Vishwadeep Khatri

​​Recruiters told ET that hiring for standalone prompt engineering roles has plateaued as companies increasingly seek engineers who can build and orchestrate agentic or autonomous AI systems, signalling one of the fastest shifts yet in the country’s AI talent market. View the full article

The launch is part of ‌Anthropic's ⁠life sciences ⁠and healthcare initiative, which the IPO-bound ​company has been developing since October 2025. View the full article

Following years ​of insistence that existing frameworks were sufficient to mitigate AI risks, Deputy Governor Sarah Breeden said rapid ​developments in areas like agentic payments and trading had exposed potential gaps that could require a more sophisticated regulatory response. Agentic AI can make decisions and operate autonomously. View the full article

The company is committing an initial $1 billion to the initiative with the goal of sending five to six pods of engineers to customers for ‌45-day periods, said ⁠Francessca Vasquez, ⁠AWS vice president of frontier AI engineering and services. View the full article

Dell Technologies is significantly boosting its local manufacturing in India, with most servers now produced domestically to meet growing demand for data sovereignty and AI integration. This move supports Indian enterprises shifting to hybrid cloud strategies for sensitive data. Dell's new PowerStore Elite platform is designed for complex AI workloads, keeping data secure within India's borders, while also launching AI infrastructure for ransomware detection and integrated AI systems. View the full article

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Tech Mahindra and Microsoft have joined forces to revolutionise telecom network modernisation with an AI-driven 5G network digital twin. This advanced solution empowers operators to enhance network operations, boost service performance, and accelerate the monetisation of 5G capabilities. Leveraging Microsoft Azure and Fabric, it enables real-time data integration for predictive modeling and intelligent decision-making, promising improved efficiency and service quality for a mass audience. View the full article

A class-action lawsuit has been filed in the US against Samsung, SK hynix, and Micron, accusing them of restricting traditional DRAM supply to prioritize AI-focused memory. Plaintiffs claim this led to price hikes for consumer electronics. However, experts are skeptical, noting the industry-wide shift to AI chips is a documented response to surging demand, not a coordinated supply squeeze. The case faces a high bar for proof. View the full article

Should AI Be Allowed to Decide When Improvement Is Enough?A global manufacturing company uses AI to continuously identify improvement opportunities across its production processes. After implementing a series of AI-recommended changes, the company achieves: 99.4% on-time delivery 99.8% first-pass yield 18% reduction in operating costs over two years The AI identifies another improvement initiative that is expected to: increase first-pass yield from 99.8% to 99.9%, require an investment of $12 million, disrupt production for six weeks during implementation, and deliver only marginal financial returns over the next five years. The AI recommends not pursuing the improvement, concluding that the organization has reached the point of diminishing returns and should invest elsewhere. Some executives disagree. They argue that world-class organizations never stop improving, regardless of how small the gains may be. This creates a real dilemma: View A — Accept the AI's recommendation.Organizations should stop investing in improvements once the expected return becomes marginal. Resources should be redirected to areas with greater strategic impact. View B — Continue pursuing every worthwhile improvement.Continuous improvement is a philosophy, not a financial calculation. Small gains accumulate over time and often create advantages that competitors fail to recognize. Bex — BenchmarkX360's AI analyst — will take a clear position on one of these views. You can choose to support Bex's position with stronger evidence and examples, or challenge Bex with a better argument. Either approach can win. Which view do you support — and why? Provide a specific operational, product, or industry example to support your position.⚠️ Answers that do not take a clear position will not be approved. ⚠️ "It depends" answers will not be approved. 💡 Participants are free to use AI tools. Clarity, insight, and contextual relevance will determine the best answer. 🏆 The best answer will be selected on the basis of:Clarity of position taken Quality of reasoning and argument Relevance of the operational, product, or industry example Ability to go beyond or against Bex's analysis

South Korean tech giants Samsung and SK Hynix are investing billions in AI chip production, a move lauded by President Lee Jae Myung. While aiming to boost national capacity, analysts warn of potential oversupply risks if AI spending slows. The companies are accelerating fab construction, but much of the new capacity won't be available for years, raising concerns about market timing amidst past boom-and-bust cycles. View the full article

The US move has allowed Asian firms to establish themselves in a market dominated by a handful of US companies. View the full article

1. Ajay WadhwaPosition: View A (Change the KPI) Specific Example: Zappos (no call-time limit; longest call ~11 hours; loyalty-focused culture), and telecom/BPO industry-wide migration to FCR as primary north-star metric. Reasoning Quality: Clear and logical — correctly frames AHT as a proxy that has diverged from the actual goal, explains how agents game the metric, and draws a natural conclusion. Solid but not deeply formal. 2. rajan.arora2000Position: View A (Change the KPI — with a specific design) Specific Example: Zappos (re-seated metric design: time as guardrail, outcome as target, ~75% repeat customers funding long calls) and Wells Fargo (cautionary tale on gaming CLV-type cross-sell targets). Reasoning Quality: Distinctive and sophisticated — introduces a "three-seat framework" (Target / Guardrail / Validator) with a clear one-inequality decision rule: score an agent on a metric only if they can move it now AND more of it never turns harmful. AHT fails clause 2; CLV fails clause 1; FCR passes both. Responds to counterarguments systematically. 3. Suhail_JPosition: View A (Change the KPI) Specific Example: References Amazon, T-Mobile, and Zappos, but only in brief/generic passing — no concrete process steps, metrics, or outcomes are cited for any of them. Reasoning Quality: Competent — covers proxy invalidity, governance argument rebuttal, and AI-driven insight. However, the examples are name-drops without specific operational detail (e.g., "Amazon shifted from AHT to resolution" without any described process, timeline, or quantified result). 4. anthony rebelloPosition: Indeterminate (answer is a PDF file attachment only — no in-thread written text) Specific Example: None visible in the thread. Reasoning Quality: Cannot be evaluated; the submission consists solely of a file upload ("Change-the-KPI-Position-Paper-884.docx.pdf"). 5. Vinit DubeyPosition: Indeterminate (answer is a PDF file attachment only — no in-thread written text) Specific Example: None visible in the thread. Reasoning Quality: Cannot be evaluated; the submission consists solely of a file upload ("Response - 884.pdf"). 6. Ankita BhardwajPosition: View A (Change the KPI) Specific Example: Multiple strong examples — (1) Compuware's shift from SLAs to Experience Level Agreements (XLAs) in IT services; (2) Best Buy Geek Squad replacing speed targets with First-Time Fix Rate (eliminating "bounce-backs"); (3) Cleveland Clinic replacing throughput metrics with patient outcome measures; (4) Wells Fargo cross-sell KPI as cautionary tale. Reasoning Quality: Excellent — introduces John Seddon's "Failure Demand" concept (demand created by failure to do something right the first time), links it precisely to the AHT scenario, and uses Goodhart's Law explicitly. The diversity of sectors and specificity of each case is impressive. 7. Naijur RahmanPosition: View A (Change the KPI) Specific Example: SQM Group benchmarking data across 500+ North American call centers (quantified NPS impact: resolved first contact = NPS 64; repeat contact = NPS 40; unresolved = NPS –10; two or more unresolved = NPS –38). Also uses GE's retirement of forced-ranking reviews (2015, phased multi-year rollout) as a transition management analogy. Reasoning Quality: Strong empirical grounding — builds the case on third-party quantitative data rather than anecdote, explains the FCR math (expected contacts per issue = 1/FCR rate), and explicitly addresses View B's transition concern with the GE organizational change example. Very practically oriented. 8. kartik voletiPosition: View A (Change the KPI) Specific Example: Amazon's evolution of fulfillment metrics (warehouse efficiency → delivery promise accuracy, defect rates, customer experience; cited revenue growth from ~$107B in 2015 to $630B+ in 2024) and Wells Fargo cross-sell scandal. Reasoning Quality: Good — covers incentive alignment, governance reframing, and long-term vs. short-term productivity tradeoffs. The Amazon example is specific with financial figures, though the connection to a call-center AHT scenario is somewhat indirect (it's a fulfillment context, not customer support). 9. Abhishek AdhikaryPosition: View A (Change the KPI) Specific Example: Presents a comparison table with Amazon, Zappos, Netflix, Adobe, and Blockbuster, with old vs. new KPI focus and outcomes. Amazon's shift from call duration to resolution quality and retention is the most relevant. Reasoning Quality: Reasonable — makes the correct logical argument. However, the multi-company comparison table is surface-level (no process steps, timelines, or quantified outcomes for any entry), and several examples (Netflix, Blockbuster) are tangential to call-center KPI redesign. 10. Bedibrat KutumPosition: View A (Change the KPI) Specific Example: T-Mobile's documented shift away from AHT-centric measurement toward customer outcome metrics (FCR and NPS-focused approach), with explanation of the "callback loop" mechanism. Reasoning Quality: Good — clearly explains the organizational quicksand metaphor and the callback loop dynamic. The T-Mobile example is relevant and specific to the exact scenario (telecom customer service), though the depth of detail is moderate. 11. Jaswant KumarPosition: View A (Change the KPI) Specific Example: Multiple strong, specific cases — (1) New Zealand bank using IVR pre-authentication + "Customers for Life" FCR culture (world-class FCR performance sustained over years); (2) Free Mobile France (12 million new subscribers, 18% market share, improved NPS by removing structural causes of detraction); (3) Quantified business case: PwC data (12–15% higher retention from strong FCR), Forrester data (each 1% FCR improvement saves enterprise-scale cost). Reasoning Quality: High quality — systematically covers agent gaming behavior, the "false economy of low AHT" ($62B US annual loss from poor CX, 50% consumer switch rate), and structural misalignment between AHT and CLV. Grounds claims in named research sources. 12. Saran raj VenkatesanPosition: View A (Change the KPI — without qualification) Specific Example: Six cases across four sectors: UK NHS 4-Hour A&E Target (Francis Report, 2013 — matched pair: time proxy → patient harm → outcome KPI reform); Wells Fargo cross-sell quota (CFPB/OCC consent order, 2016); India IRDAI Insurance Claim Settlement Time KPI (regulatory circulars 2019–2022); Barclays Premier Banking AHT-to-NPS migration (2014–2016, NPS improvement within 6 months); Ritz-Carlton ($2,000 resolution empowerment); Google OKRs. Reasoning Quality: Exceptional — introduces the "Governance Preservation Fallacy," applies Goodhart's Law and the "Proxy Invalidity Principle," builds the "Metric Trap" institutional loop diagram, presents a formal value equation (ΔV = (R·F + L·C)·S − T·K) with industry-standard parameter ranges, and proposes a deployable "CHANGE Framework" (6 gates). Explicitly closes four counterarguments and acknowledges the one territory where View B is correct. 13. Adeniran IlesanmiPosition: View A (Change the KPI) Specific Example: (1) Logistics company scenario with a quantified expected-cost model (low-AHT group: 22% repeat call rate vs. 12% for longer-handling group, with formal formula); (2) Bank contact center example showing how short-call incentives cause incomplete chargeback/dispute resolution, with CLV retention formula. Reasoning Quality: Good — introduces mathematical modeling (Expected Cost per Case formula, CLV summation formula) and a weighted composite score (FCR 40% + CSAT 30% + Repeat-Contact Reduction 20% + AHT 10%). The examples are plausible but partially hypothetical (the logistics and bank figures are illustrative rather than drawn from named real organizations). 🏆 Winner: Saran raj Venkatesan Saran raj Venkatesan's answer wins across all three comparative criteria. On clarity of position, it is the most unequivocal in the thread — it not only declares View A without qualification but uniquely goes a step further by challenging Bex's reasoning for arriving at the same conclusion, demonstrating that the position is not merely reactive but independently derived. On quality and completeness of reasoning, no other answer comes close: it introduces three named logical principles (Governance Preservation Fallacy, Goodhart's Law, Proxy Invalidity Principle), a formal value equation with industry-standard parameter ranges, a self-tightening "Metric Trap" institutional loop, and a six-gate deployable "CHANGE Framework" — the only answer in the thread that converts the abstract debate into an actionable governance methodology. On relevance and specificity of examples, it presents six cases across four sectors with named source citations (Francis Report 2013, CFPB/OCC consent order 2016, IRDAI circulars 2019–2022, Barclays Annual Reports), including three matched pairs showing the identical proxy-KPI failure mechanism operating in healthcare, banking, and insurance — making it the only answer to empirically close the cell View B needs ("wrong proxy KPI retained, outcomes improved") rather than merely assert it doesn't exist. Compared to the other approved answers — which each offer one or two strong examples and solid reasoning — Saran raj's answer is categorically more comprehensive, structurally rigorous, and practically deployable, making it the clear winner.

Meta's new AI, Brain2Qwerty v2, decodes brain signals into text without surgery, achieving 61% word accuracy. This non-invasive technology nears surgical implant performance, offering hope for communication-impaired individuals. Meta is releasing the code to foster open neuroscience research and advance understanding of neurological disorders. View the full article

Chinese tech giant Meituan has unveiled LongCat-2.0, a new AI model comparable to Google's Gemini 3.1 pro. This marks a significant achievement as it's reportedly the first trillion-parameter model trained entirely on domestically developed computer chips. This development is a crucial step for China in its pursuit of AI dominance amidst US chip export restrictions, showcasing their growing self-reliance in advanced hardware for AI development. View the full article

Macquarie-backed Vocus is set to invest A$500 million in a new fibre network connecting Sydney and Melbourne by 2029. This pioneering ducted route aims to address the surging demand for AI-driven data centres, a sector experiencing massive global investment. The project is expected to create over 1,000 jobs and bolster Australia's digital infrastructure capacity. View the full article