and providing continuous service.
Microsoft Copilot is more ingenious, embedding the AI assistant into the Office365 subscription service, and users can automatically generate copywriting, Excel intelligent analysis data, and PPT to generate charts with one click for more than a dozen dollars per month. In the past, selling software was a “one-time income”, but now it has become a “trickle”, the longer users use it, the more data accumulates, and the more AI understands users, forming a virtuous circle.
Revelation:Don’t focus on the user’s clicks, but at the user’s “pain points”. The core of the subscription system is “continuous delivery of value”, and only when users feel the value that is “inseparable” will they be willing to pay for long-term services.
2. Technical authorization: from “closed-door manufacturing” to “open and win-win”
In-depth search to open source the R1 model seems to be a loss, but in fact it is a big move: 20 million developers around the world use it to develop various applications, from poetry writing assistants to industrial quality inspection tools, forming a huge ecosystem. Deep search makes money through cloud service sharing, enterprise customization, etc., just like the Android system relies on open source to occupy the mobile phone market, and then makes money through services.
Step Leap Xingchen cooperated with the Interface Finance Association to launch the “Financial Leap Model”, which authorized financial analysis technology to more than 20 banks and securities firms, becoming an industry standard. In the past, technology companies always wanted to monopolize technology, but now they find that open technology licensing can make more money – when technology becomes the standard configuration of the industry, they have mastered the “pricing power” and “ecological dominance”.
Revelation:Technical barriers are not hidden, but good use of leverage. For hard technology enterprises, open source is not to give up interests, but to exchange “technology sharing” for “ecological co-construction”; For Internet start-ups, don’t think about developing from scratch, learn to “stand on the shoulders of giants”, and use technology authorization to quickly improve competitiveness.
3. Scene empowerment: from “single point breakthrough” to “ecological empowerment”
90% of domestic home decoration companies rely on its design system, and in turn promote the intelligent transformation of customized home furnishing production lines, forming a digital closed loop of the whole chain of “design-production-delivery”. This transformation makes a single tool the engine of industrial upgrading.
Alibaba’s “city brain” is no longer limited to traffic management, but builds a city-level intelligent system covering government affairs, people’s livelihood and industry, which improves traffic efficiency by 15% in the pilot area of Hangzhou and drives the growth of related industries such as intelligent hardware and data services. Tencent’s “smart retail” solution reconstructs the relationship between people and goods through AI, helping chain supermarkets achieve a 20% increase in inventory turnover.
Revelation:In the current context of industry development, Internet companies should jump out of the thinking of “selling a single product” and think about how to use AI applications to change the pain points of the entire industry. Just like Alipay has changed the payment industry, AI in the future will change the operating logic of more traditional industries.
4. Career development reconstruction: from “skill superposition” to “cognitive leap”
1. Deep cultivation of technology: from “breadth first” to “depth is king”
In the “Summer Science and Technology Innovation Camp” of Shenzhen University of Science and Technology, programmers study quantum physics hard – this is not a cross-border show, but a practical need. When quantum computing affects AI computing power, Internet technology practitioners must also understand some of the principles behind quantum algorithms; Autonomous driving processes millimeter-wave radar data, and engineers must also be familiar with signal processing and probability theory. The technical threshold in the era of hard technology has been upgraded from “knowing how to use tools” to “understanding the underlying principles”.
This change is particularly noticeable in the field of chips. In the past, the design of chips relied on EDA tools to “put together building blocks”, but now the process below 7nm needs to understand the quantum tunneling effect and be proficient in materials science. Just like Unitree Technology’s robot engineers, they not only need to write code, but also understand mechanical dynamics in order to make robot dogs walk steadily in complex terrain.
Revelation:Internet practitioners are also not satisfied with “knowing how to use the framework”, but must dig deep into the “underlying logic”. The future “workplace stars” are likely to belong to those “compound experts” who are both proficient in AI algorithms and industry knowledge – such as AI doctors who understand medical knowledge and AI teachers who understand educational psychology.
2. Cross-border integration: from “fighting alone” to “team fighting expert”
At the brain-computer interface research and development site, it is normal for neuroscientists, electronic engineers, and computer experts to sit around and “quarrel”. Because this technology needs to convert brain signals into electrical signals, and then use AI algorithms to analyze them, a single discipline cannot do it. This kind of “multidisciplinary melee” has become the standard for hard technology research and development, just like the Human Genome Project requires multi-disciplinary collaboration.
ByteDance’s AI product team includes both algorithm engineers and sociology experts to jointly evaluate the social impact of content recommendation algorithms; Meituan’s intelligent distribution system research and development requires the collaborative work of logistics planners, traffic engineers and machine learning engineers. This “team battle” R&D model requires practitioners to break down professional barriers and build a T-shaped knowledge structure – vertically deepen the professional field and horizontally expand the interdisciplinary horizon.
Revelation:The new opportunities for career development lie in the “domain connection points”: product managers who understand AI can better understand the technical boundaries, and engineers who know business analysis can accurately locate the pain points of requirements. The high-paying positions of the future belong to those “cross-border integrators” who can build bridges between technology and business, engineering and art.
3. Cognitive upgrade: from “experience-driven” to “scientific thinking”
When Unitree Technology engineers developed the dynamic balance algorithm of robot dogs, they did 1,200 field tests, recorded data every time they fell, optimized it with mathematical modeling, and finally achieved millimeter-level accuracy. This “experiment-theory-verification” scientific method is replacing the traditional “trial and error method” in the Internet industry. In the past, products were made by “patting the head”, but now they have to rely on data and models to speak.
For example, when product managers design AI intelligent customer service, they no longer imitate the human customer service dialogue process, but start from “why do users need customer service”, analyze pain points, disassemble needs, and then use algorithms to reconstruct the service process. This shift in thinking requires practitioners to establish data-driven decision-making habits, validate hypotheses with A/B testing, predict effects with mathematical models, and bid farewell to the empiricism of “patting their heads”.
Revelation:Under the tide of industry change, stop being superstitious about “Internet experience”, scientific thinking is the “innovation engine”. Whether it is technology research and development or product design, we must learn to use the scientific method of “hypothesis-verification-iteration”, replace intuition with data, and replace experience with models.
4. Ethical awareness: from “technical innocence” to “responsibility on the shoulders”
When the EU’s ALTAI assessment tool checks whether AI algorithms are biased, and China’s Model Law on Artificial Intelligence 3.0 requires recording the full life cycle of data, practitioners realize that technology is no longer “just do what it uses”. Developing recruitment AI should avoid gender and age discrimination, and doing medical AI should also ensure that data privacy is safer than banks.
This change forces practitioners to become “ethical guardians”: ByteDance’s AI review team has engineers and sociology experts to evaluate whether the algorithm leads to an information cocoon; Baidu’s Wenxin Yiyan team has set up an “ethics committee”, and it must pass the “moral barrier” before the new feature is launched. Technical ability is no longer the only assessment criterion, and ethical awareness has become a “necessary quality” for practitioners.
Revelation:The more powerful the technology, the greater the responsibility. Practitioners must understand that AI assistants are not “cold codes” but “digital presence” that will affect the lives of massive users. The future professional competitiveness lies not only in the level of technology, but also in the ability to control “technical ethics”.
epilogue
Standing at the moment when AI reconstructs the relationship between man and machine, the essence of this “digital evolution war” isA new paradigm of collaboration written by “carbon-based life” and “silicon-based intelligence”。 When AI evolves from a “tool” to a “partner”, the mission of practitioners also changes: deep cultivation of technology needs to pay equal attention to ethical awareness, cross-border integration must be anchored by scientific thinking, and the end of the ecological game will belong to thoseA game-breaker who understands both “code logic” and “human temperature”。
