Digital Plots

  • AI Weekly Bulletin

    Welcome to the AI Update Bulletin! Here are the highlights from the previous week.

  • AEO: The Next Frontier After SEO — Are You Ready?

    For years, Search Engine Optimization (SEO) has been the backbone of digital visibility. But the way users discover information is rapidly evolving. With the rise of AI assistants, voice search, and conversational interfaces, a new paradigm is emerging: Answer Engine Optimization (AEO).

    If SEO was about ranking links, AEO is about becoming the answer.

    What is AEO?

    Answer Engine Optimization (AEO) focuses on optimizing content so that AI systems — like ChatGPT, Google’s AI Overviews, and voice assistants — can directly extract and present your content as the best answer to a user’s question.

    Instead of asking:

    “Which website ranks #1?”

    Users are now asking:

    “What’s the best way to improve product adoption?”

    And they expect a direct, concise answer, not a list of links.

    Why AEO Matters Now

    Three major shifts are driving AEO:

    1. Rise of AI-first search

    Search engines are becoming answer engines. AI summaries often appear before traditional results.

    2. Explosion of voice interfaces

    With voice queries, users expect one clear answer, not ten options.

    3. Decline of click-based discovery

    Users increasingly consume information without visiting websites — making visibility within answers critical.

    How AEO Differs from SEO

    SEOAEO
    Focus on keywordsFocus on questions
    Optimize for rankingOptimize for answers
    Drive clicksDeliver value instantly
    Long-form contentStructured, concise responses

    Core Principles of AEO

    1. Think in Questions, Not Keywords

    Shift your mindset:

    • ❌ “AI tools for productivity”
    • ✅ “What are the best AI tools to improve productivity in 2026?”

    2. Provide Direct, Structured Answers

    AI prefers content that is:

    • Clear
    • Concise
    • Well-structured (lists, bullet points, summaries)

    3. Use Semantic Context

    Cover the topic deeply:

    • Definitions
    • Use cases
    • Comparisons
    • FAQs

    This helps AI understand context, not just keywords.

    4. Build Authority and Trust

    AI systems prioritize:

    • Credible sources
    • Expert insights
    • Real-world examples

    Quoting companies, leaders, or case studies increases your chances of being selected as “the answer.”

    AEO Playbook for Product Managers

    If you’re building products or content platforms, AEO is not optional — it’s strategic.

    1. Redesign Content Strategy

    Create:

    • FAQ-driven pages
    • Problem-solution articles
    • “How-to” guides

    2. Structure for AI Consumption

    Use:

    • Headings (H2, H3)
    • Bullet points
    • Short paragraphs

    3. Optimize for Featured Snippets

    Think:

    • “Can this paragraph be quoted directly as an answer?”

    4. Leverage First-Principles Thinking

    Explain why, not just what. AI favors explanatory depth.

    Real-World Example

    Instead of writing:

    “Top AI tools for product managers”

    Write:

    “What are the best AI tools for product managers in 2026?”

    Then answer immediately:

    • Notion AI – for documentation
    • ChatGPT – for ideation
    • Figma AI – for design workflows

    Follow it with deeper explanations.

    The Strategic Shift

    AEO is not replacing SEO — it’s layering on top of it.

    Think of it as:

    SEO gets you discovered
    AEO gets you chosen

    In an AI-driven world, visibility is no longer about ranking first — it’s about being trusted enough to be quoted.

    Final Thought

    We are entering an era where interfaces disappear and answers dominate. As product managers, content creators, and builders, the opportunity is clear:

    Don’t just create content.
    Create answers.

    Tags: AEO, SEO, AI Search, Product Management, Content Strategy, Generative AI

  • AI Is Becoming Invisible: Why the Best Products This Week Don’t Look Like “AI Products”

    This week in AI made one thing clear: the most successful products are no longer advertising AI—they’re quietly embedding it so deeply that users stop noticing it altogether.

    From updates by Apple to continued ecosystem moves by OpenAI and Google, the industry is converging on a new principle:

    The best AI experience is one that feels invisible.

    For product managers, this marks a critical shift in how AI products should be built, positioned, and measured.

    1. The Shift from “AI Features” to “Default Behavior”

    This week’s product updates show a consistent pattern: AI is no longer a toggle—it’s the default.

    • Google continues integrating AI directly into Search results and workflows
    • OpenAI is refining memory and personalization inside ChatGPT
    • Apple is leaning toward on-device intelligence and seamless UX integration

    Users aren’t being asked if they want AI—they’re simply experiencing smarter products.

    Implication:
    AI is moving from a feature layer to a behavior layer.

    For PMs:

    • Stop labeling things as “AI-powered”
    • Focus on making intelligence feel native and expected

    2. Personalization Is the Real Breakthrough

    One of the most important undercurrents this week is the acceleration of persistent AI context.

    Products are getting better at:

    • Remembering user preferences
    • Adapting tone and output style
    • Anticipating intent across sessions

    This direction aligns closely with what leaders like Sam Altman have emphasized: AI should increasingly feel like a system that knows you.

    Implication:
    Generic AI is becoming commoditized. Personalized AI is the differentiator.

    For PMs:

    • Treat memory as a core product primitive
    • Design explicit controls for trust and transparency
    • Build systems that improve with every interaction

    3. Latency and UX Are Now Competitive Advantages

    Another subtle but critical shift: speed and responsiveness are now product differentiators.

    As models converge in quality:

    • Faster responses feel “smarter”
    • Lower friction increases adoption
    • Seamless UX beats raw capability

    Companies like Microsoft are optimizing Copilot experiences across apps, reducing the gap between intent and execution.

    Implication:
    Users judge AI less by what it can do and more by how effortlessly it does it.

    For PMs:

    • Optimize for time-to-value, not just output quality
    • Minimize prompts, clicks, and context switching
    • Invest heavily in interaction design

    4. The Brand Risk of “Over-AI-ing” Your Product

    Interestingly, this week also reinforced a growing risk: overexposing AI can hurt user trust.

    When products:

    • Overpromise autonomy
    • Misrepresent capabilities
    • Or behave inconsistently

    Users disengage quickly.

    This is why some companies are deliberately downplaying AI branding, even while expanding capabilities.

    Implication:
    AI is powerful—but fragile from a trust perspective.

    For PMs:

    • Underpromise, overdeliver
    • Provide clear failure states and fallbacks
    • Avoid anthropomorphism unless it adds real value

    5. The New PM Playbook: Design for Effortlessness

    The biggest takeaway from this week is simple but profound:

    The goal is no longer to build impressive AI—it’s to remove effort entirely.

    Winning products:

    • Anticipate needs
    • Reduce decisions
    • Automate the boring parts
    • Stay out of the way when not needed

    This is the philosophy behind the direction we’re seeing across Apple, Google, and OpenAI.

    Final Thought

    We’re entering the era of ambient intelligence—where AI fades into the background and value comes from what doesn’trequire user effort.

    As a product manager, the question is no longer:

    “Where do we add AI?”

    Instead, it’s:

    “Where can we eliminate friction so completely that the user forgets the work was ever there?”

    That’s the bar now.

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  • The Shift to Agentic AI: What Product Managers Must Rethink in 2026

    The AI landscape this week made one thing very clear: we are moving beyond AI as a feature into AI as an autonomous system. For product managers, this is not just an evolution—it’s a complete reset of how products are built, scaled, and differentiated.

    Recent developments across companies like Microsoft, OpenAI, Apple, and Google highlight three major shifts that every product leader must understand.

    AI is Becoming Core Infrastructure

    This week, Microsoft doubled down on AI infrastructure by expanding its data center footprint, signaling that compute is now a strategic moat. At the same time, its evolving relationship with OpenAI shows that even the strongest partnerships are being re-evaluated in the race for AI dominance.

    Product Takeaway:
    Infrastructure decisions are now product decisions.

    PMs must start thinking about:

    • Model cost vs user value
    • Latency vs experience
    • Vendor dependency risks

    Your roadmap is no longer independent of your cloud strategy—it is defined by it.

    From Features to Autonomous Agents

    OpenAI’s move to deprioritize standalone applications in favor of more advanced agent capabilities signals a bigger trend: users don’t want tools—they want outcomes.

    AI systems are now:

    • Writing code
    • Automating workflows
    • Making decisions

    This is a fundamental shift from “assistive AI” to “agentic AI.”

    Product Takeaway:
    Stop building features. Start building goal-driven systems.

    The winning products will:

    • Accept high-level user intent
    • Execute multi-step workflows
    • Continuously learn and adapt

    Talent is the New Competitive Advantage

    Apple hiring top AI talent from Google to revamp Siri shows that AI is now central to product strategy—not an add-on.

    Meanwhile, even large players are struggling to keep up with rapid advancements, leading to partnerships, acquisitions, and talent wars.

    Product Takeaway:
    Your product quality is now directly tied to:

    • The strength of your AI team
    • Access to cutting-edge models
    • Speed of experimentation

    PMs must work closer than ever with research and engineering.

    Regulation is Shaping Product Design

    Global regulators are increasingly scrutinizing AI leaders like Sam Altman and Sundar Pichai, especially around data usage, competition, and safety.

    This means compliance is no longer a backend concern—it’s a core product requirement.

    Product Takeaway:
    Trust, transparency, and safety are now key differentiators.

    Final Thought

    We are entering the era of Agentic Infrastructure.

    AI is no longer something you “add” to your product.
    It is something your product is built on.

    For product managers, success will depend on:

    • Designing for autonomous workflows
    • Managing compute and cost constraints
    • Building trust in AI-driven decisions

    The question is no longer “How do we use AI?”
    It is “How do we build products that think and act?”

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  • AI Weekly Bulletin

    Welcome to the AI Update Bulletin! Here are the highlights from the previous week.

  • Prompt Engineering and AI Collaboration Skills Every Product Manager Should Master

    As artificial intelligence becomes integral to modern product development, Product Managers (PMs) are expected to collaborate effectively with AI systems alongside cross-functional teams. Among the most critical emerging competencies are prompt engineering and AI collaboration skills—capabilities that enable PMs to translate business intent into actionable AI outputs while ensuring alignment with user needs, ethics, and strategy.

    Understanding Prompt Engineering for PMs

    Prompt engineering refers to the structured design of inputs given to AI systems to generate accurate, relevant, and reliable outputs. For PMs, this is less about technical mastery and more about clarity of intent, context setting, and iterative refinement.

    Effective prompts typically include:

    • Clear objectives and constraints
    • Relevant context or background information
    • Desired output format or tone
    • Examples to guide responses

    For instance, OpenAI product teams emphasize iterative prompt testing to refine outputs for tools like ChatGPT, ensuring responses align with user expectations and safety standards. Similarly, Notion uses prompt patterns internally to standardize AI-assisted content generation across its workspace features.

    Collaborating with AI as a Product Partner

    AI should be treated as a collaborative assistant rather than a black-box solution. PMs must learn to:

    • Break down ambiguous problems into structured queries
    • Validate AI outputs against business logic and user research
    • Combine human judgment with machine-generated insights

    At Google, PMs working on Search and Workspace products leverage AI to synthesize large datasets and user feedback, but final decisions remain grounded in human review and experimentation. Amazon follows a similar approach, using AI-generated insights to inform pricing, recommendations, and logistics decisions, while PMs ensure alignment with customer trust and long-term value.

    Embedding AI into Product Discovery and Delivery

    AI collaboration skills are especially valuable across the product lifecycle:

    • Discovery: Using AI to summarize user interviews, analyze survey data, or identify emerging trends
    • Delivery: Assisting with PRDs, test case generation, release notes, and go-to-market drafts
    • Optimization: Interpreting AI-driven analytics to refine features and prioritize roadmaps

    For example, Airbnb uses AI-assisted tools to analyze host and guest feedback at scale, enabling PMs to identify friction points faster and validate hypotheses more efficiently.

    Ethical Awareness and Governance

    Finally, PMs must understand the ethical implications of AI usage. This includes recognizing bias, ensuring transparency, and complying with data privacy standards. Companies like Microsoft have established responsible AI frameworks, and PMs play a central role in operationalizing these principles within products.

    Conclusion

    Prompt engineering and AI collaboration are no longer optional skills for Product Managers. By mastering structured prompting, treating AI as a collaborative partner, and embedding ethical oversight into workflows, PMs can significantly enhance decision-making, speed, and product quality in an AI-driven landscape.

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  • Data-Driven Decision Making for Product Managers: Leveraging AI for Roadmaps and Prioritization

    In an increasingly complex and fast-moving business environment, Product Managers (PMs) are expected to make high-impact decisions with limited time and imperfect information. Roadmap planning and prioritization—once driven primarily by intuition, stakeholder influence, and historical precedent—are now being transformed by Artificial Intelligence. AI enables PMs to replace opinion-based debates with evidence-based decisions, improving alignment, predictability, and customer value.

    The Shift from Intuition to Intelligence

    Traditional roadmap planning often relied on static inputs such as annual plans, customer requests, or leadership directives. While experience and judgment remain critical, these methods struggle to scale in data-rich environments. AI introduces the ability to continuously analyze customer behavior, market signals, and business performance, enabling dynamic and responsive decision-making.

    At Amazon, roadmap and prioritization decisions are deeply rooted in data. Predictive analytics help product teams evaluate customer demand, operational impact, and long-term value—a practice that aligns with Jeff Bezos’ emphasis on making “high-quality decisions quickly” using measurable inputs.

    AI-Powered Inputs for Roadmap Decisions

    AI strengthens roadmap planning by synthesizing diverse data sources into actionable insights:

    • Customer usage data: Identifies adoption patterns and unmet needs
    • Customer feedback and sentiment: Analyzes reviews, support tickets, and surveys using Natural Language Processing (NLP)
    • Market and competitive signals: Detects trends and feature gaps
    • Business metrics: Forecasts revenue impact, cost, and risk

    For example, Microsoft uses AI-driven analytics across its product ecosystem to understand usage trends and customer feedback at scale. Under Satya Nadella’s leadership, data-informed decision-making has been positioned as a core pillar of Microsoft’s product strategy.

    AI-Driven Prioritization Models

    One of the most valuable applications of AI is in prioritization. Traditional frameworks such as RICE or MoSCoW depend heavily on subjective scoring. AI augments these models by providing predictive signals and scenario analysis.

    AI systems can:

    • Predict feature adoption and engagement
    • Estimate customer impact across segments
    • Assess delivery risk and technical complexity
    • Simulate trade-offs between short-term gains and long-term strategy

    At Netflix, AI is used to forecast how product and content changes will affect engagement and retention. Co-founder Reed Hastings has frequently highlighted the role of data and machine learning in guiding strategic prioritization, reducing uncertainty before major investments are made.

    Dynamic Roadmaps Enabled by AI

    Unlike static roadmaps that are revised quarterly or annually, AI enables continuous roadmap evolution. Real-time data allows PMs to adjust priorities as customer behavior, market conditions, or business objectives change.

    Spotify exemplifies this approach by using AI to monitor user engagement and listening behavior. Insights derived from personalization systems influence not only user experience but also roadmap decisions—allowing teams to invest in features that demonstrate measurable customer value.

    This shift supports a more adaptive roadmap that reflects reality rather than assumptions.

    Balancing AI Insights with Human Judgment

    While AI enhances decision quality, it does not replace the Product Manager’s responsibility. AI models reflect the data they are trained on and may reinforce biases or overlook strategic context. PMs must interpret AI outputs, challenge assumptions, and align decisions with vision, ethics, and long-term goals.

    Leaders such as Sundar Pichai have emphasized the importance of responsible AI, highlighting that technology must be guided by human values. For PMs, this means ensuring transparency, fairness, and customer trust remain central to roadmap decisions.

    Key Capabilities PMs Need in an AI-Driven Environment

    To effectively leverage AI for roadmaps and prioritization, PMs must develop:

    • Data literacy to understand models, metrics, and limitations
    • Strategic thinking to align insights with business goals
    • Cross-functional collaboration with data science and engineering teams
    • Ethical awareness to mitigate bias and ensure responsible use of AI

    Conclusion

    AI is fundamentally reshaping how Product Managers plan roadmaps and prioritize work. By enabling data-driven, predictive, and dynamic decision-making, AI reduces uncertainty and increases confidence in product strategy. Organizations such as Amazon, Microsoft, Netflix, and Spotify demonstrate that when AI insights are combined with strong product leadership, roadmap decisions become more aligned, agile, and customer-centric.

    In the AI era, the most effective PMs will be those who can transform data into decisions—balancing intelligence with judgment to deliver sustained product value.

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  • Welcome to AI News Belletin

    Hi everyone, welcome to the channel!

    Excited to start a weekly AI newsroom where we break down the most important AI developments from the past week. From new tools and product launches to major announcements from tech leaders—let’s cut through the noise and focus on what really matters in AI. Let’s get started.

  • The New Product Discovery: Using AI Tools for Customer Research and Insights

    Product discovery has always been at the core of successful product management. Understanding customer needs, validating assumptions, and identifying opportunities are essential to building products that deliver real value. In recent years, Artificial Intelligence has fundamentally transformed this phase of the product lifecycle. What was once a largely manual, time-intensive process is now augmented by AI-driven tools capable of generating deep, continuous, and scalable customer insights.

    This evolution marks the emergence of a new product discovery paradigm—one where AI acts as a force multiplier for Product Managers, enabling faster learning, better decisions, and more confident prioritization.

    1. From Manual Research to Continuous Insight Generation

    Traditional customer research relied heavily on interviews, surveys, usability studies, and periodic market analysis. While these methods remain valuable, they are limited in scale and frequency. AI tools now analyze vast volumes of qualitative and quantitative data in near real time, uncovering patterns that would otherwise go unnoticed.

    For example, Amazon applies machine learning to analyze customer reviews, search behavior, and purchasing data to identify unmet needs and product gaps. This capability, strongly aligned with Jeff Bezos’ long-standing emphasis on customer obsession, allows product teams to discover insights continuously rather than episodically.

    Similarly, Salesforce uses AI within its Einstein platform to surface customer sentiment, usage trends, and predictive insights directly to product and business teams, enabling faster discovery cycles.

    2. AI-Driven Analysis of Qualitative Customer Feedback

    One of the most significant challenges in product discovery has been synthesizing qualitative data—interview notes, open-ended survey responses, support tickets, and social media conversations. Large Language Models (LLMs) can now summarize, cluster, and interpret this data at scale.

    Companies such as Microsoft leverage AI to analyze customer feedback across multiple channels, helping product teams identify recurring pain points and feature requests. Under the leadership of Satya Nadella, Microsoft has emphasized the use of AI to “listen at scale,” ensuring that product decisions are informed by real user voices rather than isolated anecdotes.

    This capability enables Product Managers to move from selective sampling to comprehensive understanding, while still preserving the richness of qualitative insights.

    3. Predictive Insights and Opportunity Identification

    Beyond understanding what customers say, AI enables teams to predict what customers are likely to need next. Predictive models can identify early signals of churn, feature adoption, or emerging use cases, allowing PMs to proactively explore new opportunities.

    At Netflix, AI analyzes viewing patterns, engagement data, and content preferences to inform product discovery and content investments. Co-founder Reed Hastings has often highlighted how data and machine learning help Netflix anticipate user preferences rather than react to them. This predictive capability reduces uncertainty and strengthens discovery decisions before significant investment is made.

    4. Rapid Hypothesis Testing and Experimentation

    AI has dramatically reduced the cost and time required to test product assumptions. Simulation models, AI-generated prototypes, and automated A/B testing enable PMs to validate hypotheses early in the discovery phase.

    For instance, Airbnb uses AI to test variations of user flows and experiences, enabling teams to quickly learn what resonates with customers. Former CEO Brian Chesky has emphasized rapid experimentation as a key driver of product innovation, supported increasingly by data and AI-based experimentation frameworks.

    This shift allows Product Managers to move from opinion-driven debates to evidence-based discovery.

    5. Personalization as a Discovery Signal

    Personalization engines do more than improve user experience; they also serve as powerful discovery tools. By observing how different customer segments respond to tailored experiences, PMs gain insights into diverse needs and preferences.

    Spotify, for example, uses AI-driven personalization to power features such as Discover Weekly. These systems not only delight users but also provide product teams with insights into listening behaviors, emerging genres, and unmet expectations—informing future product discovery and roadmap decisions.

    6. The Evolving Role of Product Managers

    As AI takes on data processing and pattern recognition, the role of the Product Manager is shifting toward interpretation, judgment, and ethical responsibility. PMs must ask the right questions, challenge AI-generated insights, and ensure that discovery outcomes align with business goals and customer trust.

    Key competencies for modern product discovery include:

    • AI and data literacy to understand model outputs and limitations
    • Critical thinking to distinguish correlation from causation
    • Ethical awareness to address bias, privacy, and transparency
    • Cross-functional collaboration with data science and research teams

    Conclusion

    AI is redefining product discovery by enabling deeper, faster, and more continuous customer understanding. Rather than replacing traditional research methods, AI enhances them—scaling insights while preserving human judgment. Organizations that successfully integrate AI into product discovery, as demonstrated by leaders like Amazon, Netflix, Microsoft, and Airbnb, gain a decisive advantage in building products that truly meet customer needs.

    In this new era, the most effective Product Managers will be those who can combine AI-powered insights with strategic thinking and customer empathy, shaping products that are both intelligent and deeply human.

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  • How AI Is Transforming the Role of Product Managers

    Artificial Intelligence is reshaping the product management discipline at an unprecedented pace. What was once a role dominated by intuition, customer feedback loops, and incremental decision-making is rapidly evolving into a function guided by data-driven intelligence, predictive insights, and automation. As AI becomes embedded in every layer of product development, the expectations from modern Product Managers (PMs) have shifted significantly.

    1. From Insight Gathering to Intelligence Orchestration

    Traditionally, PMs relied on customer interviews, surveys, and manual market analysis. Today, AI tools automatically extract patterns from large datasets—usage logs, customer sentiment, behavioral cohorts, and competitive signals.
    For example, Google leverages AI-driven analytics to understand user intent and personalize its Search product. Similarly, Netflix, under the leadership of co-founder Reed Hastings, uses AI to analyze massive viewing datasets, helping PMs optimize recommendations and content strategy.

    2. Accelerated Decision-Making Through Predictive Analytics

    AI empowers PMs to forecast outcomes before committing resources. Demand forecasting, churn prediction, feature adoption projection, and risk scoring are now standard capabilities.
    At Amazon, predictive models help PMs understand the impact of price changes, logistics optimizations, and feature rollouts—an approach championed by Jeff Bezos’ philosophy of “customer obsession supported by data-driven decisions.”

    3. Automation of Routine Product Workflows

    AI is eliminating manual and repetitive tasks: backlog grooming, user story generation, impact analysis, and competitor research. Large Language Models (LLMs), including those used by companies like OpenAI, enable PMs to draft PRDs, analyze user feedback at scale, and even simulate user journeys. This automation frees PMs to focus on strategy and cross-functional alignment.

    4. More Strategic, Cross-Functional Leadership

    As AI systems automate execution-heavy tasks, PMs must pivot to higher-order responsibilities—ethical decision-making, responsible AI governance, and aligning AI capabilities with business objectives.
    For instance, Satya Nadella emphasizes Microsoft’s focus on “AI alignment with human values,” requiring PMs to work closely with data scientists, legal teams, and policy leaders to ensure responsible innovation.

    5. Rapid Experimentation and AI-Driven Prototyping

    AI significantly reduces the cost and time of experimentation. PMs can now generate prototypes, user flows, and product hypotheses in minutes rather than weeks.
    Companies like Airbnb use AI to simulate user behavior and test interface variations rapidly—an approach that former CEO Brian Chesky has highlighted as key to scaling product innovation.

    6. The New Skill Set for AI-Era Product Managers

    To thrive in this environment, PMs must cultivate:

    • AI literacy: understanding models, data pipelines, and biases.
    • Data fluency: interpreting metrics and running experiments.
    • Ethical reasoning: ensuring fairness, transparency, and privacy.
    • Collaboration with AI teams: working closely with ML engineers and researchers.

    Conclusion

    AI is expanding the scope, influence, and impact of Product Managers. Rather than replacing the role, AI elevates PMs to become strategic integrators of technology, business, and human values. Those who embrace AI as a foundational capability will define the next era of product leadership.

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AI’s Next Competitive Advantage Is Trustworthy Execution, Not Bigger Models

Over the past week, the AI industry has delivered an important message to product leaders: the race is no longer about who ships the biggest model first. Instead, it’s about who can deliver AI that enterprises trust, regulators accept, and customers rely on every day. Recent developments involving OpenAI, Google, Anthropic, and policymakers in the…

bhaskaransunil's avatar by bhaskaransunil

The Next AI Moat Isn’t Intelligence—It’s Economics

This week’s AI developments revealed a shift that product managers can’t afford to ignore. While new models continue to arrive at a rapid pace, the industry’s conversation is increasingly centered on economics, distribution, and sustainable adoption rather than raw intelligence. Updates from OpenAI, Google, Anthropic, and Microsoft point toward the same conclusion: The next generation…

bhaskaransunil's avatar by bhaskaransunil