Original · Unique Research · 2025-11-18
Editorial note: This complete English edition preserves the historical article and all 12 interview questions and answers. Product performance, translation-cycle reductions, cost savings, staffing comparisons, market assessments and compliance capabilities are claims made in the source or by Hu Jian, not independently verified current results. “Elite partner” translates the source’s self-description 菁英合作伙伴; it does not establish a verified Google Cloud partner tier or a current certification. The source introduces Hu Jian as Tony; no additional English legal company name is inferred for 帕皮云科技. Agent use is described as internal experimentation and work in some client projects, not universal deployment.
Many people working in cloud and AI start with the technology and then look for use cases.
Hu Jian, also known as Tony, took the opposite path.
Before founding CloudPuppy, he had already spent more than a decade on the front lines of internet marketing and business growth. His primary brand, Iplayable, specializes in playable ads and the most results-oriented work there is: helping clients acquire users, improve conversion, and drive growth. Only someone who has truly been accountable for business metrics becomes especially sensitive to whether a technology is actually useful.
So when he later founded CloudPuppy and turned Google Cloud and Gemini AI services into an independent brand, his starting point was simple: cutting-edge technology should no longer be a term on a PPT slide; it should become a technical teammate for enterprise growth.
CloudPuppy aligned itself with the Google Cloud ecosystem, providing end-to-end services for companies expanding overseas across cloud architecture, big-data migration, application development, and Gemini AI implementation. It defines itself not as a cloud service provider, but as the Google Cloud elite partner that best understands overseas-expansion businesses.
First build a solid understanding of the business; only then discuss how to move to the cloud and use AI.
That distinction matters: when you position yourself as a technical teammate, your job is not merely to deliver a project, but to run the long race alongside the company.
1. The real dividing line in generative AI: from chatting to getting work done
Over the past two years, we have heard countless descriptions of generative AI: it understands natural language, writes copy, and writes code...
But in Hu’s view, there is only one real dividing line: can AI enter the business workflow?
Earlier AI was better at understanding information and answering questions;
Today’s AI is beginning to combine three capabilities:
Multimodality: it can understand text, audio, video, and images;
Long context windows: it can retain an entire project or dataset over time;
Task execution: rather than merely producing a passage of text, it can actually operate systems, call APIs, and modify data.
In CloudPuppy’s work, these capabilities are embedded in clients’ big-data systems, customer-service operations, and content-production pipelines.
Instead of opening a chat window and asking AI, “What should I do?”, users let AI process logs, organize reports, generate content, and make decision recommendations within their existing systems.
Their objective can be summed up in one sentence:
It is not enough for AI to answer questions; AI must genuinely solve problems.
2. A short-drama translation tool that embodies a methodology for going global
At first glance, CloudPuppy’s self-developed GemiAI is a subtitle-translation tool for short dramas.
But once you break down clients’ real-world scenarios, it becomes clear that it does far more than translate subtitles.
The pain points in taking short dramas overseas are very specific:
Cultural mismatch: when original Chinese dialogue is translated literally into less widely supported languages, the grammar may be right while the flavor is lost;
Weak quality in less widely supported languages: conventional translation systems perform far worse in Vietnamese, Indonesian, and Arabic than in English and Japanese;
Inaccurate detection of sensitive terms: platforms have different rules and review standards, while manual checking is slow and expensive.
The traditional approach requires a team of 3–5 people to handle transcription, translation, polishing, and review from end to end. It takes a long time, costs a great deal, and is often rejected by platforms for rework because the team failed to understand the culture.
GemiAI combines Gemini’s large-model capabilities with the corpora, rules, sensitive-word libraries, and platform experience accumulated in the industry, turning them into a tool purpose-built for taking short dramas overseas:
Translation cycles are shortened by more than 70%;
Many teams have gone from assigning 3–5 people to one drama to having a single editor complete it independently;
Translation, proofreading, and sensitive-content filtering are completed in one pass, greatly reducing communication and rework.
On the surface, it is a vertical tool. In substance, it is a CloudPuppy showcase: large-model capabilities are embedded deeply in a vertical use case, and AI’s value is validated through business outcomes.
3. Agentic AI: from a tool to half a colleague
Hu is not satisfied with having a better translation tool. He is more interested in the next step: agents, or Agentic AI.
In CloudPuppy’s internal experiments, Agent systems have already begun entering three areas:
Automated translation: rather than simply calling an API, the Agent automatically breaks down tasks, queues them, and generates versions based on a drama’s progress, the requirements of different platforms, and historical translation styles;
Automated operations: an Agent watches the logs, costs, and alerts of cloud services, automatically initiating remediation and reporting when it detects risks or anomalies;
Content production: in Iplayable’s playable-ad business, AI automatically generates multiple creative variants, runs A/B tests, monitors performance, and recommends adjustments.
This is entirely different from the traditional experience of one person chatting with one large model.
In Hu’s understanding, the essence of Agentic AI is giving AI the ability to break down objectives, execute tasks, and monitor results.
Once an agent becomes familiar with a company’s own data, processes, and business logic, it is no longer merely a general-purpose tool; it becomes half a colleague inside the enterprise.
The future form of SaaS may also be reshaped by such agents: instead of buying a menu of features, users will buy a group of trained AI employees.
CloudPuppy wants to position itself at the forefront of this wave as early as possible.
4. Making overseas expansion simpler: deep localization plus unified cross-regional technology
As AI technology spreads without borders, CloudPuppy’s clients are spanning an increasing number of countries.
Its clients have come from Southeast Asia, the Middle East, Latin America, and other regions, with needs concentrated in several areas: localized translation, cross-regional deployment, compliance hosting, and data governance.
The difficulty is equally direct:
Cultures, censorship standards, and privacy regulations differ from country to country. Companies want a globally unified technology architecture, but they must also implement it in compliance with local rules.
CloudPuppy’s path is deep localization plus unified cross-regional technology.
More specifically:
On the technology side, it relies on Google Cloud’s global infrastructure and compliance system for multi-region deployment, data isolation, and access management;
On the business side, it works with local partners to co-develop data and model-calibration mechanisms. In high-risk scenarios such as video translation and content filtering, it continuously improves performance with local-language corpora.
As a result, companies see the same technical capabilities around the world, while local users experience content and services that understand them and their culture.
5. The real strengths of Chinese AI companies go beyond low cost
When people discuss Chinese AI companies expanding overseas, their first thought is often that better value for money gives them a price advantage.
Hu’s view is more pragmatic and more optimistic.
In his view, Chinese AI teams possess several foundational capabilities that lead the world:
Strong engineering capabilities: they can rapidly implement complex systems and deliver them reliably;
Fast delivery: fast pace, fast feedback, and fast iteration;
Strong cost control: they can push execution to the limit within a constrained budget;
Deep understanding of use cases: years of working with clients across industries have given them practical insight into how businesses actually operate.
What truly constrains Chinese AI companies from exerting greater influence in global markets lies in several other dimensions:
Global product capabilities, international compliance development, and the ability to collaborate with global ecosystems.
That is precisely why CloudPuppy chose to work alongside a global ecosystem such as Google Cloud: it uses the ecosystem’s infrastructure and compliance capabilities while also seeking partnership opportunities within the ISV and SaaS ecosystems.
In Hu’s account, Chinese companies will not merely keep pace with global AI; they will become one of the forces driving the global AI industry forward. The condition is that we are willing to do the less glamorous work of product development, compliance, and ecosystem building properly.
6. The age of the individual: when everyone has an AI team
The theme of this conference is “Pioneering Intelligence | The Age of the Individual.”
For Hu, this is not a polished slogan but something that is already happening.
In the past, an individual’s ceiling was clear: what one person could accomplish was largely determined by their time, physical capacity, and network.
With AI, however, individuals are beginning to possess personal supercomputing power and an entire virtual team: writing copy, editing videos, analyzing data, coding, running ads, reviewing reports... Many tasks no longer require a department; they require only that you learn how to direct AI.
CloudPuppy’s GemiAI is one small example:
Cross-language translation of a short drama that once required 3–5 people can now be completed by a single content editor, improving efficiency by 3–5 times and reducing costs by 70%.
In advertising, content-generation and optimization tools based on large models also enable one editor to produce what once required an entire creative team.
That is the most practical meaning of the age of the individual: moving from dependence on organizations to dependence on tools, and from people performing tasks to people managing AI that performs those tasks.
7. The dividing line over the next three years: who truly knows how to use AI
Hu puts it bluntly:
This is the era in which it is easiest to make money by implementing AI—and also the era in which it is easiest to be eliminated by change.
The opportunity is easy to understand: tool capabilities are exploding, and the barriers to cloud services and large models keep falling. Any team with a real use case and at least some product capability has an opportunity to create a monetizable AI solution in a specialized field.
The challenge is equally real: technology is changing too quickly. Models are being upgraded, frameworks are shifting, and something new appears in the ecosystem every day. Products and teams that fail to keep pace can easily be left behind by a new generation of solutions. The real competition is no longer whether my model scores 0.1 points higher, but who can turn AI into products faster and more consistently, creating repeatable commercial value.
From the perspective of individuals and small teams, his advice is similarly pragmatic: over the next 1–3 years, the real gap will open across three dimensions:
Skills: can you write prompts, work with data, and build automated workflows?
Tools: have you selected the right AI assistants, agent platforms, and content and data tools for your needs?
Mindset: are you still accustomed to doing everything yourself, or have you begun learning to let AI do the work while you define requirements, review results, and adjust strategy?
Ultimately, AI will not automatically elevate anyone to a higher position.
It quietly amplifies the strengths each person already has, while likewise amplifying every company’s weaknesses.
What CloudPuppy is doing, in essence, is standing between infrastructure and applications and making this force usable, controllable, and repeatable.
For everyone who is expanding overseas, starting a business, or undergoing a transformation, the more important question may be:
When AI can already become your technical teammate, are you ready to take on a role more like that of a coach and director?
Selected Interview Q&A
Q1: In one sentence, please introduce yourself and CloudPuppy.
Hu Jian: I’m Hu Jian; people call me Tony. I’ve worked in internet marketing and business growth for 15 years. I now run CloudPuppy (帕皮云科技). We are “the Google Cloud elite partner that best understands overseas-expansion businesses,” helping companies put cloud technology and Gemini AI to real use in their operations rather than leaving them at the conceptual level.
Q2: What are CloudPuppy’s core businesses and products today?
Hu Jian: One part covers cloud architecture, big-data migration, application development, and enterprise deployment of Gemini AI around Google Cloud. The other is GemiAI, our self-developed subtitle-translation tool for short dramas. Put simply, we use cloud technology + AI to help companies expanding overseas improve efficiency, save money, and avoid costly mistakes.
Q3: What specific problems does GemiAI solve for short-drama teams?
Hu Jian: In the past, cross-language translation of a short drama commonly faced cultural mismatch, weak quality in less widely supported languages, and inaccurate identification of sensitive terms, while keeping 3–5 people busy for a long time. After GemiAI was launched, one editor could handle the work, translation efficiency improved by 3–5 times, the overall cycle was shortened by more than 70%, and costs fell substantially as well.
Q4: What motivated you to create CloudPuppy?
Hu Jian: I previously worked on playable advertising at Iplayable and dealt with business metrics every day, so I knew exactly what kinds of technology were useful for growth. That was the starting point for CloudPuppy: make cloud technology and AI a company’s “technical teammates,” genuinely driving revenue and efficiency rather than remaining buzzwords on a PPT slide.
Q5: In your view, what is the most fundamental breakthrough in this wave of generative AI?
Hu Jian: It is not that AI answers in a more humanlike way, but that it has progressed from understanding and generating to executing tasks. Multimodality + long context windows + execution capabilities allow AI to be embedded in real business workflows and participate in customer service, data analysis, and content production rather than remaining inside a chat window.
Q6: How does CloudPuppy apply these cutting-edge capabilities in client projects?
Hu Jian: We deeply integrate Gemini’s capabilities into companies’ existing big-data systems, customer-service systems, and content-production pipelines, and then optimize them further with our industry experience. The ultimate objective is for AI not merely to answer questions, but to take responsibility for business outcomes and genuinely solve problems.
Q7: With technology evolving so quickly, how do you balance innovation with commercial implementation?
Hu Jian: Our principle is that technology serves the business. In terms of resources, we invest roughly half in Gemini and foundational cloud capabilities and half in representative industry use cases, refining both through real projects. Every technology ultimately has to become a repeatable solution and create stable commercial value rather than remain an experiment.
Q8: How far have you progressed with Agentic AI?
Hu Jian: We are already using Agent systems in our own operations and in some client projects for three things: automated translation, automated operations, and content production. In the playable-ad business, AI helps us generate and optimize advertising creative. In the long run, agents will reshape the form of SaaS, and companies will increasingly use AI employees.
Q9: In overseas business, how do you handle cultural and compliance differences among countries?
Hu Jian: At the foundational level, we rely on Google Cloud’s global compliance system for multi-region deployment, data isolation, and access management. At the business level, we work with local partners to co-develop data and model-calibration mechanisms. Particularly in highly sensitive scenarios such as video translation and content filtering, we continuously tune the systems using local-language corpora.
Q10: Where do you think Chinese AI companies’ real advantages lie in global competition?
Hu Jian: Strong engineering capabilities, fast delivery, good cost control, and deep understanding of use cases—these are all hard strengths of Chinese teams. The next areas to strengthen are global product capabilities, international compliance capabilities, and deeper collaboration with global ecosystems such as Google Cloud.
Q11: From the perspective of your field, what is the biggest opportunity for AI builders today?
Hu Jian: The opportunity lies in the explosion of tool capabilities and the maturity of infrastructure. This is the window in which AI is easiest to implement profitably. If you have a real use case and some product capability, you have an opportunity to create a monetizable AI solution.
Q12: And what is the biggest challenge?
Hu Jian: Technology is changing too quickly. If products and teams do not keep iterating, they can easily be replaced by a new generation of players. The real competition is no longer about whose model score is a fraction higher, but who can turn AI into products faster and more consistently and create a long-term commercial flywheel.