Network People Services Technologies Limited

Who it hits first

• A report estimates a small 0.4% merchant fee (MDR) on UPI shop payments could create a Rs 15,000 crore yearly pool.
• State Bank of India, the country's largest public bank, and HDFC Bank, the largest private bank, handle huge UPI volumes and would share in that fee income.
• Payment firms such as Paytm, Pine Labs and MobiKwik, which provide UPI apps and shop checkout machines, would also gain a slice if fees are shared.

Who may gain

• Big UPI banks such as State Bank of India and HDFC Bank, which would earn fees on merchant payments they process
• Payment firms such as Paytm, Pine Labs and MobiKwik, which run UPI apps and shop checkout machines that share in fees
• Payment-tech vendors such as NPST, which sell processing software to banks riding a fee-funded upgrade

Along the supply chain

Where demand moves

How it spreads across sectors

When it plays out

Who it hits first

• State Bank of India, the country's biggest lender and UPI player, expects to earn a surplus from newly introduced UPI transaction (MDR) charges.
• The charge turns UPI volumes from a cost centre into fee income for acquiring and issuing banks.
• Payment intermediaries such as Paytm, MobiKwik, Pine Labs and NPST sit in the same chain but must share the fee pool with banks.
• Merchants and possibly shoppers ultimately bear the charge, which could slightly dampen small-ticket digital payments.

Who may gain

• State Bank of India: direct fee-income surplus on its outsized UPI volumes.
• Other UPI acquirer banks such as RBL Bank and IndusInd Bank: smaller but real fee readthrough.
• UPI software and terminal providers (NPST, KFin Technologies, Pine Labs): steadier customer tech spend.

Along the supply chain

Where demand moves

How it spreads across sectors

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Who it hits first

• Mastercard, the global card network, will sell up to 49.7 million shares of Pine Labs (a payment-terminal company) for up to Rs 893 crore, about $93 million, through a block deal (a large pre-arranged share sale).
• Pine Labs, which supplies card machines to HDFC Bank, SBI, Axis Bank and ICICI Bank, faces near-term share pressure as this large parcel hits the market.
• Payment rivals One 97 Communications (Paytm), PB Fintech (Policybazaar), MobiKwik and NPST soften in sympathy as fintech investors mark down the sector.
• The business itself is untouched: terminals keep working, banks keep paying fees, and only the shareholder list changes.

Who may gain

• Institutional block buyers - pick up a large Pine Labs parcel at a discount to market
• Vanguard index funds - already bought 8.235 million and 8.533 million Pine Labs shares and can absorb more
• No business beneficiary - Pine Labs operations and its bank customers are unaffected, so gains flow only to buyers of the discounted shares

Along the supply chain

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Who it hits first

• Indian IT-services & AI firms get a favorable policy environment for US-India tech collaboration
• AI-infrastructure/server (NETWEB), chip-design (MOSCHIP) and design-engineering (TATAELXSI) firms positioned for cross-border AI co-development
• Soft MEA statement (not a binding deal/contract) -> modest, medium-term, sentiment-led impact

Who may gain

• AI-compute hardware (NETWEB)
• Semiconductor/chip-design (MOSCHIP)
• Engineering R&D/design (TATAELXSI)
• Govt/e-governance tech (SILVERTUC)
• Payments/fintech tech (NPST)

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Where demand moves

How it spreads across sectors

When it plays out

Other sectors it reaches

• {"causal_chain":"India-US AI cooperation -\u003e higher enterprise/cloud AI workloads -\u003e need for domestic data-centre capacity, power-dense hosting, cooling and managed infrastructure","direction":"positive","example_tickers":["ANANTRAJ","ESCONET","STLTECH"],"magnitude":"medium","notes":"Second-order beneficiary from AI compute localisation, distinct from server hardware. (Codex Layer 5.5)","sector":"Data Centres \u0026 Digital Infrastructure","time_horizon":"1_to_6_months"}
• {"causal_chain":"AI tie-up -\u003e more cloud/edge AI use cases -\u003e higher data traffic and enterprise private-network demand -\u003e capex in fiber, 5G, edge connectivity","direction":"positive","example_tickers":["BHARTIARTL","INDUSTOWER","TEJASNET"],"magnitude":"medium","notes":"Benefit depends on actual enterprise AI deployment. (Codex Layer 5.5)","sector":"Telecom \u0026 5G Network Infrastructure","time_horizon":"1_to_6_months"}
• {"causal_chain":"AI compute and data-centre expansion -\u003e rising electricity demand and reliability needs -\u003e demand for power supply, grid gear, transformers, backup","direction":"positive","example_tickers":["NTPC","POWERGRID","TRIL"],"magnitude":"medium","notes":"AI infrastructure is power-intensive; gradual but structurally supportive. (Codex Layer 5.5)","sector":"Power Utilities \u0026 Grid Equipment","time_horizon":"1_to_6_months"}
• {"causal_chain":"Strategic-tech cooperation -\u003e more investment in electronics, data centres and semiconductor-adjacent facilities -\u003e demand for automation, switchgear, power systems","direction":"positive","example_tickers":["SIEMENS","ABB","CGPOWER"],"magnitude":"medium","notes":"Broader capex enabler, not a direct AI beneficiary. (Codex Layer 5.5)","sector":"Capital Goods \u0026 Electrical Equipment","time_horizon":"1_to_6_months"}
• {"causal_chain":"Cross-border AI collaboration -\u003e greater data-sharing, model-security and compliance needs -\u003e demand for cybersecurity, identity, cloud-security, managed security","direction":"positive","example_tickers":["QUICKHEAL","SAKSOFT","CYIENT"],"magnitude":"small","notes":"Listed pure-play cybersecurity choices limited in India. (Codex Layer 5.5)","sector":"Cybersecurity \u0026 Digital Trust","time_horizon":"1_to_4_weeks"}
• {"causal_chain":"Favorable India-US AI policy -\u003e easier enterprise AI partnerships and productisation -\u003e SaaS firms embed AI, improve pricing power, address US clients","direction":"positive","example_tickers":["NEWGEN","RATEGAIN","INTELLECT"],"magnitude":"medium","notes":"Benefit from product/platform AI monetisation. (Codex Layer 5.5)","sector":"Cloud/SaaS \u0026 Digital Platforms","time_horizon":"1_to_6_months"}
• {"causal_chain":"AI collaboration narrative -\u003e demand for AI talent, reskilling, certifications, hiring support -\u003e training and staffing firms see higher enterprise spend","direction":"positive","example_tickers":["NIITLTD","TEAMLEASE","QUESS"],"magnitude":"small","notes":"Lagged, dependent on corporate training budgets. (Codex Layer 5.5)","sector":"Education, Skilling \u0026 HR Services","time_horizon":"1_to_6_months"}
• {"causal_chain":"India-US AI cooperation -\u003e more cross-border data, IP, model-risk and regulatory work -\u003e demand for compliance tech, governance workflows, regtech","direction":"positive","example_tickers":["CAMS","KFINTECH","INTELLECT"],"magnitude":"small","notes":"Mostly indirect; exposure via compliance-heavy fintech platforms. (Codex Layer 5.5)","sector":"Legal, Compliance \u0026 Data Governance Services","time_horizon":"1_to_6_months"}
• {"causal_chain":"AI tools and US partnerships -\u003e faster content generation, personalisation, ad targeting -\u003e productivity upside but disruption to legacy content models","direction":"mixed","example_tickers":["NAZARA","ZEEL","AFFLE"],"magnitude":"small","notes":"AI lowers costs for digital firms while pressuring traditional content economics. (Codex Layer 5.5)","sector":"Media, Internet \u0026 Ad-Tech","time_horizon":"1_to_6_months"}

Who it hits first

• Truist cut its price target on Accenture (ACN; US-listed, not in the Indian knowledge graph) after Q3, signaling softer global IT-services discretionary demand. As the sector bellwether, ACN's caution reads through to the largest Indian IT exporters (TCS, INFY, WIPRO, HCLTECH) and richly-valued mid/small-cap IT names — a sentiment overhang, not a hard order-book shock.

Who may gain

• No material domestic beneficiary — a demand-softness read-through has no clear winner. Within IT, relative preference rotates toward cheaper, stronger-balance-sheet large caps (TCS PE 14.5, INFY PE 14) over richly-valued mid/small caps; domestic-revenue (NPST) and AI-hardware (NETWEB) names are insulated rather than beneficiaries.

Along the supply chain

Where demand moves

How it spreads across sectors

When it plays out