The market is full of hype around “agent” breakthroughs — from OpenAI’s browser-capable models to automation stacks like N8N. But most of these products still sit closer to the consumer and SMB side of the spectrum.

Over the past few months, I’ve been chasing the most “AI-friendly” zones — places where AI feels less like a demo and more like real leverage. Manufacturing, e-commerce, law, consulting — I’ve tried them all. It’s been a quick A/B test across industries, ICPs, product angles, and sales motions.

The biggest friction in enterprise AI adoption tends to show up at three critical stages:

1. The Business Owner Level

They normally have some rough idea about what AI can do, but not sure the exact “can-do” of AI/ agent system, and how it can integrate into their business/ existed systems.

Every industry have a general different level of understanding, I’ve found e-commerce is more open, and manufacturing & transporting are the most conservative.

For AI solution providers, the ideal way to engage them is by showing ready-to-use examples — concrete use cases that help customers imagine how AI could fit their world. It’s a classic B2B challenge: as Steve Jobs said, “people don’t know what they want until you show it to them.”

But here’s the paradox for startups — we rarely have dozens of polished use cases. Most of the time, we enter the conversation with one or two demos and use them as a thinking tool to help customers uncover their own needs. That’s how discovery actually happens.

Palantir used this model early on. They had one powerful demo, and used it to map customer pain points across entirely different industries — guiding clients to see how the same technology could solve their specific problems.

The process works best when the decision-maker already has a conceptual understanding of AI. For example, one of my clients — a 20-year-old company — had a CEO who wanted to explore how AI could drive efficiency. He already had a clear mental model of what AI can and can’t do, which made the initial conversation productive.

Still, even with an aligned CEO, the next hurdles usually appear with the department heads or IT teams — the ones who must turn vision into action.

2. Middle-level Managers Level

Department heads fear disrupting existing corporate systems. They often ask the same question: “Can this integrate with our existing SaaS tools or workflows?”

I see this concern on two levels.

First, they don’t want to abandon what they’ve already built. They want improvement, not replacement. If our AI agents completely overtake their existing systems, it could change how the team operates — even how the department is managed. Suddenly, the CEO might question the value of what the manager and their team spent years building. That creates insecurity.

Second, we’re not just competing on product features — we’re entering a political game against existing SaaS vendors, internal budgets, and the finance team’s rules. It’s a system of power — not just tools.

3. The Front Line Level

The final hurdle is the front-line employee. Employees want job security, so they resist change.

I won’t hesitate to say it: AI will change how people work. When you try to integrate AI agents into a company’s daily workflow, you face a wall of friction from the front-line workers.

For example, in one of my cases, the CEO was eager to adopt AI. He wanted it to analyze data and help him make better decisions. But when we tried to work with his IT team to integrate data from their existing systems, the project stalled. The IT team found an endless list of “challenges” and excuses.

The project has been stuck for weeks, and the reason is obvious: the employees know this agent might replace their jobs, or at least threaten the responsibilities they cover.

These are the three main walls I’ve hit selling B2B AI products in 2025. As a startup, we can’t afford to run through walls. We have to find the doors.

Our path forward has to be based on these facts:

• We don’t have the resources to educate the market. We must find customers who already understand AI’s potential.
• We must find the “spot” existing SaaS can’t cover. Or, we must provide a 10x better solution, (which, let’s be honest, is rare and hard to prove).
• Don’t fight over existing value—create new value. Make people feel stronger because of AI, not threatened by it.

So, the smarter path for an AI provider isn’t to threaten the current system, but to expand it. Find the “no man’s land”—the untapped areas where AI can enhance existing workflows. Reframe our value around that.

Managers are far more willing to adopt solutions that empower their teams without blowing up their entire structure.

The post Selling AI in 2025: What I’ve Gotten Wrong appeared first on Felix Kang.

There are not much new design or game mechanism behind them, what developers do is just remixes of existing genres, like FPS + Infinite runner; RPG + Card; SLG + RPG – and differentiate through themes, art styles, and character designs…

This trend signals a fundamental shift. The gaming industry has evolved into a standardized manufacturing sector, splitting into a stark 1/99 divide: 1% hand-crafted innovative titles, and 99% “fast-moving consumer goods”.

This mirrors what happened in the video content market. Platforms like TikTok and YouTube now dominate users’ attention with vast quantities of template-based, standardized content. This standardization and demand for rapid production creates the perfect opportunity for AI to revolutionize game development.

The standardized and fast production demand, giving AI the perfect application to solve it. Every types of games can be disassembled into components, and reconstructed. Majority of the game dev process can be automated by AI systems, e.g. numerical system, level design, art design, 3D modelling, animations, or story writing, which can give the game infinite story development.

However, this AI gaming revolution likely won’t begin on traditional platforms like Steam or PlayStation. Instead, expect it to emerge from hyper-casual mobile games, social media mini-games, and Web3 gaming platforms. Here’s why:

1. Current AI Capabilities Match Casual Gaming Needs
   
   While AI-generated content hasn’t reached AAA game standards, it’s perfectly suited for casual games where component reuse is common and quality expectations are different. AI excels at creating variations on existing patterns—exactly what the casual market demands.
2. AI Excels at Iteration, Not Innovation
   
   Similar to how LLMs train on existing human knowledge, AI game development tools will excel at remixing and recombining existing game design elements rather than creating entirely new paradigms. This aligns perfectly with the casual gaming market’s need for familiar mechanics with fresh twists.
3. Speed and Standardization Drive Casual Gaming
   
   Casual games have short lifecycles, requiring constant updates and fresh content to maintain user engagement. AI-powered development pipelines can deliver rapid iterations and new features to drive user retention and monetization.
4. Gaming as a Feature
   
   Consider trending crypto games like Catizen and Hamster on Telegram. These products prioritize tokenomics and social features over complex gameplay. AI-generated game elements can provide adequate entertainment while developers focus on core economic and community features.

While some might worry this trend will flood the market with low-quality games, I see it differently. AI is democratizing game creation, similar to how smartphones and editing tools democratized video content. This democratization led to platforms like YouTube and TikTok, which spawned billions of creators and more diverse, high-quality content than the traditional TV/film industry could produce alone.

The future of gaming will likely mirror this pattern, splitting into two distinct categories:

• The top 1%: Elite teams of visionaries creating unprecedented, groundbreaking game experiences

• The other 99%: Enthusiasts and general creators using AI tools to bring their unique ideas to gamified content

This democratization won’t diminish gaming—it will expand it. Just as social media birthed new forms of entertainment beyond traditional media’s imagination, AI-powered game creation tools will enable new kinds of interactive experiences we can’t yet envision.

The real revolution isn’t in AI making games—it’s in AI enabling everyone to become a game creator.

The post Mini Games: AI’s Testing Ground for Mass Game Production appeared first on Felix Kang.

As we enter 2024, PC gaming remains strong. The Steam platform reached an all-time high of 33.7 million concurrent players on Sunday, and a record-breaking 14,531 games were launched on Steam in 2023 – roughly 40 new games per day! Meanwhile, Steam has announced support for AI-generated content in games, stating “We will release the vast majority of AI games.”

Will we see “Free Guy” like characters in the 2024 game market?

Last May at COMPUTEX 2023, Nvidia unveiled its new AI technology Omniverse Avatar Cloud Engine (ACE). This is a real-time AI solution that provides AI models for speech, dialog, and character animation in games – adding real-time interactive capabilities to NPCs. At this year’s CES, they updated the system again. they demonstrated how ACE technology combines speech-to-text recognition and text-to-speech responses with generative AI facial animation and automated character personas to generate computer-created character interactions. BTW, the demo is developed by ConvAI and uses Unreal engine.

NPC Creation in Modern Gaming

In 2024’s upcoming AAA games and current game DLCs, NPCs are still relying heavily on massive human and resource investment for creation.

Games such as The Witcher 3, Red Dead Redemption 2, and Cyberpunk 2077 showcase NPCs that possess remarkable freedom and autonomy, contributing substantially to the narrative’s depth and realism. However, the methodology employed in achieving this still adheres to traditional practices—relying on expansive teams and extensive scripting.

Red Dead Redemption 2 has over 1000 NPC characters across 100+ missions in 6 chapters. Each NPC has their own singer, artist, and voice actor, with nearly 8 years of development and $500 million in costs for the NPCs.

Even the highly anticipated GTA 6, with its colossal $2 billion budget, follows suit. Despite its more expansive open world, the creation of NPCs still necessitates manual intervention and intricate decision tree programming.

Despite this, studies show 52% of players complain current NPCs “just repeat dialogues”, 99% want more intelligent NPCs, and 81% would even pay more for them.

Which AI NPC development tool is better?

There are two major players in the AI NPC development tool market: Inworld and ConvAI. Both support Unreal and Unity engines and have similar functionality – creating 3D avatars, character design, natural conversation, emotions, and reactions. I tested both tools on different engines to set up a functional AI NPC in my 3D world within 2-3 hours. The time required depends on the engine and avatar used.

Unreal Engine:

• Inworld
  • Couldn’t get it running on my Unreal project
• ConvAI
  • Worked well overall. However, the AI voice was inconsistent in tone within conversations.
  • Ready Player Me avatars don’t support gestures, following, or battles. Metahuman avatars can do more but require beefy hardware.
  • The GUI is locked to the game view and can’t be hidden.

I also tried Inworld again in Unity:

• Inworld
  • Quick and easy setup compared to ConvAI.
  • Like Ready Player Me avatars in Unreal, no gesture, follow, or battle support.

In summary, the tools are quite similar – they provide comparable functionality and interaction capabilities. They can stand in for quest givers in 3D games but aren’t robust enough for complex tasks.

I won’t go into full details in this post, but let me know if you want more specifics on any part and I can expand on it in a future post. Comments and let me know.

Challenges Faced by AI NPCs

• Lack of Human-Like Characteristics
  
  AI platforms like GPT-4, Claude 2.1, Inworld, and others boast powerful character generation capabilities. However, players easily discern differences in behavior and emotion compared to human interactions. This discrepancy can disrupt the sense of “real” character engagement, diminishing overall immersion.
• Cost Barriers: The expenses associated with high-quality AI tools and platform APIs pose challenges for small and mid-sized developers, hindering widespread adoption. Economic constraints create barriers, impeding the swift integration of advanced AI technology in the gaming industry.
• Fundamental Model Limitations: The fundamental models underlying these AI tools are not specifically trained for game development purposes. This can complicate seamless integration into game projects. The lack of gaming-specific use cases and optimized configurations poses additional challenges for developers trying to leverage these tools.
• API Stability and Token Limits: Issues such as API stability and maximum token limits further hinder AI integration into the gaming landscape. Even advanced tools like GPT-4 encounter constraints, with token limits restricting the depth and length of NPC conversations. This limitation often results in AI forgetting initial conversation content, particularly detrimental in narratives that rely on extensive and intricate dialogues for in-game storytelling and character development.

Key Elements to “Free Guy”

• Intelligence Beyond Appearance
  
  Just like talented human actors can portray roles well, the realism of AI NPCs depends first on having an underlying model that’s sufficiently smart – with strong general intelligence.
  
  This is mostly determined by pretraining, but game NPC models need broad data plus lots of high-quality dialog, storytelling, etc. tailored to “roleplaying” – not just general text. This enables more human-like conversational ability.

• Cost Reduction
  
  Current industry approaches rely on inefficient API calls across multiple teams. As base model prices fall and indexed knowledge bases for game NPCs emerge, costs can decrease dramatically. Otherwise, high API and token usage will raise game prices or require new monetization like in-game purchases for AI dialog.

• Advancements in Movement and Reactions
  
  While current tools like ConvAI and Inworld excel in handling dialog, actions beyond simple facial expressions often require additional manual programming.
  
  Recent research by Daniel Holden presents promising developments in lifelike simulated actions. When integrated with existing AI decision frameworks, this innovation has the potential to significantly elevate the realism of NPC movements, extending beyond gestures to actions like fighting or hiding.

All in all, In films like Westworld and Free Guy, the idea of NPCs with “consciousness” interacting dynamically with the game world is getting closer, thanks to advances in generative AI. However, these capabilities are mainly in top companies due to tech limitations and funding constraints.

The good news is these challenges are being actively worked on, but we likely won’t see major breakthroughs in 2024.

Comments? Any thoughts to share?
If you liked this post, please follow me on Twitter. You can also find more essays here.

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1. Opt for Work with a Compounding Effect: Instead of merely trading time for wages, focus on tasks that generate a compounding impact over time.
   
2. Take on tasks aligned with your maximum capabilities： Identify and undertake tasks that match your highest capabilities. If what you’re doing falls below your predefined value, don’t hesitate to adjust, outsource, or delegate to AI.
   
3. Practice Patience in Personal Growth: Understand that personal and professional growth often takes more time than we anticipate. Give yourself the freedom of several months, or even years, to make transformative changes— nothing is easy.
   e.g. OpenAI takes 7 years to launch a product that everyone loves.
   
4. If external progress doesn’t meet expectations, focus on personal growth.
   
5. Prioritize Core Metrics: Concentrate 90% of your efforts on core metrics. This focus will naturally resolve other details, preventing internal friction.
   
6. Lower Expectations for Greater Devotion: Lower your expectations. A smaller set of desires means fewer concerns and obsessions, allowing you to fully immerse yourself in your work.
   
7. No one cares about how hard you work, much like no one cares about what time Buffett wakes up. What truly matters is excelling in your business. Learn to leverage, master AI, and establish your own AI co-pilot to amplify your capabilities. It will amplify your marginal capabilities.
   
8. Life is short: Acknowledge the finite nature of life. Each day presents a unique opportunity for accomplishment. Strive for continual growth, relish the experiences, and take a moment to enjoy the metaphorical blue sky.

The post Something I Want To Share appeared first on Felix Kang.

3D era is coming, everybody will be able to create and share games, film, virtual worlds, and simulations. But, it may not work the way you want it.

Hi Readers

Due to my work, I’ve spent thousands of hours on 3D creations in previous years. Although I don’t publish games, I understand how 3D creations work, from story writing to prototyping and production in a 3D engine. As we all know, 3D creation is unlike photo or video creation; it requires a high learning cost and professional knowledge and is usually done by a team.

To make 3D creation accessible to everyone, we have to solve the problem of the entire pipeline: generating a story, turning the story into game chapters, creating game assets, building and coding the game logic, map, avatar, NPCs, abilities, and more.

AI. Yes, AI has been considered the best way to solve this problem. We have seen some interesting products on the market to solve different problems along the way (I will list them later). But in my opinion, they are heading in the wrong direction.

Coincidentally, I gained inspiration from the office collaboration work platform – Lark. My recent work has transitioned to Lark, an all-in-one work platform. From group messaging, email, documents, video meetings, and calendars, to team wiki knowledge bases and AI assistants, everything can now be accomplished within a single platform. This serves as a compelling example of the integration of AI into the workplace.

This transition prompted me to ponder: AI has already transformed our learning, work, and content creation. Why does 3D content creation still remain complex and somewhat outdated? Could the AI + platform model provide insights and inspiration for the 3D content industry?

So before discussing the future of the 3D engine, let’s take a look at how AI empowers the workspace.

3rd Generation AI workspace

The first-generation workspace is Microsoft Office, where every file is independent, and team members find it hard to collaborate with each other.

The second generation evolved to Google Docs, where team members can access the same file and edit together, but there are issues with file management and knowledge management.

The third generation includes Notion, Lark Doc, etc. The files are based on an integrated knowledge base with robust version control and AI integration.

1. Information find people, not people find information
   
   We’ve been using tools Word, Excel, and PowerPoint for decades. Excel is great for finding data and figuring out problems. But can everyone really use all of Excel’s features? (Think about what the World Excel Championship champion two weeks ago) With AI, we can now easily spot the answer behind the data without needing to be Excel experts. AI + data helps us solve problems by getting information directly, without the need for complicated Excel formulas. That is how AI could change the way of getting information.
   
2. Proactive and Real-time Information Retrieval
   
   Every team has its own way of welcoming new members, telling them about the company’s culture, document rules, decision-making steps, and more. But new folks might forget things, and if the team’s documents are messy or outdated, it can be hard to find what they need. With an AI assistant, all the team’s latest documents are in one place. Newcomers can just ask the AI if they have questions, making it easier to get company info and standards. This makes it simpler to find information and lowers the cost of team communication.
   
3. Personalization – Tailored Workspaces for Everyone
   
   In Word and Excel, everyone sees the same layout, even though we use different features. A good workspace should be like a platform that fits each person’s needs. AI can make the next-gen recommendation engine – not for content but for functions. It can understand what you’re working on and suggest the support and features you need, saving you from a bunch of steps. For example, if a team member asks me for a user profile analysis of last year’s active product users, when I open my workspace, the user behavior data table and analysis template are already there for me.

Will 3D Engines Follow Suit?

There is no all-in-one 3D-dev suite yet

Before the 2010s, most studios created their own internal engines for game development. Nowadays, almost all games are crafted using third-party engines, except some major AAA games that still opt for in-house development (e.g., EA’s Frostbite, Infinity Ward Engine, etc.).

Today, we call them 3D Creation Engines because they’re not just for games; they’re for any virtual simulation. However, these engines mainly tackle issues during the “production stage.” Game development teams still need other tools for different steps, like Google Docs, Slack, Trello, Photoshop, Blender, 3Ds Max, and more.

Potential AI 3D Creation Tools

We have seen some AI tools are trying to solve the 3D creation along the pipeline. Here are some I have spotted

[Story Crafting] Companies like Hidden Door and Storycraft are pioneering AI-driven story-driven games, offering players constant streams of unique, personalized experiences. For instance, game objectives might change based on a player’s Bartle type, broadening the potential audience for each game.

[Game Art] In the realm of art, AI is making waves. Midjourney, DALL-E, Stable Diffusion, and Runway ML are leading the way in generating game art. However, achieving optimal results may require some practice with prompts and familiarity with each model’s temperament.

[3D Models] While text-to-3D modeling is still in its early stages, interesting products are emerging. Tools like DreamFusion can generate 3D models from text inputs, expanding on Google’s 2021 unveiling of Dream Fields. Point-E works similarly to OpenAI’s image generation tool DALL-E; you can describe something, and it generates a 3D model. Meshy aids in turning text or images into 3D or lets you upload a 3D model for AI to add textures.

[In-game Social] Enhancing in-game social and NPC interactions is possible with tools like , convai, and Replica Studios recently showcased a demo featuring a modified version of the Matrix Awakens game, allowing users to converse with NPCs using their own microphone.

I can’t cover them all, but it’s evident that 3D-related AI generation tools are still in their early stages, and we need more time to optimize both the models and user experience. The idea of using over 10 AI tools to create a game is hard to imagine.

AI 3D-dev Suite Will Look Like This

1. All-in-One Game Development Information Flow
   
   Similar to AI workspaces, game development info will be more integrated. From brainstorming ideas to the game story, numbers, game design docs, and later, art guidance. No more scattered files on Slack or Google Docs. AI will bring everything together, preventing info from getting lost in big teams or between departments.
2. AI Generates Over 50% of Framework Content
   
   Whether it’s RPGs, action games, simulations, survival games – the fundamental logic underlying each game is the same. The variations mostly come from art, style, game pace, and other elements. AI models trained on games can generate 50-70% of the basic modules based on the creator’s needs. This allows creators to focus more on crafting the core creative elements.
3. Easier Creation, Simplified Steps
   
   When the complex foundation of game development is automated, 3D creators can focus more on creativity than technical skills. It means we won’t need as many artists or animators but will require more creative planners. This change makes the process less reliant on a lot of manual work, making it more about creativity. Anyone can turn their ideas into playable 3D content, just like quickly editing a short video with ‘CapCut.’

However, Unity and Unreal won’t be replaced, just like Photoshop remains the most powerful tool for image creation. Even with AI, they still cater to professional users and may not be suitable for the broader consumer market, as the dynamics in the consumer market operate under entirely different rules.

But they might not be the go-to 3D creation tools. The rise of UGC platforms and their creation tools will make game creation easier for non-developers.

For instance, back in 2000, Maxis released modding toolkits for The Sims even before the game itself. This allowed players to make personalized in-game assets, making the game bigger and more unique. These toolkits and templates made it easier for players to start creating. Similarly, UGC services like Roblox Studio have made game creation more accessible, creating a cycle where more people are encouraged to make games.

Therefore, I believe the next AI 3D creation tools won’t be standalone; they’ll come with a user-generated content (UGC) platform, similar to the relationship between TikTok and CapCut. When the general users develop a demand for 3D content creation, AI 3D-dev suites become meaningful.

Imagine a 3D version of TikTok. With a platform where everyone creates game stories, we’ll need an easy-to-use AI 3D tool that turns ideas into playable 3D games in minutes.

Comments? Any thoughts to share?
If you liked this post, please follow me on Twitter. You can also find more essays here.

The post Will AI Kill Unity and Unreal? Maybe the AI Workspace Holds the Answer. appeared first on Felix Kang.

It is the iPhone 4’s moment of AI. After Open AI’s keynotes and drama story, here are some of my thoughts.

Today, whether it’s developers, the capital market, or the media, everyone is comparing AI to the mobile internet era. In the past 20 years, the internet has cleansed and rebuilt every industry.

Will AI do the same?

In my opinion, in a certain sense, AI will have a more impactful effect than the internet. However, it won’t necessarily disrupt existing internet giants but rather give birth to new blue ocean markets. Along the way, there will be different stages throughout the process. As entrepreneurs, investors or participants, we need to identify the stage and make suitable decisions.

AI VS Mobile internet

Perhaps not every application will undergo a complete overhaul. Many applications have already found practical applications in their domains, and simply incorporating AI as an auxiliary function can lead to upgrades and iterations.

Take Windows operating system as an example; it has been evolving continuously and Microsoft has added copilot into Win11, these applications establishing a symbiotic relationship with A.

However, for emerging startups originating from the fringes, they may enter the mainstream market in a completely new form, challenging the established positions of existing giants.

How to identify the timing?

Looking back, the release of the iPhone in 2007 marked the beginning of a fragile era for the entire mobile internet. In 2010, with the launch of the iPhone 4, the overall framework of mobile internet capabilities became relatively sound.

Another crucial point came in 2011 when mobile internet applications exploded, giving rise to a series of companies established in that year, such as Instagram and Uber, which are now well-known.

As for AI, where is the next key milestone? Perhaps, we need to pay attention to the development of architectures like Transformer, as they have the potential to unlock new opportunities in areas such as video models and protein models.

Where are the next big opportunities for AI?

Imagine if the Transformer model or other AI architectures could accurately predict the next frame of a video, generating a dynamic, evolving 3D world. This would give rise to next-generation entertainment content platforms similar to TikTok. A user-generated content (UGC) based 3D AI TikTok?

If AI could accurately predict protein sequences, it would lead to new advancements in drug development. Similarly, accurate prediction of the next step of human actions could enable human-like robots to overcome limitations.

In this perspective, the current AI model can be compared to the early versions of the iPhone, waiting for a similar opportunity for a significant breakthrough.

Are the growth strategy same for AI and Internet product?

AI applications differ from the free tools and products of the internet era. AI products carry a high cost of inference, making them less likely to be entirely free from the start.

However, ultimately successful AI applications still require a large user base (e.g., ChatGPT / CharacterAI). But challenges arise when capital no longer supports subsidies, and achieving the explosive growth phenomenon seen in the mobile internet era becomes more challenging without charging fees.

Midjourney is a good example. In this case, every acquired user becomes a paying user. Therefore, it is unlikely to rely on massive fission marketing and may instead focus on linear growth.

Recognize the cycle, play smart

Looking back at the development of the mobile internet era, initially, we see fake opportunities like flashlight tools, those APPs gained millions of users in a short time, but they were eliminated very soon. Just as what we see in the OpenAI’s keynote, GPTs kill all AI Automation Agencies.

After that, super apps came, and ultimately evolved into a few must-have APPs for everyone, such as Facebook, TikTok, Amazon, Grubhub…

The field of AI may have a similar development pattern, and we need to recognize this cycle and wisely choose our strategies. In the AI era, we need to play smart, identify future opportunities, and not be swayed by short-term trends.

The post Is AI same as Internet Era? And how to invest and build the next top level company appeared first on Felix Kang.