Ingeun Kim

코인 과세 유예는 끝났다, 국세청이 당신의 지갑을 터는 완벽한 시나리오 대중은 선거철마다 정치인들이 던져주는 "코인 과세 유예"라는 달콤한 마약에 취해, 세금은 영원히 남의 일일 거라 착각한다. 하지만 현실은 냉혹하다. 정부는 당신의 시선이 비트코인 10만 불을 향해 있을 때, 조용히 당신의 목에 칼을 들이밀 '단두대'를 완성해가고 있다. 1. 팩트체크: 시스템은 이미 돌기 시작했다 오늘(11일) 국세청이 조달청에 띄운 '가상자산 통합분석 시스템' 입찰 공고를 보자. 이건 단순한 협박용이 아니다. 업비트, 빗썸에서 뽑아낸 거래 내역은 기본이고, 해외 거래소 신고 자료, 심지어 당신의 개인지갑(메타마스크 등) 주소까지 싹 다 긁어모아 블록체인 '온체인 데이터'와 퓨전시키는 괴물 같은 시스템이다. 당신이 크립토판에서 온갖 노력으로 얻어낸 수익, 해외 거래소로 몰래 빼돌렸다고 안심하던 자금 흐름까지 최대 5년 치가 국세청 모니터 클릭 한 번에 엑셀로 뽑혀 나오는 시대가 내년 1월에 열린다. 2. 에어드랍과 스테이킹: 숨 쉬는 것까지 과세한다 가장 소름 돋는 건 과세의 '범위'다. 단순히 코인을 사고팔아 남긴 차익만 털어가는 게 아니다. 당신이 디파이(DeFi)에 예치해서 받은 스테이킹 이자, 공짜로 받은 에어드랍(Airdrop) 코인까지 싹 다 '기타 소득'으로 간주해 22%의 세금을 뜯어낼 준비를 하고 있다. 이 바닥에서 살아남으려 발버둥 치며 만들어낸 모든 온체인 활동이, 그들에겐 그저 살찌운 돼지를 도축하는 '세수 확보' 이벤트일 뿐이다. 3. 주식은 구제하고, 코인은 쥐어짠다 (기울어진 운동장) 더 끔찍한 건 '손실 이월 공제'가 안 된다는 거다. 작년에 코인으로 1억 원을 잃고, 올해 1천만 원을 벌었다고 치자. 상식적으론 9천만 원 적자지만, 국세청 시스템은 올해 번 1천만 원에 대해서만 칼같이 22%의 세금 고지서를 날린다. 주식 투자자는 보호하면서 코인 투자자는 철저하게 무형자산 취급하며 피를 말리는, 가장 완벽하고 합법적인 수탈 구조다. 대중이 "세금 내기 싫다", "형평성에 어긋난다"며 커뮤니티에 키보드로 분노를 쏟아낼 때, 시스템 구축의 톱니바퀴는 이미 돌아가기 시작했다. 복잡한 온체인 흐름을 스스로 소명하지 못하면, 수익보다 더 큰 세금 폭탄과 가산세를 맞고 시장에서 영원히 퇴출당할 것이다. 당신의 자산을 지킬 합법적인 탈출구와 구조를 미리 짜두지 않는다면, 내년 당신의 계좌는 가장 뼈아픈 방식으로 털릴 것이다. Keep going.

stop what you're doing and look at this image. each dot is 3.2 million people. 2,500 dots = 8.1 billion humans. the grey? 6.8 billion people who have never used AI. the green? 1.3 billion free chatbot users. the yellow? 15-35 million who pay for it. the red? that tiny sliver is us. you think the AI space is crowded because you're in an echo chamber of the 0.06%. the real world hasn't even started. wrote a full breakdown on the data, the opportunity, and 7 businesses you can build from this gap today:

A few random notes from claude coding quite a bit last few weeks. Coding workflow. Given the latest lift in LLM coding capability, like many others I rapidly went from about 80% manual+autocomplete coding and 20% agents in November to 80% agent coding and 20% edits+touchups in December. i.e. I really am mostly programming in English now, a bit sheepishly telling the LLM what code to write... in words. It hurts the ego a bit but the power to operate over software in large "code actions" is just too net useful, especially once you adapt to it, configure it, learn to use it, and wrap your head around what it can and cannot do. This is easily the biggest change to my basic coding workflow in ~2 decades of programming and it happened over the course of a few weeks. I'd expect something similar to be happening to well into double digit percent of engineers out there, while the awareness of it in the general population feels well into low single digit percent. IDEs/agent swarms/fallability. Both the "no need for IDE anymore" hype and the "agent swarm" hype is imo too much for right now. The models definitely still make mistakes and if you have any code you actually care about I would watch them like a hawk, in a nice large IDE on the side. The mistakes have changed a lot - they are not simple syntax errors anymore, they are subtle conceptual errors that a slightly sloppy, hasty junior dev might do. The most common category is that the models make wrong assumptions on your behalf and just run along with them without checking. They also don't manage their confusion, they don't seek clarifications, they don't surface inconsistencies, they don't present tradeoffs, they don't push back when they should, and they are still a little too sycophantic. Things get better in plan mode, but there is some need for a lightweight inline plan mode. They also really like to overcomplicate code and APIs, they bloat abstractions, they don't clean up dead code after themselves, etc. They will implement an inefficient, bloated, brittle construction over 1000 lines of code and it's up to you to be like "umm couldn't you just do this instead?" and they will be like "of course!" and immediately cut it down to 100 lines. They still sometimes change/remove comments and code they don't like or don't sufficiently understand as side effects, even if it is orthogonal to the task at hand. All of this happens despite a few simple attempts to fix it via instructions in CLAUDE . md. Despite all these issues, it is still a net huge improvement and it's very difficult to imagine going back to manual coding. TLDR everyone has their developing flow, my current is a small few CC sessions on the left in ghostty windows/tabs and an IDE on the right for viewing the code + manual edits. Tenacity. It's so interesting to watch an agent relentlessly work at something. They never get tired, they never get demoralized, they just keep going and trying things where a person would have given up long ago to fight another day. It's a "feel the AGI" moment to watch it struggle with something for a long time just to come out victorious 30 minutes later. You realize that stamina is a core bottleneck to work and that with LLMs in hand it has been dramatically increased. Speedups. It's not clear how to measure the "speedup" of LLM assistance. Certainly I feel net way faster at what I was going to do, but the main effect is that I do a lot more than I was going to do because 1) I can code up all kinds of things that just wouldn't have been worth coding before and 2) I can approach code that I couldn't work on before because of knowledge/skill issue. So certainly it's speedup, but it's possibly a lot more an expansion. Leverage. LLMs are exceptionally good at looping until they meet specific goals and this is where most of the "feel the AGI" magic is to be found. Don't tell it what to do, give it success criteria and watch it go. Get it to write tests first and then pass them. Put it in the loop with a browser MCP. Write the naive algorithm that is very likely correct first, then ask it to optimize it while preserving correctness. Change your approach from imperative to declarative to get the agents looping longer and gain leverage. Fun. I didn't anticipate that with agents programming feels *more* fun because a lot of the fill in the blanks drudgery is removed and what remains is the creative part. I also feel less blocked/stuck (which is not fun) and I experience a lot more courage because there's almost always a way to work hand in hand with it to make some positive progress. I have seen the opposite sentiment from other people too; LLM coding will split up engineers based on those who primarily liked coding and those who primarily liked building. Atrophy. I've already noticed that I am slowly starting to atrophy my ability to write code manually. Generation (writing code) and discrimination (reading code) are different capabilities in the brain. Largely due to all the little mostly syntactic details involved in programming, you can review code just fine even if you struggle to write it. Slopacolypse. I am bracing for 2026 as the year of the slopacolypse across all of github, substack, arxiv, X/instagram, and generally all digital media. We're also going to see a lot more AI hype productivity theater (is that even possible?), on the side of actual, real improvements. Questions. A few of the questions on my mind: - What happens to the "10X engineer" - the ratio of productivity between the mean and the max engineer? It's quite possible that this grows *a lot*. - Armed with LLMs, do generalists increasingly outperform specialists? LLMs are a lot better at fill in the blanks (the micro) than grand strategy (the macro). - What does LLM coding feel like in the future? Is it like playing StarCraft? Playing Factorio? Playing music? - How much of society is bottlenecked by digital knowledge work? TLDR Where does this leave us? LLM agent capabilities (Claude & Codex especially) have crossed some kind of threshold of coherence around December 2025 and caused a phase shift in software engineering and closely related. The intelligence part suddenly feels quite a bit ahead of all the rest of it - integrations (tools, knowledge), the necessity for new organizational workflows, processes, diffusion more generally. 2026 is going to be a high energy year as the industry metabolizes the new capability.