How will generative AI impact and transform our future?
TechFlow Selected TechFlow Selected
How will generative AI impact and transform our future?
The emergence of generative AI is not intended to replace humans, but to help people create faster, better, and at lower cost.
Navigating Web3 tides with focused insights[ Editor's Note ] Generative AI is becoming increasingly common in our daily lives. Today, artworks, articles, music, games, and even code—any creative work that once required human originality—may now be produced by generative AI. However, it’s important to understand that the emergence of generative AI is not meant to replace humans, but rather to help people create faster, better, and at lower cost.
In our previous article "Generative AI: A New World Full of Creativity | HongShan Insights", we introduced this emerging technological trend from perspectives including the development history, market landscape, and application breakdown of generative AI. Today, we continue to expand on this topic by exploring what types of participants might emerge around this trend, and how entrepreneurs can find opportunities within it.
Industries Most Impacted by Generative AI
1. Marketing and Copywriting
Copywriting is arguably one of the most eye-catching and well-known applications of generative AI. The cost of internet marketing continues to rise, and the difficulty grows as well—effective marketing demands significant time, skill, and expertise. The emergence of generative AI offers marketing teams a third option beyond outsourcing or building an in-house team: anyone can leverage this powerful AI assistant to smoothly write emails, blog posts, social media updates, and more, at speeds multiple times faster than before. For startups especially, this approach will dramatically reduce both cost and effort.
Tools like jasper.ai or copy.ai use algorithms similar to GPT-3. But their capabilities extend far beyond writing articles—they can also generate creative slogans, product descriptions, and various other content types. In the long term, this will have a profound impact on the entire copywriting and marketing industry, leading to a new kind of partnership between humans and AI.
If you're considering entrepreneurship in this space, note that competition will be fierce. Companies like Jasper.ai must quickly build strong barriers to entry to avoid being overwhelmed by rivals, as increasingly powerful competitors are emerging. In fact, nearly all these companies rely on variations of the same underlying algorithm—mostly variants of GPT-3—and none have independently developed their own core models. Moreover, these algorithms still fall short when generating content across broader topics, so wider adoption may take more time. Alternatively, GPT-4 may become the truly capable, scalable technology product we've been waiting for.
Additionally, many existing platforms and tools are already launching their own AI assistants. As a result, one-stop platforms like jasper.ai may lose favor. Overall, opportunities in this field for new startups are limited.
2. Visual Content Creation
Another prominent application area of generative AI is art and visual content creation, and its uses go well beyond mere entertainment. Artists use it for inspiration and rapid design prototyping, and some have even created 100% computer-generated artworks. We’re witnessing tremendous growth in AI-generated art, and a new type of artist with computer science skills is emerging. However, these tools are still far from perfect—for instance, outputs often aren’t exactly what users want, interaction during generation can be cumbersome, and results usually cannot be locally edited or modified.
For entrepreneurs, this gap represents opportunity: building better assistive tools for artists and designers by integrating generative AI more effectively into their workflows. For example, specialized tools tailored to specific design domains could, with AI enhancement and local editability, empower anyone to become a better, more efficient designer. We may also see new types of design agencies emerge built around these tools.
Of course, challenges remain, particularly regarding copyright issues. Additionally, since current algorithms are trained on publicly available internet data, there may come a time—such as when copyright laws evolve to prohibit using existing web materials for AI training—that we’ll need to hire large numbers of designers to create training datasets for companies like OpenAI and Stability AI, in order to fully unlock AI’s creative potential.
3. Gaming, VR, and the Metaverse
One of the biggest challenges in gaming, VR, and the "metaverse" is content creation. Take the gaming industry: over the past decade, most successful games have thrived due to their vast and rich virtual worlds. Yet, this aspect also represents the most expensive part of game development. Similarly, this remains one of the biggest hurdles for VR and metaverse companies today—without sufficient content to attract large user bases, revenues decline, creating a vicious cycle where less content can be produced over time.
For a long time, game companies have used approaches close to generative AI algorithms to build infinite worlds. The most popular method has been procedural generation—game designers provide algorithms containing objects, landscape elements, and rules, which then randomly generate new worlds. Games like Minecraft and No Man’s Sky use this approach. While simple, this method has significant limitations and lacks broad applicability.
Generative AI will change everything. Imagine being able to ask a computer not only to generate a world by combining your designed assets, but also to create entirely new content based on them, forming virtual worlds hundreds or thousands of times richer and larger. By fine-tuning 3D generation algorithms, integrating game designers’ inputs, and incorporating digital replicas of real-world objects, companies in the future could generate incredibly rich virtual worlds and games at just 1% of the current cost and time. And it doesn’t stop there—thanks to text and audio generation technologies, we may also create truly intelligent NPCs and game characters, breakthroughs previously unimaginable.
This will usher in a new era for gaming and the metaverse, making dreams no longer out of reach.
Moreover, its impact won’t be limited to video games—it will reshape the entire 3D domain. Entertainment, education, and even healthcare industries will undergo transformation as a result.
4. Code and Software Engineering
GitHub Copilot is a generative AI tool that automatically writes code, completing lines or functions after you type just a few words. Sounds revolutionary? Currently, however, it’s mainly helpful for non-professional developers, while offering limited value for professional engineers.
Non-professional developers benefit because they often lack precise knowledge of command syntax, especially when switching between programming languages—AI helps bridge those gaps. Even non-developer employees can use such tools for tasks like database queries without needing to write full programs.
For professional engineers, however, AI-generated code currently suffers from numerous structural and accuracy issues, requiring extensive manual review. Thus, the productivity boost is only about 1.5x—an improvement, certainly, but negligible compared to the 5–10x efficiency gains seen when transitioning from C to Python.
Overall, generative AI’s current main impacts in software development include four aspects:
-
Beginners and part-time programmers can write medium-quality code more efficiently;
-
Converting legacy code from old to new programming languages becomes easier;
-
More extensions and integrated development environments (IDEs) will better incorporate these technologies to enhance developer efficiency. Specifically, some tools may allow companies to train algorithms on existing codebases and best practices, creating intelligent assistants that manage repository-wide code consistency and onboard new employees;
-
By integrating these algorithms with no-code platforms, we’ll be able to build powerful web applications much more easily.
5. Others
These use cases may only represent the tip of the iceberg—many other transformative applications are already being tested or are nearing trial stages:
-
Using generative AI to discover new molecules and drugs. Due to their immense computational power, generative AI models can produce novel molecules and medications that humans might never conceive of or bother testing. This is one of the most promising areas for generative AI.
-
Cybersecurity. Black-hat hackers are already using generative AI to devise new attack methods and breach systems. On the white-hat side, some companies are leveraging it to prevent attacks and protect enterprises. This could become a massive market, as cybersecurity is one of the most critical issues of the 21st century. With great threats come great opportunities for ethical “white hats.”
-
Image and video optimization. There are already several strong players applying AI to optimize images and videos for specific purposes.
Characteristics of Future Participants in Generative AI
1. Global players focused on massive markets, solving multiple problems within a single product
It’s certain that over the next decade, several “decacorns”—companies valued at over $10 billion—will emerge centered around generative AI.
However, note that the number of such decacorns will likely remain small, as there simply aren’t enough markets where generative AI can generate over $10 billion in revenue. Given that most current generative AI companies use roughly the same algorithms, entrepreneurs aiming to become industry giants must be capable of building substantial competitive moats, such as achieving strong network effects, collecting proprietary data, optimizing user experience, or combining generative AI with other proprietary technologies.
2. Companies using generative AI to target very specificniches or applications
This may be where generative AI creates the greatest value. Generative AI will profoundly impact most industries as we know them. The real game-changers will be those who focus on upgrading generative AI tools and integrating deep industry expertise to develop functional solutions. If you’re thinking of starting a business here, carefully consider the key challenges in your chosen industry that AI could solve.
In some cases, companies may operate in such narrow niches that they won’t grow into unicorns, but they could still become highly profitable small businesses. Tweet Hunter is a great example—a company with fewer than four part-time employees yet generating over $1.2 million in annual recurring revenue. Applying generative AI to a highly specific niche may currently be one of the best opportunities to build a profitable side business, requiring minimal technical or time investment.
3. Established companies enhancing tools with generative AI
Even though most innovation in generative AI may come from startups, the majority of products we use daily will likely originate from established large tech firms. Today, major tech and large-scale companies are already entering the space, actively integrating capabilities like GPT-3 or Stability into their existing products.
Such innovations may arise internally through in-house development or externally via acquisitions. Therefore, in the next five to ten years, we will likely see many large corporations acquiring AI startups—an excellent opportunity for entrepreneurs.
Join TechFlow official community to stay tuned
Telegram:https://t.me/TechFlowDaily
X (Twitter):https://x.com/TechFlowPost
X (Twitter) EN:https://x.com/BlockFlow_News
