Video summary

Highest Paying AI Careers No One is Talking About (₹30 LPA Jobs)

Main summary

Key takeaways

Business

Business focus: why ad tech can pay 3–4x more than traditional tech

The video argues that ad tech is “hard mode” AI + distributed systems—involving:

  • Ultra-low latency decisioning
  • Massive real-time auctions
  • Feedback loops that continuously improve models

It also claims that a “fresher in India” can reach ~₹30 LPA, tied specifically to ad-tech roles and companies.

Core ad-tech “AI” system (real-time pipeline)

Ad decisions are made before the page/app loads, described as end-to-end < 500 ms.

What happens in the auction (publisher ↔ advertiser ecosystem)

  • Publisher side

    • Supplies ad inventory (ad slots)
    • Runs an auction to maximize revenue

  • Exchange

    • Auctions the opportunity to competing demand partners

  • DSP (Demand-Side Platform) (advertisers)

    • Uses ML to predict:
      • Whether a user will have intent to buy (intent prediction)
      • Which advertiser should be shown (smart routing)
      • Bid optimization / expected value (bidding strategy)

  • Winning creative

    • Is rendered
    • Advertiser is charged
    • Publisher gets paid

  • Post-ad outcomes

    • Click / purchase / attribution results are fed back
    • Improves future predictions

Examples / named case

The video mentions OpenAI running ads and partnering with ad-tech providers such as Criteo for:

  • Measurement
  • Bidding / optimization
  • Commerce-related data signals

The two-sided “full-stack” advantage (example company)

The video highlights an Indian ad-tech company (implied to be InMobi, though the name is garbled in subtitles) that operates both sides:

  • Exchange-like capability (publisher monetization)
  • DSP-like capability (advertiser buying)

Reported scale & metrics (from “Google Cloud public case study”)

  • ~7 million requests/sec served by exchange infrastructure
  • ~4 million bids/sec processed via a DSP AI engine (“Helix” mentioned)
  • >50,000 apps used for behavioral signals (user profiling)
  • Real-time profile created in milliseconds (behavioral signals network)
  • Pipeline: < 500 ms end-to-end
  • Peak scaling: up to 7.5 lakh compute cores (Black Friday / Cyber Monday mentioned)
  • Availability: 99.99%
  • ML inference: “more than 50 million ML inferences per day” (as stated)

Organizational / data advantage claims

  • SDK coverage described as being on billions of devices (claimed >3 billion devices)
  • Competitors allegedly struggle to replicate distribution; the differentiator is:
    • distribution + intelligence + full-stack learning loop

“Glance” flywheel (first-party intent + advertising monetization)

A key business thesis: independent players can survive by creating their own consumer surfaces to generate first-party commerce intent signals, improving ad targeting and performance.

Product strategy: AI-native shopping agent

A platform/surface called Glance is described as:

  • Embedded on devices (Samsung TVs, Google TV, OEM integrations mentioned)
  • Available across multiple devices and countries

Glance AI is described as a personalized shopping experience where users:

  • Upload a selfie and see themselves in different outfits
  • Swipe and use visual try-ons
  • Generate high-signal intent data (shopping actions as signals)

Closed-loop economics (“flywheel”)

  • More intent signals → smarter ad engine → better advertiser returns
  • More advertisers spend → surface grows → more intent data

Creative / commerce monetization shift

The video contrasts older AI monetization (subscriptions) with advertising:

  • Downloads of generative AI apps: crossed 1.5B by 2024 (doubling YoY claimed)
  • Ad-supported monetization is positioned as the scalable model

Advertising as the next layer for AI-native apps (GTM / partner strategy)

The video claims AI-native apps won’t always build ad systems from scratch due to:

  • measurement complexity
  • platform maturity requirements

It states AI ad rates may have dropped (example given):

  • CPM drop from $50–$60 to $20–$30 over “last three months”

Yet it asserts ads remain:

  • 3–5x more expensive than mature platforms

Strategy playbook implied

  • Option A: build internal ad infrastructure (slow, hard, measurement-dependent)
  • Option B: partner with established ad-tech platforms (faster time-to-market)

Example given: OpenAI partnered with Criteo.

Hiring / role “playbook” inside ad tech (what companies need)

The video outlines 4 booming role categories directly tied to the system above.

  1. Machine Learning & Data Science

    • Builds:
      • click probability / purchase probability
      • ranking
      • bid optimization
    • Optimization/test loop runs in milliseconds per auction
    • Mentions hiring from IITs / IISc / ISI, plus lateral hiring

  2. Distributed Systems Engineer

    • Owns low-latency, large-scale pipeline requirements:
      • millions of requests/sec
      • strict time constraints
      • backend infrastructure for the ~500 ms SLA
    • Focus: data engineering and systems reliability

  3. Data Engineering (marketed as underrated)

    • Runs real-time + batch pipelines
    • Claims petabyte-scale daily processing and 100s of production pipelines

  4. Foundation for AI Engineering / AI-native services & agentic commerce

    • Builds AI agents and commerce experiences (e.g., Glance AI)
    • Key challenge: make generative visuals feasible at scale (cost reduction)

Concrete technical example (agentic commerce scalability)

The video claims Glance partnered with NVIDIA and used optimizations including:

  • RTX Pro / Blackwell
  • “TSRT optimizations”

Asserted speedups:

  • Image generation: 20X faster
  • Video generation: 16X faster

Also highlights engineering constraints:

  • GPU scheduling
  • memory management
  • kernel-level engineering

Key frameworks / “mental models” explicitly emphasized

  • Real-time auction & feedback loop

    • outcome tracking → improved targeting/ranking/bidding

  • Flywheel

    • distribution + first-party intent + advertiser ROI loop:
      • intent signals → better ads → better returns → more spend → bigger surface → more intent

  • Two-sided full-stack positioning

    • exchange (publisher inventory) + DSP (advertiser demand)
    • enables learning from end-to-end outcomes

Presenters / sources mentioned

  • Presenter: Nishant Chahar

    • ex-Microsoft software engineer
    • “built and sold one company”
    • currently fundraising to start another

  • Company / source references:

    • OpenAI (ad partnership example; ads for free users)
    • Criteo (measurement/partner mentioned)
    • Google Cloud (source of large-scale numbers/case study)
    • NVIDIA (partnership for faster image/video generation)
    • Ecosystem/investors referenced: Google, Peter Thiel, Mithril Capital, Reliance Jio

Original video