Yatin Bhalla

AI Product Manager(Aspiring) · Shipped 20+ AI tools 0→1 (Voice Matters, PM-Scenario-Lab, EcomLens-AI) · 7-yr Operator (3 retail brands) · BITSoM × Masai AI-PM ·

[Yatin Bhalla @yatinbhalla42]

South Korea is rolling out free, unlimited AI access to 52 million citizens via SK Telecom, KT and Kakao. Sounds generous, until the catch: 80% must run on domestic Korean models, not ChatGPT. This is a bet that whoever owns the model stack owns the next decade of infrastructure.

[Yatin Bhalla @yatinbhalla42]

@Gamer4He Solid habit. If you haven't hit it yet, add evals to your fundamentals list, how you actually judge if a model output is good enough to ship. Most AI PM failures trace back to unclear success criteria, not the model itself.

[Yatin Bhalla @yatinbhalla42]

PMs who treat AI as a research assistant will get outpaced by PMs who treat it as a research department. Same tools. Different leverage.

[Yatin Bhalla @yatinbhalla42]

Practical test: audit your calendar for a week. Anything that's "summarize X" or "format Y" is a candidate to hand to AI today. Anything that's "decide X" or "convince Y" is where the freed hours should go.

[Yatin Bhalla @yatinbhalla42]

The failure mode: PMs use AI to produce more decks instead of using the saved hours to talk to customers. AI made the busywork faster. Your job didn't get easier. It got narrower and higher stakes.

[Yatin Bhalla @yatinbhalla42]

Four tasks AI already does better than most PMs: writing PRDs from rough notes, summarizing user interviews, building comp analyses, drafting release notes. All information processing. None of it needed your judgment in the first place.

[Yatin Bhalla @yatinbhalla42]

What AI still can't do: decide which 2 of 12 requested features actually move the metric you're accountable for. That call needs context AI doesn't have. Org politics, customer trust, technical debt tradeoffs, timing.

[Yatin Bhalla @yatinbhalla42]

AI is not replacing product managers. It's replacing 40% of what they do all day. Here's the actual line between the two. A thread on what to hand off and what to hold onto:

[Yatin Bhalla @yatinbhalla42]

MiniMax H3 Max crossed a real threshold. It renders 15 seconds of video in about 10 seconds, faster than real time. That turns video generation from a batch job you wait on into a live stream you can hook up and never stop. Latency, not quality, is the next AI video race.

[Yatin Bhalla @yatinbhalla42]

Anthropic just announced a 25% increase to Claude Code limits. Read the fine print. The current boost is 50% and temporary, expiring Sept 14. Net result, your real limit drops about 17% from where it sits today. A raise can still be a cut if the baseline moved first.

[rari @0xwhrrari]

Google DeepMind Chief Scientist Jeff Dean:

"Coding is dead"

"Agents write it, review it, and deploy it
Humans just approve"

Agents → Workflows → Graphs → Autonomous Systems

Multi-agent workflows just built an operating system from a single prompt

It runs Doom

Research → Build → Review → Deploy

This 28-minute talk is better than most $1,000 AI engineering courses

Bookmark and watch it today

Then read the article on harness engineering below ↓

[Yatin Bhalla @yatinbhalla42]

@0xwhrrari Contract and evidence are the two most skipped parts of this. Teams build tools and permissions fine, but rarely define what counts as proof a task actually succeeded. That gap is why agents look great in demos and drift once they hit production.

[Yatin Bhalla @yatinbhalla42]

The AI PMs whose projects survive past 2027 won't be the ones who shipped fastest. They'll be the ones who tied metrics to money and behavior change from day one.

[Yatin Bhalla @yatinbhalla42]

If your eval suite only checks model accuracy, you're measuring the model, not the product. A feature can hit 95% accuracy and still fail if nobody's outcomes actually changed.

[Yatin Bhalla @yatinbhalla42]

Week 1 numbers lie. Everyone tries a new AI feature once out of curiosity. The number that predicts survival is whether people are still using it a month later, for the same job.

[Yatin Bhalla @yatinbhalla42]

3 numbers worth tracking for any AI feature: task success rate on real user inputs, not test prompts. Cost per successful outcome, not cost per API call. Retention past week 2, not week 1 adoption spikes.

[Yatin Bhalla @yatinbhalla42]

The fix: outcome metrics. Not how many people used the AI assistant. Instead, support tickets dropped 30% because of it. Track cost saved, revenue moved, time actually returned to the user.

[Yatin Bhalla @yatinbhalla42]

Most AI PMs still report output metrics. Features shipped. Pilots launched. Story points burned. None of that tells you if the thing is actually worth what it costs to run.

[Yatin Bhalla @yatinbhalla42]

Gartner says over 40% of agentic AI projects get scrapped by 2027. Most don't die from a bad model. They die from a metrics problem. Here's the shift that keeps AI features alive:

[Yatin Bhalla @yatinbhalla42]

Tencent just open sourced Hy4, a 770B parameter model with 49B active and 1M context, and it beats GPT-5.6 Sol on coding benchmarks. It ranks 5th globally on the Arena Code leaderboard. Weights are live on Hugging Face. The frontier is getting cheaper to reach every month.