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Showing posts with label ML. Show all posts
Showing posts with label ML. Show all posts

Wednesday, September 30, 2026

How GPU Prices Can Double While AI Gets Cheaper

 

GPU prices have doubled in the last six months, from $4.40 to $8.08 per GPU-hour. But AI prices are falling. How can that be?

Article content

It is not a simple answer. Higher GPU costs are likely to remain while the industry races to build out new data centers. Every component of the buildout is increasing in cost, from concrete to copper to credit.1 Above all, electricity remains the limiting factor, which Oracle is experiencing : last week it invoked force majeure on its New Mexico campus after the natural-gas pipeline feeding it slipped by six months.2

Demand continues to explode. Inference companies & model companies are posting record-setting growth, which emboldens investors to value them more richly, provide them more capital, & up the bidding on those limited GPU-hours, to generate more revenue. The flywheel spins.

Meanwhile, AI efficiency is improving just as rapidly. Claude Opus 5.5 costs 40% less to run than the previous model.3 OpenAI cut Luna 80% in July, then another 50% in September, presumably powered by margin innovation.4 The same benchmark threshold that cost $0.55 eighteen months ago now clears for $0.0015, a 377x reduction.5

But capital is more expensive. As rates have risen, the relationship that governed tech multiples has come undone. The 10-year Treasury has been the strongest historical predictor of tech valuations : higher rates, lower multiples. Over the full history the correlation runs −0.50. Over the last two years it has flipped to +0.39.6 Rates climbed from 3.63% to 5.18% while the NASDAQ rose roughly 78%. The market is no longer repricing tech on the cost of money. It is betting the growth math works out.

Article content

Today, the capital markets are fueling this torrid growth, betting the math works out positively. The metric that will ultimately matter is gross profit dollars per GPU-hour.7 As GPU costs increase, are efficiency gains outpacing them? Microsoft says it generates 90% more tokens per GPU year over year, concentrated in its smaller models.8 If that pace continues, the two curves are at least running neck & neck.

So for now, the industry seems to be holding both forces in balance.

Tuesday, March 17, 2026

Anthropic Just Doubled Claude's Usage Limits for Everyone

  Welcome back! Anthropic just doubled Claude's usage limits in a surprise weekend move, Elon Musk is admitting xAI needs to be rebuilt from scratch, Meta is reportedly preparing to cut 20% of its workforce to fund its AI bet, and a pet owner with no science background used four AI models to design a cancer vaccine that actually shrank his dog's tumor.


Anthropic Doubles Claude's Usage Limits Across Every Plan
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Anthropic dropped a surprise weekend announcement: double usage limits across all Claude plans for the next two weeks. The boost applies automatically outside peak hours, with no action needed. The company also made its full 1M-token context window generally available for Opus 4.6 and Sonnet 4.6 at no additional cost.

The details:

    Double Usage: 2x limits on Free, Pro, Max and Team plans for two weeks, applied automatically outside peak hours (weekdays 5am to 11am PST). 
    1M Context Window: Full 1M-token context now GA for Claude Opus 4.6 and Sonnet 4.6, enabling uploads of entire codebases and long-form documents. 
    No Price Increase: Both the usage expansion and the context window upgrade come at no extra cost. 
    All Tools Included: The promotion covers all Claude tools, not just the chat interface.

The 1M-token context window is the bigger move here. For developers and researchers working with large codebases or dense documentation, this changes the scope of what a single conversation with Claude can handle.


Musk Admits xAI "Was Not Built Right" As Rebuild Begins
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Elon Musk publicly stated that xAI "was not built right" and needs a ground-up rebuild. Nine of the original 11 co-founders have now departed, leaving only Manuel Kroiss and Ross Nordeen. The admission comes as SpaceX, which owns xAI, prepares for a public listing later this year.

The rebuild:

    Latest Departures: Zihang Dai and Guodong Zhang left the company, with Zhang reportedly blamed by Musk for Grok's coding shortfalls before exiting. 
    New Hires: xAI brought in senior Cursor engineers Andrew Milich and Jason Ginsberg, both reporting directly to Musk, to accelerate Grok's coding capabilities. 
    Coding Deficit: Musk has publicly acknowledged that Grok is "currently behind" on coding versus frontier competitors. 
    IPO Pressure: The full rebuild is happening while SpaceX prepares for one of the largest public listings in recent history.

Rebuilding from scratch while preparing for an IPO is an extraordinary challenge. Musk is betting that new talent and a clean architecture can close the gap with OpenAI and Anthropic, but the timeline to prove that is tightening.


Meta Reportedly Weighing Layoffs Affecting 20% Of Its Workforce
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Meta is reportedly considering layoffs that could cut 20% or more of its nearly 79,000 employees. The move would offset aggressive AI infrastructure spending, including $600 billion earmarked for data centres by 2028.

The pressure points:

    Potential Scale: A 20% cut would impact roughly 16,000 people across the company. 
    AI Investment: Meta has committed $600B to data centre buildout, one of the largest infrastructure bets in tech history. 
    Recent Acquisitions: The company's purchase of Manus adds to its AI portfolio and its cost base. 
    Official Position: A Meta spokesperson described the reporting as "speculative reporting about theoretical approaches."

Meta is making one of the biggest infrastructure bets in corporate history while potentially cutting the workforce needed to operate it. A 20% reduction is not efficiency trimming. It is a structural bet that AI can absorb the work.


AI-Designed Vaccine Shrinks Rescue Dog's Tumor
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Sydney AI consultant Paul Conyngham built a custom mRNA cancer vaccine for his dog Rosie by chaining ChatGPT, Grok, DeepMind's AlphaFold and a university genomics lab. One tumor shrank by half after the first injection.

How it worked:

    Diagnosis: Rosie was diagnosed with mast cell cancer in 2024 and given months to live after chemo and surgery failed. 
    AI Pipeline: Conyngham used ChatGPT to map the research, paid $3,000 for genomic sequencing and ran the data through AlphaFold to model mutations. 
    Vaccine Design: The UNSW RNA Institute helped produce the vaccine, with the final construct designed using Grok. 
    Results: One tumor shrank 50% after a December injection. A second vaccine targeting non-responding tumors is now in development.

Try this yourself:

Conyngham's approach demonstrates what is possible when you chain multiple AI models together for a single complex problem. The tools he used (ChatGPT, Grok, AlphaFold) are all publicly accessible. The breakthrough was not any single model, but the pipeline connecting them. 

Monday, March 9, 2026

Will AI replace you ?

 



Article content

Artificial Intelligence and the Professional Landscape: A Comprehensive Study Guide

This study guide examines the transformative impact of Artificial Intelligence (AI) across various professional sectors. It explores the paradigm shift from manual task execution to strategic judgment, highlighting the specific risks, opportunities, and preparation strategies necessary for professionals to thrive in an AI-integrated workforce.

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Industry Impact Summary

The following table summarizes the evolution of key professional roles as they transition from traditional methods to AI-enhanced workflows.

Profession

Traditional Focus (The "From")

Future Focus (The "To")

Core Preparation Strategy

Software Engineering

Coding and boilerplate execution

Architecting and shipping products

Practice shipping real code weekly using AI as a primary tool.

Law

Research and document drafting

Judgment, strategy, and trust

Use AI for all first drafts; focus on selling high-level judgment.

Finance

Analyst modeling and memo writing

Interpreting data and making investment calls

Let AI build the models while you focus on the final decision.

Consulting

Slide formatting and market synthesis

Problem framing and insight generation

Focus on how to frame complex problems rather than formatting decks.

Accounting

Bookkeeping and compliance

Strategic financial advisory

Automate repetitive workflows and replace them with advisory offers.

Design

UI production and visual systems

Taste, storytelling, and brand depth

Use AI for rapid concept generation; hone the instinct to pick the best.

Marketing

Copywriting and draft generation

Audience psychology and relationships

Use AI for drafts to test 50 angles instead of 5; own the audience connection.

Medical Analysis

Literature review and diagnostics

Patient empathy and clinical auditing

Integrate AI as a mandatory second opinion and learn to audit its output.

Education

Standardized instruction and lecturing

Facilitation, mentoring, and logic

Design assignments that require a combination of AI and human reasoning.

Customer Support

Answering basic tickets

Retention and high-stakes resolution

Learn to supervise AI agents and handle complex, relationship-based cases.

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Review Quiz: Short-Answer Questions

Instructions: Answer the following questions in 2-3 sentences based on the provided materials.

  1. What is the fundamental shift occurring in the value of professional work due to AI?
  2. How is the measurement of productivity changing for Software Engineers?
  3. In the legal profession, how can AI provide a competitive advantage to smaller firms?
  4. What is the primary "future skill" required for professionals in the Finance sector?
  5. How should Consultants change their focus regarding "strategy decks"?
  6. What is the predicted evolution of the Accountant's role?
  7. How does AI change the workflow for a Designer when generating concepts?
  8. In Marketing and Content creation, what allows a professional to stand out despite AI's ability to generate scale?
  9. What is the recommended role of AI in Medical Analysis?
  10. How does the role of a Customer Support professional change regarding "AI agents"?

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Answer Key

  1. The Shift: The value of work is shifting from manual task execution to strategic decision-making and judgment. AI is not necessarily replacing the professional, but rather replacing the manual "doing" of tasks.
  2. Software Engineering Productivity: The old metric of "lines of code" is disappearing as AI handles boilerplate code and debugging. Productivity is now measured by an engineer's ability to conceptualize, architect, and ship products.
  3. Legal Competition: AI commoditizes junior associate research and drafting, which were traditionally labor-intensive. This allows small firms to compete with "Big Law" by matching their output levels through AI efficiency.
  4. Finance Future Skill: While AI can build models and simulate scenarios instantly, the professional's value lies in interpretation. The dominant skill is the ability to analyze AI-generated data to make the final investment decision.
  5. Consulting Focus: Consultants should stop prioritizing the formatting of slides and the manual synthesis of markets. Instead, they must master problem framing and generating high-level insights.
  6. Accounting Evolution: Accountants are moving away from being compliance operators focused on routine bookkeeping and tax prep. They are evolving into strategic financial advisors who use automated workflows to support their clients.
  7. Design Workflow: Designers can use AI to generate 20 or more concepts or UI layouts instantly on demand. The designer’s value then shifts to using "taste" and "brand depth" to select the best option.
  8. Marketing Stand-out: While AI can iterate on 50 campaign angles instead of five, humans stand out through audience psychology. Understanding the audience relationship and storytelling remains a uniquely human-driven skill.
  9. Medical Analysis Role: AI should be used to accelerate literature reviews and diagnostics beyond human speed. Professionals must treat AI as a mandatory second opinion and focus on auditing those insights while spending more time with patients.
  10. Customer Support Supervision: Professionals will move away from answering simple tickets to supervising and improving AI agents. Their human effort will be redirected toward retention, high-stakes resolution, and complex relationship management.

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Essay Format Questions

Instructions: Use the themes from the source context to develop comprehensive responses to the following prompts. (Answers not provided).

  1. The Commoditization of Entry-Level Tasks: Discuss how the automation of junior-level tasks (such as legal research or analyst modeling) impacts the traditional "career ladder" in professional services.
  2. Human-Centric Value: Identify the specific human traits (e.g., empathy, taste, trust, judgment) mentioned in the text. Analyze why these traits become more valuable as AI execution becomes more prevalent.
  3. The "Shipping" Paradigm: Explore the concept of "One engineer shipping what used to require five." What are the economic and organizational implications of this increase in individual output?
  4. AI as an Auditor vs. AI as a Creator: Compare the use of AI in creative fields (like Design and Marketing) versus high-stakes analytical fields (like Medicine and Law). How does the "Human-in-the-loop" requirement differ?
  5. Strategic Education: How must educational institutions adapt their teaching methods if AI tutors provide "hyper-personalized learning at scale" and standardized instruction is outperformed?

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Comprehensive Glossary of Key Terms

  • Advisory Offer: A service model in accounting and finance where the professional provides strategic guidance and expert opinions rather than just performing compliance tasks like bookkeeping.
  • AI Agents: Automated systems designed to handle specific interactions, such as customer support tickets, which humans must learn to supervise and improve.
  • Audience Psychology: The study of how a target demographic thinks and reacts; identified as a critical skill for marketers to maintain a connection with their audience.
  • Boilerplate Code: Standardized blocks of code that can be reused in many places with little or no change; currently being automated by AI in software engineering.
  • Commoditization: The process by which a skill or service (like legal research or copywriting) becomes so common and easily accessible (via AI) that it loses its high market value as a standalone task.
  • Compliance: The act of adhering to laws, regulations, and guidelines (e.g., tax preparation); a traditional accounting task now being automated.
  • Hyper-personalized Learning: An educational approach where instruction is tailored to the specific needs, pace, and interests of an individual student, enabled by AI tutors.
  • Manual Execution: The "doing" part of a job—such as drafting a memo or formatting a slide—that is being replaced by AI automation.
  • Problem Framing: The ability to define the core challenges and questions of a project; identified as a primary future skill for consultants.
  • Strategic Judgment: The high-level ability to make decisions, negotiate, and provide trust-based advice, which remains the core value of human professionals.
  • Taste: The human ability to discern quality, aesthetic value, and brand alignment; a key differentiator for designers in an AI-driven environment.


Thursday, March 5, 2026

How I drew out an Ai System to Fail !

 

"Drawing out an AI system to fail" refers to intentionally designing, prompting, or testing an AI in ways that expose its weaknesses, limitations, or failure modes. - Mr. Perplexity Ai

With the advent of my excitement state, especially after visiting the Ai Symposium held in Delhi (India), I decided to push the Claude Ai to a stress Test and "bingo!" - it failed.

My learnings from the experiment are as follows :

  1. Firstly give a "simple looking" yet a very complex task for the Ai to perform
  2. Ensure that you gradually load with additional tasks based on its responses
  3. Let the Ai assume the additional inputs that it need to consider
  4. Respond only to the questions for which the Ai is seeking responses
  5. If the Ai makes improper assumptions, just point out the same
  6. Never preempt the Ai not to make any wrong assumptions
  7. Let the Ai feel that you are very comfortable in answering the questions
  8. Observe whether the Ai is going in circles - Fixing one issue leading to another etc
  9. After completing your desired iterations, then point out to the AI about the frustrating results
  10. At this stage, the Ai will accept failure

Ai Systems need to be Smart, that is the very purpose for which they are being built.

Whilst minor bugs in the Ai will not cost much, but failures in the Ai system to arrive at a solution can be very expensive and catastrophic in certain cases. Startups and Enterprises should be sensitive to this before they release their products.

Startups and Enterprises need to Test their Ai Solutions rigorously before they launch the same to internal/external stakeholders. In early 2000s, software companies used to launch software products with bugs, which customers used to unearth and report back during the implementation phase. In this Ai age, companies do not have that luxury. If the customers find the Ai to be prone to failure, they will lose faith in the company and will switch to competitions/substitutes. Ai Solution Testing Roadmap should be as critical and prioritised as any Feature Roadmap. Whilst it is not possible to have 100% Test Cases for an Ai Solution, any Startup or SME who do not deploy adequate "Stress Testing" of their Ai Solutions, will find it extremely difficult to survive/scale in the long run.

Smart Ai Systems tend to acquire customers at a disruptive speed.

“AI + Recession: Why India’s IT Revenue Pressure Is Reshaping Jobs, IIT Placements & Workforce Strategy”

 

India is entering a sensitive economic transition.

Three major forces are converging:

• Global recessionary slowdown • Rapid AI-led automation • Revenue pressure in Indian IT services

Individually, each is manageable.

Together, they are reshaping job creation, placement security, and workforce strategy across India — especially for B.Tech and technical graduates.

This is not panic.

This is structural transformation.


Revenue Pressure in Indian IT: The Immediate Trigger

Leading Indian IT companies such as Tata Consultancy Services, Infosys, Wipro and HCL Technologies have recently reported:

• Slower revenue growth • Margin compression • Delayed client decision cycles • Reduced discretionary spending in global markets

In certain service verticals, analysts indicate project deferrals creating 20–40% revenue pressure in specific segments, especially those dependent on traditional manpower-driven billing models.

When revenue visibility weakens:

• Hiring slows • Campus recruitment reduces • Offer letters get delayed • Workforce rationalization discussions increase

Revenue pressure directly impacts job creation.


AI Adoption: Transition Phase Creates Temporary Instability

Artificial Intelligence is transforming service delivery.

AI now performs:

• Code generation • Testing automation • Data processing • Support analytics • Documentation tasks

However, many Indian IT firms are still transitioning from:

Manpower-intensive delivery to Automation-integrated delivery

This shift requires:

• Large-scale reskilling • Infrastructure investment • Change in billing structures • New client contract models

During this transition:

• Productivity rises • Headcount dependency reduces • Revenue models adjust • Hiring becomes cautious

When AI acceleration meets recession slowdown, the impact on entry-level hiring becomes visible.


Job Creation Is Slowing — And Selective Cuts May Continue

If revenue pressure in certain verticals reaches 20–40%, companies prioritize:

• Cost optimization • Margin protection • Operational efficiency

This may result in:

• Reduced fresher intake • Selective job reductions • Delayed expansion hiring • Performance-linked workforce exits

The first and biggest impact is typically seen in:

• Entry-level B.Tech graduates • Routine technical profiles • Support and maintenance roles

This is not collapse.

It is correction.

But corrections create pressure in the short term.


Is This a Question on IITs?

Yes — but not as criticism.

IITs remain premier institutions with strong academic reputation.

However:

Earlier:

IIT tag = Placement guarantee

Now:

AI adaptability + business value + multi-skill capability = Placement guarantee

The IIT brand remains respected.

But the automatic safety cushion is thinner.

Companies are no longer hiring in bulk based solely on brand reputation.

They are hiring fewer, sharper, AI-ready professionals.

The brand opens the door.

Skill sustainability keeps it open.


The Multi-Talent Era Has Begun

Like cricket evolved from pure specialists to all-rounders, careers are evolving similarly.

Earlier:

Pure developer Pure tester Pure analyst

Now:

• Developer + AI automation • Engineer + business understanding • Data expert + communication strength • Technical + strategic thinking

Versatility builds resilience.

Single-dimensional skillsets are vulnerable during economic slowdown.


This Is Also Creating Demand for Smart Staffing Companies

Revenue pressure and AI transition are forcing companies to rethink workforce strategy.

In uncertain economic cycles, corporates prefer:

• Flexible hiring • Variable workforce cost • Reduced fixed payroll liability • Compliance-managed outsourcing • Project-based staffing models

This is increasing the demand for intelligent staffing partners.

Smart staffing companies help organizations:

• Convert fixed cost into flexible cost • Reduce long-term financial risk • Manage compliance efficiently • Deploy skilled professionals on demand • Scale up or down without structural disruption

In a revenue-sensitive environment, workforce flexibility becomes a survival strategy.

Staffing partners are no longer optional vendors.

They are cost-optimization and risk-management partners.

As hiring becomes selective and revenue remains under pressure, the role of professional staffing companies becomes strategically important.


Government Jobs Cannot Absorb the Gap

Public sector expansion cannot match private sector employment needs.

• Hiring capacity is limited • Automation is entering governance • Fiscal discipline restricts expansion

The real solution lies in:

Skill upgradation Entrepreneurship AI-driven innovation Flexible workforce ecosystems


The Strategic Reality

AI is not destroying Indian IT.

Recession is not destroying India.

But together, they are ending the illusion that degrees alone guarantee employment.

The workforce model is shifting from:

Degree-driven to Capability-driven

From:

Mass hiring to Precision hiring

From:

Fixed payroll structures to Flexible staffing ecosystems


Final Thought

If revenue pressure persists and AI adoption accelerates without parallel reskilling, job creation will slow further in the short term.

But long-term opportunity will belong to:

• AI-ready engineers • Multi-skilled professionals • Continuous learners • Flexible workforce strategists

And organizations that adopt smart staffing models will navigate this transition more smoothly than those that rely solely on traditional hiring structures.

The risk is not recession.

The risk is rigidity.

India’s future will not depend on how many degrees we produce.

It will depend on how adaptable our workforce becomes — and how intelligently companies manage talent.

The transition has begun.

The preparation must begin faster.

Saturday, February 28, 2026

AI-Driven Product SEO: Automated Optimization & User Journey Mapping

 

AI is no longer just helping brands rank higher.

It’s helping them sell smarter. 🚀

For SaaS and Ecommerce leaders, the real shift isn’t about keywords - it’s about AI connecting product visibility, inventory signals, and user journey insights into one optimization loop.

This is where AI-Driven Product SEO changes the game.

The Problem: SEO Is Still Siloed

Most product SEO strategies still operate in fragments:

  • SEO teams optimize metadata.
  • Merchandising teams manage inventory.
  • CRO teams tweak landing pages.
  • Data teams analyze churn.

But today’s AI search engines - including generative platforms - reward holistic signals, not isolated tactics.

If your product content ranks but:

  • Inventory is low ❌
  • Reviews are outdated ❌
  • Pricing signals are inconsistent ❌
  • FAQs don’t match buyer intent ❌

You lose conversions and AI visibility.

The Shift: AI as a Product Optimization Engine

Modern AI doesn’t just suggest keywords.

It can simultaneously analyze:

1️⃣ Search Intent + Behavioral Signals

AI clusters:

  • Transactional intent
  • Comparative queries
  • Post-purchase concerns
  • Churn triggers

Then aligns product descriptions, FAQs, schema, and internal linking accordingly.

Result: Higher query match accuracy in AI Overviews & LLM results.

2️⃣ Inventory & Demand Signals

AI integrates:

  • Stock availability
  • Seasonal velocity
  • Return rates
  • Refund reasons

And dynamically adjusts:

  • Featured products
  • Page prominence
  • Internal anchor priorities
  • Product snippet structures

This prevents sending paid and organic traffic to low-margin or high-return SKUs.

That’s not just SEO. That’s margin optimization.

3️⃣ User Journey Mapping in Real-Time

AI models can now map:

Search Query → Product Page → Engagement → Add to Cart → Post-Purchase → Returns → Repeat Behavior

When connected correctly, this allows:

  • Pre-purchase objection handling
  • Better FAQ structuring
  • Clearer sizing/spec guidance
  • Churn-reducing content modules

SEO becomes predictive - not reactive.

Why This Matters for AI Search (AEO + GEO)

Platforms like generative search engines don’t just rank pages.

They extract:

  • Structured answers
  • Product specs
  • Trust indicators
  • Review sentiment
  • Entity relationships

If your product pages lack structured clarity, AI may cite your competitor instead - even if you rank organically.

That’s where Automated Product SEO becomes critical.

The Business Impact 📊

When AI connects optimization with business data:

✅ Higher conversion rates

✅ Lower return rates

✅ Better product-market alignment

✅ Reduced churn

✅ Stronger AI citation probability

✅ Improved LTV per customer

This is Revenue-Driven SEO, not vanity traffic growth.

Enterprise & Startup Opportunity

For startups:

  • Automate scaling without bloated teams
  • Compete with enterprise brands through structured intelligence

For enterprises:

  • Unify SEO, merchandising, and analytics
  • Deploy predictive content updates at scale

The advantage goes to brands that treat product SEO as a dynamic system, not a static checklist.

What AI-Driven Product SEO Actually Looks Like

It includes:

  • Automated schema refinement
  • Dynamic meta updates based on demand shifts
  • AI-optimized FAQ blocks aligned with churn insights
  • Behavioral data-informed internal linking
  • Intent-cluster-based product content frameworks
  • GEO optimization for AI answer engines

This is where SEO, AEO, and GEO converge.

The Future: Optimization That Learns

The next evolution isn’t “better keyword research.”

It’s:

👉 Content that adapts to buyer behavior

👉 Product pages that update with real-time signals

👉 AI that flags churn risks before customers leave

👉 SEO strategies aligned with revenue, not impressions

If your product SEO isn’t tied to lifecycle analytics, you’re optimizing for yesterday’s search engine.


Final Thought

In 2026 and beyond, the brands that win won’t be the ones with the most traffic.

They’ll be the ones with the most intelligent product ecosystems.

AI-driven optimization is no longer optional. It’s your conversion engine. ⚙️

Ready to See Where You Stand?

If you’re a SaaS or Ecommerce leader serious about:

  • AI visibility
  • Conversion growth
  • Reduced churn
  • Revenue-aligned SEO

Request a comprehensive SEO / AEO / GEO Product Audit.

We’ll show you:

✔ Where AI engines are (or aren’t) citing you

✔ Which product pages leak revenue

✔ How automation can drive measurable growth

Let’s build product SEO that actually drives business outcomes. 🚀

#AISEO #ProductSEO #EcommerceGrowth #SaaSMarketing #AEO #GenerativeSearch #ConversionOptimization #DigitalCommerce #AIVisibility #RevenueGrowth

Sunday, February 22, 2026

What If We Asked AI to Design the Best Hiring System from Scratch?

 Using AI to Convert Hiring into a True Business Process

Not tweak the current one. Not optimize it. Start over.

No legacy ATS workflows. No "we've always done it this way." No vendor influence. Just this question: given everything we know about talent markets, organizational science, and what actually predicts someone's success a year into a job – what would the ideal hiring system look like?

That's the experiment. The results will be discussed at this week's "Moneyball for HR!" prime time event (February 26th at 1 PM ET Zoom) or LinkedIn Live.

Here's the Challenge

Before our session, I'm asking every participant to take the nine requirements below, paste them into the AI system of their choice – ChatGPT, Claude, Gemini, Copilot, whatever you prefer – and ask it to design the optimal hiring system from scratch. Then come to the session ready to share what your AI came up with.

The prompt:

"Using the nine requirements below, design a hiring system from scratch that operates as a true business process – one that is accountable, objective, predictable, measurable, repeatable and cost-effective compared to current practices. Explain your reasoning for each design choice and cite the research that supports it."

The Nine Requirements

  1. Focus on attracting the best talent for staff and management roles recognizing that the strongest candidates are not actively looking and change jobs based on career growth opportunities, not job postings. The goal is to raise the level of talent companywide.
  2. Embed how research shows the best hiring managers actually find, evaluate and hire the strongest talent and develop them to succeed. Use What the World's Best Manager's Do Differently as the baseline but revalidate these findings.
  3. Ensure the system is more legally defensible than traditional hiring by anchoring every screening and evaluation step in job-related criteria consistent with EEOC Uniform Guidelines, Griggs v. Duke Power principles, and local, state and Federal labor laws.
  4. Use best organizational development science to predict post-hire success using multi-trait, multi-method assessment approaches considering the work of Paul Sackett, Todd Rose, author of The End of Average, and Tom Janz, the founder of behavioral event interviewing. Again, validate common benchmarks to ensure their findings.
  5. Conduct a proper job analysis that accurately reflects the open role and the factors most likely to affect post-hire performance and engagement.
  6. Redesign the apply process so that candidates demonstrate their competency through a structured self-assessment against the actual requirements of the role, making the application itself an initial evaluation. The purpose is to minimize reliance on costly upfront screening while giving candidates enough information about real job expectations to self-select appropriately.
  7. Achieve more Win-Win Hiring outcomes where success is measured on the first year anniversary date, not the start date, considering performance, job satisfaction and manager assessment of growth trajectory.
  8. Design the employer value proposition and sourcing approach based on how the best people prefer to find new jobs and what they need to see and learn in order to consider a new opportunity a career move.
  9. Design an onboarding program that ensures the new hire is fully engaged and performing at a high level throughout the first year based on Gallup's Q12 engagement survey.

What I Expect Will Happen

When 50 or 100 people independently ask different AI systems the same question, the answers are going to converge. Not because the AI has been told what to say, but because the underlying research points in the same direction. And that convergence is the conversation — what does it mean when an unbiased system, reasoning from first principles, designs something fundamentally different from what most companies are doing today?

Then try this prompt:

How well does Performance-based Hiring meet these requirements? Be cynical in your assessment and as part of this compare what you know about (company's) current hiring system and major HR tech tools against these same findings.

Tuesday, January 20, 2026

AI’s $15 Trillion Opportunity Won’t Come from Technology Alone — It Will Come from Learning

 Artificial Intelligence is advancing faster than any workplace technology we’ve seen before. Leaders across business, government, and civil society are optimistic—and for good reason. The World Economic Forum estimates AI could contribute nearly 14% of global GDP by 2030, translating to a $15+ trillion economic opportunity.

AI adoption is accelerating, but enterprise-level productivity gains remain inconsistent and fragile. Employees report saving time with AI tools, yet organizations and economies are not seeing sustained performance improvements. Productivity growth in major economies remains uneven, even as AI becomes embedded in daily work.

This signals a hard truth: technology alone does not create productivity. Systems do.


The Real Gap Is Not AI Adoption — It’s Learning

Most organizations are still approaching AI transformation in the wrong sequence:

  1. Deploy new technology
  2. Add training later

This “technology-first, people-second” model no longer works.

True productivity emerges when humans and AI are designed to learn together, continuously, within real workflows. That means rethinking not just tools—but how work itself is structured.

The future of productivity lies in workforce augmentation, not automation versus humans.


From Training Programs to Learning Systems

Traditional upskilling models—offsite workshops, static courses, one-size-fits-all training—are too slow for an AI-driven world.

What’s needed is a learning system embedded directly into work, enabling people to adapt as roles evolve in real time.

To address this challenge, a practical framework emerges: DEEP.


The DEEP Framework: Embedding Learning into AI Transformation

1. Diagnose

Organizations must analyze work at the task level—not job titles—to understand where AI can meaningfully augment human performance. This requires collaboration between domain experts, early AI adopters, and cross-functional “augmentation squads” focused on real use cases.

2. Embed

Learning should happen in the flow of work. AI can deliver just-in-time coaching, personalized feedback, and contextual guidance while employees are doing their jobs. This also requires a culture that rewards experimentation, knowledge sharing, and durable human skills like creativity, judgment, and critical thinking.

3. Evaluate

Modern learning systems need real skills data. AI can help infer capabilities from behavior and work outputs, enabling continuous assessment, smarter recommendations, and personalized development paths—without disrupting productivity.

4. Prioritize

Learning and Development must evolve from content delivery to capability architecture. This means skill-based workforce planning, strong executive sponsorship, and portable, verifiable skill records that follow individuals across roles and careers.


Why This Matters Now

AI transformation is not a one-time rollout—it’s a continuous cycle of change. Organizations that treat learning as a side initiative will struggle to keep pace. Those that embed learning into everyday work will move faster, adapt better, and scale productivity sustainably.

The real competitive advantage won’t come from who adopts AI first—but from who learns fastest.


The Path to an Augmented Future

To unlock AI’s $15 trillion promise, leaders must move beyond the false choice between automation and human labor. The future belongs to organizations that invest equally in AI capability and human learning, designing systems where both evolve together.

AI doesn’t replace people. People who learn with AI will outperform those who don’t.

And that is where real productivity begins.

 

The Rise of Agentic AI Tutoring: Moving Beyond the Chatbot Era

 

Imagine a tutor who doesn’t just answer your questions—but anticipates them.

A tutor that recognizes when you’re struggling before you ask for help. One that changes its teaching strategy mid-lesson, asks the right Socratic questions instead of giving answers, and adapts continuously to how you think, learn, and apply knowledge.

This isn’t science fiction.

This is the emerging reality of Agentic AI Tutoring—a shift that takes us far beyond basic chatbots and into a new era of intelligent, proactive, and personalized learning systems.


What Is Agentic AI?

At its core, agentic AI refers to artificial intelligence systems that operate with autonomy, intent, and adaptability.

Unlike traditional AI tools that simply respond to prompts, agentic systems are:

In education, this marks a fundamental change.

Instead of waiting for students to ask questions, agentic AI tutors actively manage the learning journey—guiding, nudging, diagnosing, and adjusting in real time.


Traditional AI Tutor vs. Agentic AI Tutor

Traditional AI tutors behave like smart textbooks:

  • Reactive
  • Prompt-driven
  • Limited to predefined rules

Agentic AI tutors, on the other hand, function more like a personal learning coach:

  • Proactive and anticipatory
  • Focused on long-term learning goals
  • Continuously adapting to learner behavior
  • Optimizing not just answers, but understanding

The difference isn’t incremental—it’s transformational.Why Agentic AI Tutoring Matters Now

The limitations of one-size-fits-all education are well known. Agentic AI tutoring directly addresses them by enabling:

Hyper-Personalization at Scale

Every learner follows a unique path—pace, content, difficulty, and teaching method adapt in real time.

Proactive Intervention

Instead of discovering learning gaps after an exam, agentic tutors detect early signals—hesitation, repeated errors, conceptual drift—and intervene immediately.

24/7 Intelligent Support

High-quality, personalized tutoring becomes accessible anytime, anywhere—helping democratize learning at scale.

Empowered Human Educators

By handling routine practice, diagnostics, and administrative tasks, AI agents free teachers to focus on mentorship, creativity, emotional intelligence, and higher-order instruction.


The Future of Education Is Collaborative: AI + Humans

Agentic AI is not here to replace teachers.

The future belongs to collaborative intelligence—where AI handles personalization and scale, while humans provide empathy, motivation, ethics, and meaning.

In this model, educators evolve into learning architects:

  • Designing experiences, not just lessons
  • Interpreting AI-generated insights
  • Supporting social-emotional development
  • Guiding critical and creative thinking

Classrooms of the future will blend AI-guided practice with human-led discussion, mentoring, and collaboration.


Challenges We Must Address

This transformation must be built responsibly:

  • Data Privacy & Security: Learner data must be protected and ethically governed.
  • Algorithmic Bias: AI systems must be audited continuously to avoid reinforcing inequities.
  • Avoiding Over-Reliance: Agentic tutors should gradually reduce scaffolding to encourage learner independence—not dependency.

Design matters as much as capability.


Conclusion: A Defining Moment for Education

The rise of agentic AI tutoring marks a turning point in how knowledge is taught, practiced, and mastered.

We are moving from static tools to adaptive learning partners—from content delivery to capability development.

When implemented thoughtfully, agentic AI has the potential to:

  • Personalize learning for every student
  • Empower teachers, not replace them
  • Build faster, deeper, more resilient learning systems

The future of education will not be automated. It will be augmented.

And those who learn with intelligent agents will shape what comes next.