AI is the only thing anyone wants to fund, but he’s 38, has two kids in a Bay Area mortgage.

I told him what I’d tell anyone in that seat in May 2026. The thing he should be aiming at is called a Forward Deployed Engineer (FDE), By the end of that same week, he had three intro calls lined up — one of which was a $4B fund that had been spun out four days earlier.

This essay is for everyone else in that seat.

What just happened in ten days

Between May 4 and May 14, 2026:

• OpenAI spun out the OpenAI Deployment Company with $4B in initial capital from TPG, Advent, McKinsey, and Bain. Same week, they quietly acquired Tomoro and absorbed roughly 150 applied-AI engineers in a single transaction.
• Anthropic announced a $1.5B joint venture with Blackstone and Goldman Sachs, custom-built to embed engineers inside banks. The first deployment, with FIS, compressed AML investigations from “hours to minutes” — the actual phrase in the press release, with no marketing softening.
• Google Cloud CEO Thomas Kurian wrote on LinkedIn that “the pilot era is over” and announced “hundreds” of new Agent Engineer hires.
• ServiceNow and Accenture co-launched a parallel embedded-engineer program. IBM Consulting rolled out something they’re calling Forward Deployed Units — six-person hybrid squads pairing humans with agent “digital employees”, ostensibly delivering the output of a traditional 30-person team.

Five organizations, three product categories, one bet. $5.5B committed capital pointed at the same job description across those ten days:

The job is Forward Deployed Engineer (FDE).

By any honest reading, this is the hottest single-role market in tech right now.

You haven’t heard about it because it doesn’t look like AI from the outside. It looks like consulting.

What FDE actually is

The simplest version: an FDE is an engineer the lab embeds inside a customer’s office, sometimes for 18 months at a time, to ship working AI agents against the customer’s real data, real workflows, and real compliance perimeter.

Palantir invented the role around 2003 to serve agencies that couldn’t write a coherent requirements doc. Their co-founder Shyam Sankar has a line about this that the whole industry is now quoting: “If a problem could be solved with a requirements document, it would have been solved already.”

That is the FDE thesis in one sentence. And it is why every frontier lab is now copying the Palantir playbook with their checkbook.

The work itself is unglamorous. Mostly you are mapping fields between an Oracle database that hasn’t been touched since 2014 and a Claude API that’s three weeks old; writing eval harnesses; sitting in a windowless room with the customer’s CISO trying to explain why a probabilistic model can’t be wrapped in a try/except.

Of the work an FDE does in their first year at a customer — maybe 15% of it is anything you’d recognize as “AI engineering.” The rest is the connective tissue. The plumbing. The diplomacy. The dirty work.

The labs are paying a quarter-million dollar premium over a comparable L6 platform job because almost nobody on their existing payroll has the temperament to do it.

Why this market exists at all

MIT NANDA’s 2024 study — 95% of enterprise GenAI pilots produced no measurable business value.

That’s the punchline. The frontier model layer is extraordinary. The integration layer between the model and a real business is broken in ways the labs can’t fix from headquarters.

Sanchit Gogia at Greyhound Research has the cleanest framing I’ve read: “The Forward Deployed Engineer is the invoice for making AI real.” For a year that deploying an agent looks more like hiring an employee than installing software.

That is a structural observation about why AI delivery is currently labor-bound.

So we have a clean setup: a $500B+ enterprise opportunity gated by a workforce that doesn’t exist yet, and frontier labs willing to pay almost any number to lock up the few people who can do the work. This is what a real talent arbitrage looks like.

BUT, here is an interesting twist: this arbitrage closes.

Gartner analyst Alex Coqueiro has put a timer on it. By 2028, his estimate is that 70% of FDE-led enterprise agent projects will be abandoned

What this means in practice: there is a 24-to-36 month window in which the FDE role is being mispriced by the labs as a frontier engineering hire because they desperately need bodies.

After that, the role compresses into something that looks much more like high-end offshore systems integration at a quarter of today’s comp band.

Get in while the curve is inflecting, or accept that the seat will close.

Three doors

Door 1: The big-tech engineer who just got cut

This is the door I told my friend to walk through.

You spent the last 8-12 years at big tech, or one of the second-tier survivors. You owned infrastructure that fifteen thousand engineers depended on, or you ran a service that touched three billion users.

Here is the inventory you don’t realize you have:

• Production discipline. You know what 99.95% actually means when a paying customer is reading the SLA. Most lab-trained AI engineers do not have this in their bones.
• Cross-system debugging. The FDE job is essentially following a request across seven services owned by four teams to find where the agent’s tool call is silently failing. You’ve done this for a decade.
• Spec writing, code review, on-call. The labs need adults who can run a delivery team inside a customer’s security perimeter. There are not enough of them anywhere.
• AI tooling fluency. You already write code with an LLM in the loop. You understand context windows. You’ve debugged a prompt that worked yesterday and broke today.

Here is what you’re missing:

• Vertical context. You built generic platform tools or consumer products. You’ve never had to explain to a compliance officer at a regional bank why an agent’s tool call retried three times and triggered a duplicate wire transfer.
• Customer skin. Most big-tech ICs above L5 have never sat in a room with an external customer for eight straight hours. The first time a CIO asks you a question you can’t answer in front of their CTO is going to feel awful.
• Production AI engineering. Using Claude Code to ship an agent that runs autonomously inside a regulated workflow lives somewhere else entirely — eval harnesses, guardrails, drift detection, human-in-the-loop checkpoints. The gap between “I use AI to write code” and “I build AI systems for someone else to run without me” is 6-8 months of focused work.

Entry price: a year of trading platform prestige for industry immersion. When Anthropic’s Applied AI team built the financial-crimes agent inside FIS, the bulk of the work was a streaming pipeline that silently dropped messages between core banking and the agent’s tool layer for two months before anyone caught it.

Trap: applying with the same resume frame that got you into Meta. Recruiters at OpenAI DeployCo and Anthropic Applied AI have screened thousands of ex-FAANG L6s in the last six months.

The ones who get through lead with a deployment. Even a six-week internal pilot showing you shipped an agent end-to-end at your last employer beats three years of perf reviews.

Door 2: The vertical expert with no code

You’re a credit risk officer at a regional bank, a clinical informaticist at a hospital system, an operations planner at an industrial supplier, a treasury operations lead at a midmarket asset manager.

You know exactly which 30 workflows in your industry are about to be eaten by agents — you live inside them.

Your entry price is the steepest of the three. You need to become technical enough that an FDE team trusts you on a production codebase.

Your moat is also the steepest. When OpenAI’s FDEs went into John Deere and got herbicide use down 70% on the precision recommendation agent, the limiting factor was knowing what a row-crop agronomist actually does in the cab in late June. You arrive on day one as that person. The engineers cannot.

Trap: Hiring managers want code in your hands and a real deployment on your resume — even a six-week internal pilot at your current employer.

Build one before you apply. Sankar at Palantir likes calling FDEs “heretics inside their own industries” — the ones who already see what’s broken and refuse to wait for permission to fix it. That’s the energy the role selects for.

Door 3: The new grad with no industry, no AI, no idea

You’re 22. CS degree, or econ, or chemistry. Your peers are split between people chasing Meta offers and people stalling in a master’s program because they can’t decide what’s next.

You think you have to choose between pure AI and pure industry. Walk through the third door instead.

FDE is the only career in tech where being undifferentiated at 22 is an actual advantage. You have no industry baggage to unlearn, no codebase loyalty, no opinion about how things “should” work.

Nexus dropped a team of exactly this profile into Orange Group across multiple European markets earlier this year — your only job for the first year is to learn faster than anyone else in the room.

Entry price: your early twenties belong to airports, customer conference rooms, and onboarding sessions for legacy enterprise software you’ll never use again.

You’ll be at a 200,000-employee insurance company learning how their broker hierarchy works while your friends are at a startup with a foosball table.

Trap: optimizing for prestige in year one. What compounds is the agent you shipped end-to-end inside Heineken or Nestlé. That stays on your résumé through three funding cycles.

The thing nobody puts in the job ad

I want to be honest about the parts the recruiters skip.

The first year is mostly not what you’d call AI work. You are cleaning other people’s data. You are negotiating with a CISO who doesn’t trust your model. Some FDEs burn out at the 18-month mark for exactly this reason.

The labs already know this. IBM’s Forward Deployed Units are an early attempt to engineer around it — Mohamad Ali, who runs IBM Consulting, has been explicit that the FDU structure is meant to dilute single-engineer dependency by routing repetitive scaffolding to agents while humans take the judgment calls.

Constellation Research’s critique is also worth sitting with. If frontier AI companies are accidentally becoming consulting firms, then the people doing the consulting are accumulating exactly the institutional knowledge that makes the labs work. Which means your three years of dirty work at DeployCo are also training you to be the next generation of lab leadership.

That is the compound.

Why now

The cleanest historical analog for this moment isn’t the LLM boom of 2023. It’s the cloud transition of 2008-2015.

The people who got rich on the cloud transition were the ones standing in two worlds — AWS-native and SAP-native at the same time. Integration was the value. Cloud was the commodity.

The window opened in May 2026. By my read, it stays open through roughly mid-2028 — long enough to do one or two real deployments and accumulate the moat.

After that, MCP-style standardization, agent-native delivery tooling, and offshore systems integrators compress the role into something much closer to managed-services pricing, and the comp band collapses.

You have about 30 months. Use them.

Pick a door. Pay the price. The compound is real.

References

Links current as of May 24, 2026. Where the canonical announcement is a press release, the corporate newsroom URL is given; where the canonical source is a research note behind a paywall, the publishing analyst firm’s domain is given so the reader can locate the underlying report.

[1] OpenAI. “Introducing the OpenAI Deployment Company.” OpenAI Blog, May 5, 2026. https://openai.com/index/openai-deployment-company/

[2] OpenAI. “OpenAI acquires Tomoro.” OpenAI Blog, May 2026. https://openai.com/index/openai-acquires-tomoro/ — see also Tomoro company background: https://tomoro.ai

[3] Anthropic. “Anthropic, Blackstone, and Goldman Sachs launch joint venture to deploy AI in financial services.” Anthropic News, May 7, 2026. https://www.anthropic.com/news

[4] FIS Global. “FIS and Anthropic deploy AI agent for financial crime investigation.” FIS Newsroom, May 2026. https://www.fisglobal.com/en/about-us/media-room

[5] Kurian, Thomas. “The pilot era is over.” LinkedIn post, May 11, 2026. https://www.linkedin.com/in/thomaskurian28/

[6] ServiceNow. “ServiceNow and Accenture launch joint AI deployment practice.” ServiceNow Newsroom, May 12, 2026. https://www.servicenow.com/company/media/press-room.html

[7] IBM. “IBM Consulting introduces Forward Deployed Units.” IBM Newsroom, May 14, 2026. https://newsroom.ibm.com — Mohamad Ali interview context: https://www.ibm.com/consulting

[8] LinkedIn Economic Graph. “Emerging Jobs Report, Q4 2025.” https://economicgraph.linkedin.com/research

[9] Sankar, Shyam. Palantir Investor Day remarks and recurring interview quote on FDE methodology. Palantir public commentary: https://www.palantir.com/newsroom/ — see also Sankar’s Acquired Podcast episode (2024): https://www.acquired.fm

[10] MIT NANDA / MIT Media Lab. “The GenAI Divide: State of AI in Business 2024.” 2024. https://nanda.media.mit.edu/ — coverage of the 95% finding: https://fortune.com/2024/08/ (search “MIT NANDA 95%”)

[11] Gogia, Sanchit. “The Forward Deployed Engineer is the invoice for making AI real.” Greyhound Research analyst note, Q1 2026. https://greyhoundresearch.com/

[12] Levie, Aaron. X (Twitter) thread on AI deployment as hiring, late 2024–2025. https://x.com/levie

[13] Coqueiro, Alex (Gartner). “Predicts 2026: 70% of enterprise agent projects will be abandoned by 2028.” Gartner research note, Q1 2026. https://www.gartner.com/en/newsroom

[14] Constellation Research. Analyst commentary on frontier AI labs operating as consulting businesses with software valuations. https://www.constellationr.com/blog-news

[15] Meta Platforms. “Year of Efficiency” and subsequent 2025-2026 perf cycle communications, summarized across Q4 2025 earnings call transcript and subsequent reporting. https://investor.atmeta.com

[16] John Deere & OpenAI. See & Spray precision-agriculture deployment case study, 2025. https://www.deere.com/en/news/ — herbicide-reduction figures: https://www.deere.com/en/sprayers/see-spray/

[17] Nexus / Orange Group. Customer-service agent deployment case study, Q1 2026. https://www.orange.com/en/newsroom and https://nexus.ai (case study)

[18] Anthropic. “Introducing the Model Context Protocol.” November 2024 (initial), ongoing standard. https://www.anthropic.com/news/model-context-protocol — spec: https://modelcontextprotocol.io

The post FDE: The hottest job in AI is the one no one at the lab wants to do appeared first on Felix Kang.

I’ve stopped mentoring juniors. Not because I don’t care—but because training AI is just… faster.

And I don’t think I’m alone.

The Part of the AI Story No One Talks About

This is the part of the AI story no one talks about. Everyone debates whether AI will “replace jobs.” However, the quieter shift is already happening: AI is replacing the mentorship that used to create those jobs in the first place.

We hired a junior employee a few months ago. Smart, eager, willing to learn. The kind of person who, five years ago, I would have spent hours mentoring—sitting in meetings together, walking through decks, reviewing their work line by line.

But I’ve noticed something.

The Time That Disappeared

I’m not spending that time anymore.

Every hour I spend giving feedback to a new hire, I find myself thinking: I could build this into a skill file for Claude and never explain it again.

The math keeps running in my head, even when I don’t want it to.

Training a person: repeat the same feedback weekly. Watch them make the same mistakes. Wait months for them to internalize patterns.

Training an AI: one feedback session. One skill file. Immediate execution. No drift. No emotional days.

Honestly, I don’t like this math. But I can’t unsee it.

The Silent Choice Every Manager Is Making

Every manager I talk to is quietly facing the same tension. They won’t say it out loud—it sounds cold. Nevertheless, the time they used to spend mentoring juniors is slowly migrating toward building AI workflows.

And this creates a problem no one is talking about.

Junior talent isn’t struggling because skills are harder to learn. If anything, AI made learning easier. You can teach yourself almost anything with YouTube and ChatGPT.

Instead, the struggle is different.

It’s that no one is investing in them anymore.

Why the Apprenticeship Model Is Breaking

The apprenticeship model—seniors spending years passing down intuition, correcting mistakes in real-time, building someone up through repetition—is quietly breaking. Not because seniors are cruel. Rather, it’s because the ROI equation changed.

When I spend 10 hours this week mentoring a new hire, I’m betting on a payoff 12 months from now. Maybe longer. Maybe never, if they leave.

In contrast, when I spend 2 hours building an AI workflow, I get the payoff today.

Most managers are making the same choice. They just won’t admit it.

Programmer: A Preview of What’s Coming

I keep watching what’s happening to software engineers. It feels like a preview.

For instance, junior developers are being squeezed out. Not because coding is harder—AI actually made it easier to write code. Instead, the squeeze is coming from a different direction.

Companies are realizing: Why hire someone who can learn to code, when I can hire an agent that already codes?

As a result, the entry-level pipeline is collapsing. Internships are disappearing. Meanwhile, the people still getting hired are architects—engineers who can design systems, not just write functions.

The same pattern is starting in sales. In product management. In marketing.

Specifically, the jobs that require “learning by doing under supervision” are exactly the jobs most vulnerable to this shift. Because the supervision is going away.

How the Career Ladder Broke

The old career ladder looked like this:

Junior (learn from seniors) → Mid-level (execute independently) → Senior (mentor others)

Each rung depended on the one above it. Seniors invested in juniors because they remembered being juniors. The system was self-reinforcing.

But AI broke the first rung.

If seniors stop investing in juniors—because AI is more efficient—then juniors never become mid-level. They get stuck. Or they leave. Or they’re never hired in the first place.

The ladder doesn’t disappear. Instead, the bottom rungs do.

What’s left is a jump: from “outsider” directly to “architect-level thinking.” No gradual climb. No hand-holding.

The New Hiring Reality

Companies aren’t hiring people who can be trained. Instead, they’re hiring people who already think like architects.

And if you’re a new professional, no one is coming to bridge that gap for you.

What Do You Do If You’re Early in Your Career?

I don’t have a neat answer. But I’ve been thinking about what I would tell myself if I were starting over today.

1) Stop waiting to be trained

The old playbook was: join a company, find a mentor, learn by watching and learning from others. That playbook assumed someone would invest in you.

Unfortunately, that assumption is increasingly wrong.

If you’re waiting for a senior to pull you aside and teach you the ropes, you might be waiting forever. After all, the people who used to do that are now building AI workflows instead.

The new default is self-driven. You teach yourself—using AI as your tutor, not your replacement.

2) Use AI to compress the learning curve, not to skip it

Here’s the trap I see junior people falling into: they use AI to do the work, instead of using AI to learn the work.

If you let Claude write your code, your emails, your analysis—you’ll ship faster. But you won’t build intuition.

The better move: use AI to explain why. Ask it to critique your thinking. Have it simulate a senior giving you feedback. Extract the mentorship you’re not getting from humans.

In short, AI can be your tutor. But only if you treat it as a learning partner, not an outsourcing machine.

3) Chase architecture problems, not execution tasks

The roles that are disappearing are the ones that say: “Do this task, then do the next one.”

In contrast, the roles that are growing are the ones that say: “Design the system. Decide what tasks should exist.”

If you’re early in your career, actively seek out architecture-level work—even if it’s not in your job description. Volunteer for projects where you define scope, not just deliver output. Push yourself into the uncomfortable zone where you’re making judgment calls, not following instructions.

Ultimately, the goal is to become someone who designs workflows—including workflows that involve AI—rather than someone who executes them.

4) Build in public. Create your own proof

The old career path gave you credentials through association. “I learned from so-and-so.” “I was trained at such-and-such company.”

If that path is closing, you need a new way to signal competence.

The answer is proof of work.

Ship side projects. Write about what you’re learning. Build a portfolio that shows your thinking, not just your resume.

When no one is recommending you, your work has to vouch for itself.

I want to be honest about something.

I’m not sure this is a better world.

The old apprenticeship model had inefficiencies. It was slow. It depended on the generosity of senior people. Not everyone had access to good mentors.

But it also worked. It built human capital. It created continuity. It gave people a way in.

What’s replacing it is faster and more efficient—but also colder. The burden of growth is shifting entirely onto the individual. Sink or swim. Figure it out yourself.

That’s not a moral judgment. It’s just what’s happening.

– Felix

The post The End of Learn from Your Senior – Why Mentorship Is Migrating to Machines appeared first on Felix Kang.

Everyone is worried about AI replacing the bottom 10% of the workforce. They are looking in the wrong direction.

Jason Lemkin (SaaStr) attended Lenny’s podcast, and he laid out a frightening new reality:

A team of 1.2 humans and 20 AI agents can now outperform a traditional sales team of 10 people.

For the last decade, the formula for scaling was simple: If you want $10M in ARR, you hire an army of SDRs to smash phones and send emails. It was a brute-force game.

This type of game is over.

The “Entry-Level SDR” is dead. Not dying—dead. The idea of paying a human to copy-paste emails or qualify leads is meaningless.

But the disruption goes deeper than just efficiency. It’s changing the nature of the sale itself.

Customers don’t want “relationships” with a mediocre sales rep who takes 24 hours to reply. They want answers. Immediate, technical, accurate answers. They want “Service-First Sales”

This isn’t just a “Sales” evolution. It is a compression event. The walls between Sales, Product, and Engineering are collapsing. And from the rubble, a new species is emerging.

The End of “Let Me Check with Product”

In the old world, the B2B sales cycle was like:

1. Client asks for a specific customization.
2. Sales says “I’ll check with Product.”
3. Product puts it on a roadmap for Q3.
4. Client walks away.

We accepted this inefficiency because building software was expensive. You couldn’t just “whip up” a custom dashboard for one prospect.

Today, if a client says, “I need this report to integrate with my weird legacy ERP,” you don’t call a PM. You don’t call an Engineer. You open your laptop, summon an agent, and you build it. Live. On the call.

It is the power brought by Lovable, V0, and Claude Code….

The cost of a “Customized Solution” has dropped to near zero.

This shifts the power dynamic entirely. The “Salesperson” who can only talk about the roadmap is useless. The “Architect” who can deploy a solution in real-time is godlike.

We are moving from a world of “Selling Promises” to a world of “Delivering Prototypes.” And to survive in this world, human charm isn’t enough. You have to be a builder.

The New Species: The AI Business Architect

So what do we call this person? The one who doesn’t just sell, but audit and build?

I call them the AI Business Architect.

This role is a fusion of three traditional jobs that used to be separate silos:

1. Salesperson: You still need to understand the client’s business pain. AI can’t read the room (yet).
2. Product Manager: You need to translate that pain into a system requirement, not just a feature request.
3. Technical Architect: You need to know how to orchestrate Agents, API calls, and workflows to solve the problem.

In the past, you needed three people to do this. The friction between them (“Sales sold vaporware again!”) was the cost of doing business.

Now, that friction is removed. One person, equipped with a fleet of agents, can traverse the entire stack.

This is why the “Social Salesperson”—the one whose primary skill is buying dinner and being charming—is dying. When a client has a technical problem, they don’t want a dinner. They want a solution. They prefer talking to an AI that knows the documentation perfectly over a human who has to “get back to you on that.”

The market is shifting from Relationship-Based Sales to Competence-Based Sales. And the ultimate competence is building the solution right in front of their eyes.

Don’t Wait to Be Disrupted. Do It Yourself.

So, what should you do on Monday morning?

Don’t hoard your anxiety scrolling X . And don’t blindly “learn AI tools” without a purpose.

You need to start disrupting your own job.

Most people are waiting. Waiting for the company to issue an “AI Policy.” Waiting for IT to buy ChatGPT Enterprise. Waiting for permission.

If you are waiting, you are losing.

Buy the AI yourself. Pay the $20/month. Treat it as your personal R&D budget.

Then, look at your workflow and ask: “How would I replace myself today?”

• That weekly report you write? Engineer an agent to write it.
• That client onboarding email sequence? Build a workflow to personalize it automatically.
• That demo data setup? Create a script to generate it in seconds.

This is not about “saving time.” This is about training your architectural capability.

By forcing yourself to automate your own job, you learn the boundaries of the technology. You learn where AI hallucinates, where it shines, and how to structure data so it understands.

You are effectively building the “AI Teammates” that will eventually replace the manual parts of your role. But because you built them, you become their manager. You upgrade yourself from “Worker” to “Architect.”

The person who starts building these personal agents today gains a Compound Knowledge Advantage. In six months, while your colleagues are still asking IT for permission, you will be operating as an Exponential Organization of one.

There is only one seat at the table for the person who controls the agents. Make sure it’s you.

The post Sales, Product, and Code are Now the Same Job appeared first on Felix Kang.

Apple worked because it rose in an era of mass media monoculture. Everyone watched the same TV channels. Everyone read the same papers. You could buy the world’s attention.

We are now living in the era of Tribal Warfare.

The global market is fracturing into infinite sub-cultures. There is no longer a “Mainstream.” There are only streams.

And in this fragmented world, the “Safe, Professional, Global Brand” is invisible.

Why? Because humans have evolved a new biological filter: The Anti-AI Defense Mechanism.

As an AI founder, I see the tsunami coming.

We are about to be flooded with perfect, polished, “professional” content generated by AI. Infinite mediocrity, delivered at infinite speed.

Our brains are already adapting.

When we see a perfect logo? We ignore it.

When we read a perfectly structured corporate post? We scroll past.

We assume it is fake. We assume it is a bot.

“Professionalism” is now a red flag.

The only things that will penetrate this filter are Flaws, Edges, and Pulse.

The brands that win the next decade will be the ones that aggressively serve a specific group—and aren’t afraid to alienate everyone else.

The new status symbol, in my eyes, is Human Error.

It is the ability to show the glitch, the stumble, and the raw texture of reality.

If you try to please everyone with a sterilized brand, you will be ignored by everyone.

I want to show you why your inability to be professional is actually your biggest competitive advantage.

1) Weaponized Transparency (The Mint Mobile Effect)

The Old Way:

Brands like AT&T and Verizon spend millions on “Super Bowl” ads with CGI dragons and celebrity cameos. They try to signal Status (“Look how rich and powerful we are”).

The New Way:

Ryan Reynolds signaled Solidarity (“Look how I’m saving you money”).

Instead of shooting a $5 million commercial, Ryan famously shot an ad using a PowerPoint presentation and a screen recording. He explicitly told the audience: “We spent $500 on this ad so we don’t have to raise your prices.”

He didn’t just “act” like a friend; he exposed the unit economics of the industry. He treated the customer as an insider who understands that a fancy ad = a higher phone bill.

The Lesson:

Don’t just be “transparent.” Weaponize your constraints.

Show the cheap set. Show the messy script. When you expose the “scam” of high-production marketing, you instantly position yourself as the only honest player in the room.

2) Hostility over Politeness (The dbrand Effect)

The Old Way:

“The customer is always right.”

Corporate brands are terrified of offending anyone. Their social media is run by a committee that apologizes for everything. They sound like a customer service bot.

The New Way:

dbrand (a company that sells phone skins) decided to be the Anti-Bot.

Go to their Twitter. They don’t apologize. They roast their customers.

If you complain about their product being “just a piece of tape,” they agree with you and call you an idiot for buying it.

When Sony threatened to sue them for making PlayStation faceplates, dbrand didn’t issue a polite press release. They released a new line called “Darkplates” and put “Go Ahead, Sue Us” on the homepage.

The Lesson:

Politeness feels robotic. Sass feels human.

When a brand has the guts to fight back or make fun of its users, it signals confidence.

It proves there is a human behind the keyboard, not a LLM trained on “safety guidelines.”

3) Strategic Suicide (The Nike Effect)

The Old Way:

The cardinal rule of business was “Republicans buy sneakers, too.” (Michael Jordan). Brands tried to be water—formless, odorless, and offensive to no one.

The New Way:

Nike decided to be fire.

When Nike made Colin Kaepernick the face of their campaign, they knew exactly what would happen. People burned their shoes on YouTube. Their stock dipped. The media screamed “Disaster.”

But Nike ran the math. They knew their growth wasn’t coming from the angry boomers burning shoes; it was coming from the youth who wanted a brand with a spine.

Result: Online sales jumped 31% in the days following the controversy.

The Lesson:

If you aren’t generating hate, you aren’t generating love. You are generating indifference.

A “Living Brand” has enemies. It has a moral compass. It is willing to commit “Strategic Suicide” with one group to lock in “Eternal Loyalty” with another.

The “Human-First” Operating System

If I were advising a business owner today on how to survive the next 10 years, I wouldn’t tell them to buy more AI tools. I would tell them to execute these 3 strategic shifts.

1) The “Founder-Led” Strategy

If your website “About Us” page is a stock photo of people shaking hands, delete it.

Stop hiding behind the “Company We.” Start leading with the “Founder I.”

• For the CEO: you are the Chief Storyteller. You don’t need to be an influencer, but you need to be present.
• The Action: Once a week, record a 2-minute video on your phone. No script. No studio lighting. Just you, talking about why you built this feature, or what kept you up last night about the industry.
• The Logic: High production value signals “Marketing Budget.” Low production value signals “Truth.”

2) Define The “Anti-Persona” (The Exclusion Strategy)

Most businesses are terrified of losing a sale. So they write copy that appeals to everyone.

“We help businesses grow.” (Boring. AI-generated.)

In a noisy world, safety is dangerous. You need to signal exactly who you are not for.

Don’t just define your Target Audience. Define your Anti-Audience.

• The Action: Create a “Who this is NOT for” section on your landing page or in your content.
  • “If you want a quick fix, do not buy this.”
  • “If you care more about cheap prices than fair wages, we are not for you.”
  • “If you want to outsource your thinking to AI, look elsewhere.”
• The Logic: When you actively push away the wrong people, the right people trust you instantly. It shows you have standards. It shows you are real.

3) The “Open Kitchen” Policy (Process as Content)

The open kitchen feels safer. It feels honest. You can see the ingredients.

Treat your process as your “marketing asset.”

• The Action:
  • Don’t just announce the product launch; publish the sketches that led to it.
  • Don’t just show the success case study; show the problem you struggled to solve for a month.
  • Share your roadmap. Share your philosophy. Share the internal slack message (screenshot) where the team celebrated a small win.
• The Logic: In an age of deepfakes and scams, transparency is the ultimate currency. Showing how the sausage is made proves that it’s real meat, not synthetic filler.

The End of the “Silent Factory” Era

If you are building a global brand, listen closely.

For the last 20 years, you won because you were faster, cheaper, and more efficient. You won on Supply Chain.

But in the next decades, AI will democratize efficiency. Everyone will be fast. Everyone will be cheap.

When the price of “good enough” drops to zero, the only moat left is Trust.

The market will distrust you because you are faceless.

They don’t want another perfect, soulless brand that looks like it was generated by a template. They want to know who is behind the curtain.

So, here is my final bet:

The next unicorn won’t be the company with the most polished language or the most expensive PR firm.

It will be the company that has the guts to drop the mask.

Stop trying to look like a Fortune 500 company.

Start acting like a human being worth following.

The supply chain era is over.

The humanity era has begun.

Your move.

– Felix

The post How to beat AI at its own game (the rise of the “Human Glitch” and the end of the perfect brand） appeared first on Felix Kang.

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.

• Product is the problem you solve.
• Team is the talent you assemble to solve it.
• Resources are the fuel—capital, brand, and distribution.
• Culture is the operating system that governs how everything runs.

Recently, I’ve gained a deeper appreciation for the role of management systems and culture.

I’ve come to see its evolution in three distinct stages, best measured by how the team experiences its leader.

Stage 1: The System — The CEO is Feared (or at least, closely watched)

In the beginning, the founder’s will is everything. You have a unique insight, and you drive progress through sheer force and tight control. The product is the only thing that matters.

But once you find product-market fit, the company has to grow beyond you. This is the awkward stage of scaling. You introduce formal management: departments, levels, OKRs, KPIs. It’s a heavy, clunky scaffolding, but it’s necessary to stop things from breaking.

In this phase, the CEO’s job is to be the system’s enforcer. You’re in the trenches, driving execution, and making unpopular decisions to instill discipline. The team might not love you for it, but they follow the rules because you’re watching. Leadership is direct, visible, and top-down. It works, but it doesn’t scale.

Stage 2: The Habits — The CEO is Respected

The management system doesn’t change much between Stage 1 and 2. What changes is the team’s behavior. The rules on paper start to become rituals in practice.

Culture emerges in the small, consistent actions that need no top-down command:

• Every meeting ends with clear action items and owners.
• Product mocks are debated based on customer delight, not just short-term metrics.

But here’s the trap: many assume culture comes simply from hiring for perfect alignment. In reality, very few candidates are born fully compatible with company values. More often, people bring different strengths and weaknesses:

• Someone executes with precision but lack first-principles thinking.
• Someone thinks deeply but dislike tedious execution.
• Human nature is hard to change. No amount of lectures, rules, or KPIs can rewrite someone’s core traits.

Your job isn’t to change their nature. It’s to shape the environment. You create a feedback loop—rewarding aligned behaviors, course-correcting misaligned ones—until the desired habits become muscle memory. The team follows the norms not because the CEO is watching, but because the team is watching.

Stage 3: The Values — The CEO is Invisible

The final stage is the hardest to reach. Here, the CEO’s presence is barely felt in day-to-day operations. The company runs on a deeply embedded set of values that function like an immune system, automatically rejecting behaviors that don’t fit and nurturing those that do.

It takes years to reach this stage.

A vivid example comes from Anker, the global leader in charging accessories (even Trump has used their power banks). At one point, Anker had over 80 versions of power banks list on their store. Quantity diluted efficiency, and despite repeated warnings, the pattern persisted.

The CEO,Steven Yang wanted an Apple-like focus: fewer, better products. But commands, OKRs, and KPIs all failed. For an individual product manager, shipping more SKUs was a rational way to de-risk their career and look busy.

To flip the logic, the CEO introduced a penalty mechanism:

• Each category had a products cap. If a team exceeded it, their bonuses were cut. Too many SKUs, and the bonus dropped to zero.

The initial friction was immense. Many PMs quit.

But the ones who stayed believed in the philosophy of “less is more.” The culture shifted almost overnight. The wasteful 80-product problem vanished because the system was now wired to reward focus. The CEO didn’t need to approve every product decision anymore; the culture did it for him.

It is a process from stage 1 to stage 2, and he claimed that Anker yet enter stage 3.

When we look across these three stages, a pattern emerges:

• Stage 1 depends on systems and management.
• Stage 2 depends on habits and shared practices.
• Stage 3 depends on values embedded so deeply that they function as an invisible operating system.

This is the paradox of building: the more invisible the CEO becomes, the more powerful the company becomes. The less you rely on top-down control, the more the organization becomes self-sustaining.

Culture isn’t the soft stuff you deal with after you’ve figured out product and growth. It’s the hardest system you will ever build—and the only one that truly scales. In an era where AI can automate workflows, a dense culture is what automates decisions. One offers efficiency; the other builds an enduring company.

The post How to Manage a Company When the CEO Disappears: Culture Does appeared first on Felix Kang.

Think of it as an efficiency multiplier, not a ground-up revolution.

Our feeds on X (Twitter) have been saturated with news of AI funding, flashy new features, and “auto-agents” promising to revolutionize everything. But let’s be honest, most of these dazzling demos or consumer-focused tools don’t translate to real-world enterprise application. They might solve small, specific problems for end-users, but they often fall flat when confronted with the complex, multifaceted needs of large organizations.

The Real AI Frontier Isn’t in Tech

The unmet needs within enterprises are staggering, and they’re not confined to the tech sector. The intelligent upgrade of operations is a must-do for every businesses in next 10 to 15 years. Yet, the majority of AI developers remains focused on the tech fields. The truly massive opportunities, however, remain largely untapped in sectors like manufacturing, healthcare, retail, and agriculture.

Take Tyson Foods, for example. The largest chicken company in the U.S. is investing a colossal $1.3 billion by 2025 in automation, with a strong emphasis on AI analytics, robotics, and intelligent logistics. What’s more, they’re committing $50 million in 2025 alone to upskilling and educating their workforce.

This isn’t an isolated incident. Many traditional industries are deep in the trenches of significant digital investments, and they possess a powerful, growing appetite for intelligent solutions. As these non-tech sectors truly embrace AI, an enormous, dormant market will be unleashed, driving both a boom in practical AI product development and a dramatic surge in AI-related employment.

Where Enterprises Really Are with AI

Here’s the often-overlooked reality of current enterprise digitalization: these companies aren’t shopping for another “cool” feature.

The far more common scenario is they’ve already sunk millions into CRM or ERP systems, yet only 20% of the functionality ever gets used. The other 80%? Often too complex or simply unknown to employees. Here’s where AI truly shines: it’s the solution to unlock that massive, unused potential.

When I talk to decision-makers, it’s clear their understanding of AI is still largely conceptual. They think chatbots, basic Q&A, and rudimentary knowledge bases. Even with exposure to advanced use cases, when faced with their own operational bottlenecks, they’re often paralyzed. There’s a glaring lack of in-house AI expertise to guide them, and the perceived integration risk for agents is sky-high. So, they default to throwing more human capital at the problem.

Our Playbook for Enterprise AI Adoption

How do we cut through the noise and get enterprises to truly understand and embrace AI services?

We’ve heavily invested in user cognitive alignment. Before they even get hands-on with our product, we ensure they have a crystal-clear understanding of how our AI ‘workers’ function, why integration is frictionless, and why our solutions are inherently robust and reliable.

This strategic focus is the core of our sales methodology: “Cognition Build – Pain Point Identification – Solution Mapping.” This guided approach significantly accelerates sales cycles and empowers confident decision-making:

• Direct Visual Comparisons: We create side-by-side videos illustrating a specific workflow: the human-driven steps versus the AI-powered steps. It’s incredibly impactful and easy to digest.

• Collaborative Pain Point Mapping: We sit down with clients, helping them articulate their standard operating procedures (SOPs) and identify critical inefficiencies. This empowers them to discover their own bottlenecks.

• Targeted AI Interventions: Once they’ve recognized their inefficiencies, we present tailored AI services designed to optimize those specific problem areas. It’s about solving the problems they know they have.

A Prime Example: AI Enhancing Sales Workflows

Let’s look at a concrete example of AI seamlessly integrating with existing workflows: AI sales services, like what Clay is doing. They’re not trying to replace sales reps. Instead, they’ve streamlined lead qualification, leaving the final outreach and negotiation to the human touch.

Clay developed an AI research agent called “Claygent,” essentially an AI + SDR Agent. This tool lets users build customized data sources and rich workflows tailored to their needs, helping businesses scour the web for prospective client information.

The workflow with Clay is incredibly simple, just three steps:

1. Retrieve and acquire data.
2. Verify and provide sources.
3. Output the retrieved results in a specified format.

This doesn’t disrupt how sales teams operate; it follows their existing workflow. Sales personnel don’t need to relearn anything when using it, making it easier to measure value through quantifiable service effects. This makes it a no-brainer for enterprises to adopt.

Consider the cost: a single sales lead, if generated by a human sales rep (or customer service), might cost around $37.50. With an AI sales agent, that cost drops to a mere $0.69. No workflow changes, no team adjustments, and instant, tangible results. This is the sweet spot that enterprises are most keen to invest in right now.

The post The Enterprise AI Sweet Spot: Unlocking 80% of Untapped Efficiency appeared first on Felix Kang.

Russell argues that the glorification of hard work is a “slave morality” and that modern society no longer needs slaves. He divides work into two types:

1. Physical labor—arduous and poorly paid.
2. Managing or persuading others—comfortable and well-paid.

The latter category, which includes politics and executive roles, thrives on persuasion rather than deep expertise. Success here depends on marketing oneself, not mastering a domain.

Russell claims there’s no justification for denying most people leisure. The idea that humans wouldn’t know how to fill their time if they worked only four hours a day reflects poorly on modern civilization. Historically, people knew how to enjoy leisure—until efficiency became a cult.

Russell makes an interesting claim: if we’d kept the efficiency methods from World War II, we could all be working four-hour days by now. Instead, we went back to the old system where some people work too much and others can’t find work at all.

But there’s a flaw in this argument. Those efficiency methods didn’t appear out of nowhere. They were created by people working hard under pressure. This is how innovation always happens. Look at tech today—companies like OpenAI, Anthropic, Google, DeepSeek release major breakthroughs every few months precisely because people are pushing hard, not because they’re working less.

The real question isn’t “how little can we work?” but “how can we make work meaningful?”

I definitely agree that modern society should provide more leisure. And modern urban people have forgotten how to use leisure well. Today’s urban leisure has become passive – dining out, watching movies, attending sports games. You rarely see spontaneous folk dances anymore except in remote villages. Yet that same creative impulse still lives in us.

With true leisure time (not just time to recover from exhaustion), we might create new art forms – maybe not traditional dances, but experimental games, boundary-pushing music, or something crazy (in positive way)

For the past two years, I’ve worked 10-12 hour/day. I lose a lot leisure time. There is no doubt that my life is not balanced, but the process has given me deep industry knowledge and unexpected innovations.

What if I have 8 hours leisure time every day? I might find my true interests, spend my effort and energy into creative pursuits. In my case, it can be literature or Guitar (I haven’t played for almost a year, it is right next to me bed)

Just like how Paul graham discuss his attitude about the work:

Don’t let “work” mean something other people tell you to do. If you do manage to do great work one day, it will probably be on a project of your own. It may be within some bigger project, but you’ll be driving your part of it. choose a field, learn enough to get to the frontier, notice gaps, explore promising ones. This is how practically everyone who’s done great work has done it, from painters to physicists.

The post Work, Leisure, and the Paradox of Innovation appeared first on Felix Kang.

In 2024, we’re witnessing unprecedented changes in AI that are reshaping how we work and create.

1. Knowledge Compounds Faster Than Ever: We’re in a “compressed century” where the next decade will pack in the technological progress that would have normally taken 100 years. From AI breakthroughs to medical innovation, software & hardware application evolvement… Keeping up with new technologies isn’t just beneficial – it’s essential for thriving in this rapidly evolving landscape.
2. Focus on Industry-Specific AI Solutions: Instead of chasing the next “0 to 1” breakthrough, focus on vertical AI opportunities. A new blue ocean is emerging as AI becomes essential at both business and personal levels – soon individuals will need specialized AI tools just like businesses need SaaS today. This dual market of business and personal AI solutions could be 10x larger than traditional SaaS.
3. Don’t Settle for Niche Success: While focusing on vertical AI opportunities is essential, don’t let success in a specific niche lull you into complacency. Instead, focus on building “technical compound interest”—a foundation of skills, knowledge, and iterative progress that multiplies over time. AI has significantly lowered the cost and time required for experimentation, enabling you to explore new possibilities and future-proof your efforts.
4. Redefining Roles in the AI Era: Tech giants are reshaping their teams, hiring fewer traditional engineers as routine programming tasks are increasingly handled by AI software engineers like Devin . AI is disrupting existing job functions while creating a demand for individuals who can think broadly and connect dots across disciplines. This shift presents a unique opportunity for everyone—engineers, product managers, marketers, and sales professionals alike—to reinvent themselves and explore new breakthroughs across all roles.
5. Early Movers Win: We’re at a technological turning point. Those who adapt and move early will reap the rewards.

Building and Growing with Clarity

Building something meaningful takes both strategy and patience.

1. Honor the Role of Time: Foundational elements like building a cohesive team and cultivating deep technical expertise cannot be rushed. Respect the time it takes to develop these pillars of success.
2. Start Small, Then Scale: Begin with small, innovative steps to capture the market opportunities. Once you achieve PMF, scale up decisively.
3. Clarity Drives Motivation: A clear and tangible goal inspires your team far more effectively than complex incentive structures. Use product metrics and authentic user feedback to guide your efforts.
4. Rethink Brand Building: Avoid draining resources on endless events and generic content. Instead, leverage trending topics, social momentum, and timely opportunities to amplify your brand’s reach.

Personal Growth in a Time of Transformation

The Essence of Growth Remains Unchanged. Even as AI and technology advance, personal growth continues to stem from exploring possibilities, nurturing balance, and breaking through self-imposed barriers.

1. Break Self-Imposed Limits: Don’t let your job title or background restrict your exploration. Whether exploring AI, science, or crypto, move beyond surface-level buzzwords and dive into the fundamental principles that drive these fields. True growth comes from understanding the essence of new domains.
2. Find Internal Balance: Happiness and fulfillment shouldn’t hinge on external validation. Practices like meditation have helped me better understand myself and my place in this changing world.
3. Foster Cross-Disciplinary Thinking: Some of the most groundbreaking ideas arise at the intersection of disciplines. Stay curious, embrace diverse perspectives, and connect seemingly unrelated dots to spark innovation.

2025 will be a year to take those insights further, to refine what’s been started, and to keep moving forward with clarity and purpose. There’s a lot to look forward to.

The post Navigating The Inflection Point 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.