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  • OpenAI’s ChatGPT and Codex Reach Nearly a Billion Weekly Users—What That Signals for AI Interfaces and Software Engineering

    This article was generated by AI and cites original sources.

    OpenAI president Greg Brockman says the company’s AI tools, ChatGPT and Codex, are now used by nearly a billion people weekly. As reported by Tech-Economic Times, the scale points to a shift in how many people interact with computers—moving from traditional interfaces toward systems that adapt to user inputs in natural language and related workflows.

    ChatGPT and Codex: AI as a weekly interface for nearly a billion users

    The central claim from Brockman is straightforward: OpenAI’s ChatGPT and Codex now serve nearly a billion users weekly, according to the Tech-Economic Times report. While the source does not break down whether the figure represents unique users across both products or usage frequency per product, it frames the milestone as evidence that these tools have become common entry points into computing tasks.

    The report also highlights a specific interaction model: AI adapting to users. In practical terms, this suggests that the software experience is increasingly shaped by what a user types or asks, rather than by navigating fixed menus. The source does not specify the technical mechanisms behind that adaptation, but the framing aligns with how conversational systems and code-assistance tools typically respond to prompts, constraints, and iterative feedback.

    From chat to code: Codex and developer workflows

    The Tech-Economic Times report ties OpenAI’s product pair to a broader computing shift: software engineering is expected to be the first sector to experience disruption. That expectation is presented as part of the article’s implications rather than a quantified forecast, but it points to the role of Codex as an AI coding tool connected to software creation and maintenance.

    In the source material, the disruption claim is linked to the idea that AI is lowering friction between an idea and executable output. Even without additional technical details, the emphasis on “software engineering” indicates that the most immediate operational impact may show up where developers translate requirements into code, test results, and iteration cycles—areas where AI assistance can shorten the time between intent and implementation.

    Because the article does not provide benchmarks (for example, time-to-implementation, code quality metrics, or adoption rates by team size), readers should treat the “first sector” statement as a directional industry expectation rather than a measured outcome.

    Lower barriers for entrepreneurship: the idea-to-reality pipeline

    Beyond software engineering, the report connects broad consumer usage to a second effect: a new wave of entrepreneurship, with lowered barriers for new ideas to become reality. The causal chain in the synopsis is not supported with figures in the source, but it implies a technology-driven pipeline change: if AI tools are widely accessible and capable of turning prompts into working artifacts, more people may prototype and ship without needing the same level of specialized setup or staffing as before.

    From a technology perspective, this could shift the practical unit of development from “assembling tools” to “describing outcomes.” If AI systems are widely used weekly—again, “nearly a billion” per the report—then the interface pattern becomes familiar across user groups, which could accelerate experimentation and reduce the learning curve for producing software or code-adjacent outputs.

    However, the source does not specify what kinds of projects users are building, what percentage of outputs become deployed products, or how teams validate correctness and security. Those gaps mean any conclusion about real-world business outcomes would be speculation beyond the provided material.

    What this scale could mean for the AI industry

    The most material detail in the Tech-Economic Times report is the adoption level: nearly a billion weekly users of ChatGPT and Codex. At that scale, AI assistants move from novelty to infrastructure—something many users rely on regularly for tasks that previously required separate applications, specialized knowledge, or manual steps.

    For the broader industry, this could pressure competitors and adjacent platforms to rethink interaction design around conversational and assistive AI rather than only around traditional search, forms, or IDE-only workflows. The source does not mention specific rivals or market moves, so observers should limit conclusions to what follows logically from the reported usage milestone: widespread weekly adoption can change user expectations about what “computer interaction” looks like.

    The report’s specific emphasis on software engineering suggests a likely first testing ground for these expectations. If AI-based coding support becomes routine for large numbers of users, the ecosystem around development—documentation practices, review workflows, testing habits, and tooling integration—may need to adapt. The synopsis does not provide evidence of these process changes, but it frames them as a likely early disruption point.

    Finally, the entrepreneurship angle implies that AI tools are not only consuming compute but also enabling new production patterns. If barriers are truly lower, then more experiments may be launched by people who previously could not translate an idea into working software. Again, the source does not quantify this shift, but the claim is tied directly to the reported adoption scale and the idea of AI adapting to user needs.

    In sum, the Tech-Economic Times report—citing OpenAI president Greg Brockman—places ChatGPT and Codex at a massive usage level and links that scale to two technology-adjacent outcomes: anticipated disruption in software engineering and a broader expansion of who can build. The details provided do not include performance benchmarks or product breakdowns, but the reported “nearly a billion” weekly users offers a concrete data point for understanding how quickly AI interfaces are moving into everyday computing.

    Source: Tech-Economic Times

  • Google DeepMind Hires Philosopher Henry Shevlin to Focus on Machine Consciousness and Human-AI Relationships

    This article was generated by AI and cites original sources.

    Google DeepMind has appointed Henry Shevlin to a philosopher position focused on machine consciousness, human-AI relationships, and AGI readiness. The hire signals that leading AI labs are integrating academic expertise from philosophy and related fields into their research operations.

    The Appointment

    According to mint, DeepMind’s new hire is not an AI engineer or researcher. Instead, the lab has created a role explicitly titled as a philosopher position. Shevlin will work on topics including “machine consciousness,” “human-AI relationships,” and “AGI readiness.”

    In a post on X (formerly Twitter), Shevlin announced that he would be joining DeepMind in May. He also indicated he would continue his research and teaching at Cambridge on a part-time basis. This part-time arrangement suggests DeepMind is integrating the role into ongoing academic and industry work streams rather than relocating the entire research agenda around this position.

    Who Henry Shevlin Is

    Shevlin currently serves as Associate Director (Education) at the Leverhulme Centre for the Future of Intelligence, University of Cambridge. According to mint, he has expertise across cognitive science, AI ethics, animal minds, and consciousness. He has published multiple papers in journals including the Journal of Consciousness Studies.

    Originally from rural England, Shevlin earned a BA in Classics and a BPhil in Philosophy from the University of Oxford. He later completed his PhD in philosophy at the CUNY Graduate Center between 2010 and 2016, and served as a lecturer at Baruch College during that period.

    Research Focus Areas

    DeepMind’s stated focus areas—machine consciousness, human-AI relationships, and AGI readiness—form a cluster of research themes. The mint article does not provide technical deliverables, evaluation methods, or specific integration points with DeepMind’s model development process.

    The choice of topics reflects a pattern in the AI industry: as systems become more capable, labs increasingly discuss not only performance but also interpretation, interaction, and readiness for more general capabilities. A philosopher role could help operationalize questions that are difficult to reduce to standard benchmarks.

    For example, “machine consciousness” is presented as a research area rather than a specific engineering feature or measurement. Similarly, “human-AI relationships” and “AGI readiness” are listed as focus topics without technical definition in the source material.

    Industry Precedent

    This hiring move reflects a broader trend in AI research. According to mint, this is “not the first time that an AI company has hired a philosopher.” Late last year, Anthropic hired Amanda Askell, a PhD philosopher and AI researcher, to work as an in-house philosopher on areas including AI alignment and fine-tuning.

    The Anthropic example suggests that philosopher roles in AI labs can be tied to technical work such as alignment and fine-tuning, rather than serving only public relations or ethics functions. For DeepMind’s appointment, the source material does not specify whether Shevlin’s work will connect to model training, alignment methods, or evaluation.

    What This Signals

    DeepMind’s appointment of Henry Shevlin indicates that “human-AI relationships” and “machine consciousness” are being treated as research topics worth staffing at a major AI lab. The practical impact—what changes in systems, processes, or evaluation—remains unspecified in the source material. However, the creation of a philosopher position suggests that DeepMind is investing in conceptual frameworks that could influence how teams reason about advanced AI capabilities and their interaction with people.

    Industry observers may watch whether the role produces publications, technical guidance, or internal frameworks that align the lab’s engineering work with the stated research focus areas.

    Source: mint – technology

  • Booking.com breach exposes reservation data and enables targeted phishing attacks

    This article was generated by AI and cites original sources.

    Booking.com confirmed that hackers breached its systems and accessed customers’ personal data, warning that “unauthorised third parties may have been able to access certain booking information associated with your reservation.” The company said it noticed “suspicious activity affecting a number of reservations” and took steps to contain the issue, including updating the PIN number for affected reservations. While Booking.com told The Guardian that “financial information was not accessed,” the incident highlights how reservation platforms can become targets for data theft and follow-on social engineering.

    What Booking.com says was accessed

    In its confirmation, Booking.com did not disclose the exact number of people affected, the regions impacted, or the timeframe of the breach. However, it did clarify that “financial information was not accessed,” according to reporting by mint. The company’s message to customers, as shared in notifications circulated on social media, focused on the scope of booking-related data that could have been exposed.

    Based on customer notifications discussed in the mint report (including a screenshot shared by a Reddit user), Booking.com said that unauthorised parties may have accessed “certain booking information associated with your reservation.” The company warned that hackers may have gained access to names, email addresses, phone numbers, and specific booking details. It also stated that attackers could view “anything that you may have shared with the accommodation.”

    That last point is significant from a data-security standpoint because it suggests the breach may not have been limited to a narrow set of database fields. Instead, the notification language indicates that data flows between Booking.com and accommodations—such as messages or other content shared in the context of a stay—may have been accessible to the attackers under the compromised access.

    Containment steps: PIN resets and direct guest notification

    Booking.com said it “recently noticed suspicious activity affecting a number of reservations and we immediately took action to contain the issue,” as quoted in the customer notification message shared on Reddit and reported by mint. Booking.com spokesperson Courtney Camp told TechCrunch (as referenced by mint) that the company noticed “suspicious activity involving unauthorised third parties being able to access some of our guests’ booking information.” She added that Booking.com “took action to contain the issue,” updated PIN numbers for affected reservations, and directly informed guests.

    Updating reservation PINs serves as a security control: it can disrupt attacker attempts to authenticate or apply changes tied to those reservations. The company’s approach reflects how reservation systems often rely on secondary verification beyond passwords—especially when customers manage bookings through confirmations, links, or reservation-specific credentials.

    At the same time, Booking.com’s decision not to disclose the breach window, impacted regions, or affected population size leaves outside observers with fewer technical details about how long the attackers may have had access and how widely the exposure may have spread across systems.

    Stolen booking data enables targeted phishing

    According to the mint report, a user who posted the notification screenshot said they received a targeted phishing message via WhatsApp two weeks earlier. The message reportedly included personal information and booking details that matched what the company later said could have been accessed.

    This suggests attackers may be using stolen reservation data to make social engineering more convincing—an approach that does not require direct access to payment systems to be harmful. Even if “financial information was not accessed,” attackers could still attempt to redirect payments, harvest additional credentials, or manipulate communications between travelers and accommodations.

    The mint report notes Booking.com’s guidance for staying safe: if users were affected, they should look for an official confirmation in their mailbox. For recent bookings, the report advises travelers to be “extremely wary of urgent payment requests from hoteliers” and to prefer payment only through Booking.com’s official portals. That advice aligns with a common pattern in incident responses for consumer platforms: when attackers can reference real booking details, urgency-based prompts can become a tactic to bypass normal verification steps.

    Prior breach and regulatory context

    Booking.com’s history provides context for the current incident. According to the mint report, Booking.com suffered a phishing attack in 2018 that compromised booking data of over 4,000 customers. In that earlier case, the platform reportedly had login credentials stolen from hotel employees in the UAE. Booking.com was later fined €475,000 by the Dutch Data Protection Authority for reporting the breach 22 days late, exceeding the 72-hour legal limit.

    While the mint summary does not provide technical details on how the 2018 attack operated beyond the credential theft mechanism, it underscores a recurring pattern: phishing remains an entry point into larger reservation ecosystems, and data exposure can extend beyond a single user account to include booking-associated records and partner interactions.

    Looking forward, observers may watch how Booking.com’s incident response is operationalized—particularly the speed and completeness of customer communications, the effectiveness of PIN resets in thwarting account-linked changes, and how the company validates whether shared content with accommodations was accessed. The lack of disclosed details about the breach timeframe and affected regions in the current reporting may also affect how quickly security researchers and affected users can assess impact.

    What this means for reservation platforms

    The confirmed breach, the specific categories of data mentioned in customer notifications, and the reported WhatsApp phishing tie-in point to a security challenge that extends beyond perimeter defense. Reservation systems handle identity attributes (names, emails, phone numbers), itinerary context (specific booking details), and potentially communication artifacts (“anything that you may have shared with the accommodation”). If attackers can access those records, they can increase the credibility of downstream scams even when direct payment systems are not compromised.

    Booking.com’s stated control—updating PIN numbers for affected reservations—shows how platform-specific authentication mechanisms can be used to contain harm after unauthorized access is discovered. Meanwhile, the company’s consumer-facing guidance to use official payment portals and to scrutinize urgent requests reflects the reality that attackers can exploit real booking context to drive fraudulent actions.

    Source: mint – technology

  • ESOP buybacks rebound in Q1 2026: Indian startups push employee liquidity with $2B in payouts since 2020

    This article was generated by AI and cites original sources.

    Indian startup employee liquidity programs are showing signs of a rebound. Data compiled by Entrackr reports that ESOP buybacks in the first quarter of 2026 already surpassed the full-year figures for both 2024 and 2025, with seven startups collectively buying back ESOPs worth nearly $220 million in Q1 2026—versus just over $75 million in 2025. The shift matters for how startups structure equity compensation, how employees convert vested options into cash, and how liquidity planning changes as companies move toward public markets.

    From 2021–2022 peak to mid-cycle slowdown

    Entrackr frames the recent pattern as a cycle. It describes 2021 to 2022 as a period when ESOP buybacks, liquidity, and payout programs were “common,” a phase it links to “strong venture capital inflows” that many observers associate with a “golden phase” for Indian startups. According to the same compilation, momentum later slowed: 2023 saw fewer buybacks (in terms of number), and 2024 and 2025 saw declines further in terms of total value.

    The numbers Entrackr provides show how steep that drop was. Total ESOP-related value stood at around $190 million in 2024, compared with $802 million in 2023, $440 million in 2021, and $200 million in 2022. This matters technologically and operationally because ESOP buybacks are one of the mechanisms startups use to manage the “equity-to-cash” pathway without changing core product teams or hiring plans; when the buyback pipeline dries up, employees may have fewer options to monetize equity during periods of slower fundraising or valuation pressure.

    Entrackr says the cumulative effect since the start of 2020 is now approximately $2 billion—specifically $1,977 million—in ESOP buybacks by Indian startups. While this figure is aggregated across multiple companies and years, it signals that equity liquidity has become a persistent operational pattern rather than a one-off event tied only to early funding booms.

    Q1 2026: buybacks accelerate, with BrowserStack and Innovaccer leading

    Entrackr’s Q1 2026 snapshot highlights the scale of the rebound. It reports that seven startups have collectively bought back ESOPs worth nearly $220 million in the quarter. For comparison, Entrackr notes that buyback and payout activity remained “subdued” in 2025 at just over $75 million.

    Within that Q1 2026 activity, Entrackr identifies Mumbai-based BrowserStack as leading ESOP liquidity events with a $125 million share buyback programme aimed at employees and early investors. The program is described as enabling nearly 500 employees to sell their shares, with roughly half of the total amount reserved for employees and the remaining portion allocated to early backers such as Accel. For technology companies—especially those whose employee compensation relies heavily on equity—these details illustrate how liquidity programs can be designed to target specific stakeholder groups and manage cash allocation across cohorts.

    Entrackr also reports that healthtech firm Innovaccer has completed a $75 million ESOP buyback offering liquidity to current and former employees holding vested stock options. It adds that media reports indicate holders of restricted stock units also benefited, though “the exact number remains undisclosed.” Even without the employee-count detail, the inclusion of both vested stock options and RSUs suggests that equity instruments with different vesting and ownership structures are being folded into liquidity planning.

    Company-by-company signals: from Flipkart’s $700M to CoinDCX’s $12M

    Entrackr’s historical comparison includes a notable data point from 2023: Flipkart contributed $700 million to the total through ESOP liquidity provided as compensation for the decline in value following the PhonePe spin-off. It then reports that other startups together accounted for $102 million in buybacks that year. The implication here is that ESOP liquidity can be triggered not only by routine valuation and recruiting strategies, but also by corporate events that alter equity value, requiring a structured compensation mechanism.

    In the current cycle, Entrackr reports more modest activity from CoinDCX, which repurchased ESOPs worth $12 million. It also notes that Unacademy rolled out a Rs 50 crore ($5.5 million) ESOP buyback programme to provide liquidity to its workforce. Entrackr includes specific expectations from founder Gaurav Munjal: eight employees are expected to earn over Rs 1 crore each, 17 employees will receive more than Rs 50 lakh, and 38 employees are likely to make upwards of Rs 10 lakh. The same section also ties the timing to the edtech sector’s fundraising challenges, stating that the SoftBank-backed firm was eventually acquired by upGrad.

    Entrackr lists additional startups that participated in ESOP buybacks in 2026, including Emversity, Atlys, Cashfree, and Kratikal. While the source does not provide buyback amounts for each of these companies in the excerpt, their inclusion indicates that the practice is spreading across multiple verticals—software testing, healthtech, fintech, cybersecurity, and education—rather than being confined to a single segment.

    Regulatory backdrop: buyback routes and taxation remain stable

    On the policy side, Entrackr says there have been no major new rules specifically for ESOP buybacks, though “some regulatory changes could influence how companies structure such programmes.” It points to the Securities and Exchange Board of India (SEBI), which phased out the open-market route for share buybacks from 2025 for listed firms, while also proposing reintroduction under a revised framework. Entrackr suggests that this shift could affect liquidity options as startups move closer to public listings, because the mechanics of share repurchase can determine how and when liquidity becomes available.

    Entrackr also states that provisions under the Companies Act, 2013 remain unchanged, and that buybacks continue to be a key mechanism for employee liquidity. It further reports that the Union Budget 2026 did not introduce changes to ESOP taxation, keeping the existing framework intact. For technology companies operating at the intersection of compensation systems and compliance, stable taxation reduces the need for frequent plan redesigns—though changes in buyback execution routes could still require operational updates.

    Finally, Entrackr addresses a common critique: that buybacks may serve a limited purpose because there are “far too few” to make an impact, with “headline grabbers” distorting perception. It also argues for a use case: buybacks can support employee loyalty via “delayed gratification,” and because buybacks have “virtually been counted as part of CTC” in some contexts, founders may prioritize them. While Entrackr frames these points as an argument, the underlying operational takeaway is that ESOP buybacks can be embedded into compensation accounting and retention strategy, not just treated as ad hoc liquidity.

    Looking ahead, Entrackr suggests that buybacks could become more prominent “going ahead” in a way similar to expectations around IPOs, while noting “uncertain market conditions.” This could mean teams may continue to plan for internal liquidity events even when external exits are delayed—an approach that, if sustained, may shape how equity compensation is managed across Indian startups.

    Why this matters for tech teams and equity compensation

    For technology-focused startups, ESOP buybacks sit at a practical intersection: engineering and product hiring depend on compensation packages, while employee retention depends on how equity translates into cash when markets shift. Entrackr’s data indicates that liquidity events are not uniform year to year, but Q1 2026’s rebound—nearly $220 million in buybacks—suggests that some companies are again prioritizing structured ways for employees and early investors to sell shares.

    Even with the source’s emphasis on aggregated totals and selected company examples, the broad pattern is clear: the ecosystem’s liquidity tooling is responding to both market conditions and regulatory execution pathways. Observers may watch whether the Q1 2026 acceleration persists beyond the first quarter and how SEBI’s evolving buyback framework for listed firms could influence planning as companies approach public-market timelines.

    Source: Entrackr : Latest Posts

  • India Launches Fund of Funds 2.0 with Rs 10,000 Crore to Support Deeptech and Micro VCs

    This article was generated by AI and cites original sources.

    The News

    The Indian government has launched Fund of Funds 2.0, a new investment scheme with a Rs 10,000 crore corpus. According to Tech-Economic Times, the program is designed to boost startup investment by supporting SEBI-registered alternative investment funds (AIFs), with a focus on deeptech, micro VCs, manufacturing, and sector-agnostic funds. Implementation will be led by SIDBI, and deployment is planned across upcoming Finance Commission cycles. (Source: Tech-Economic Times)

    How Fund of Funds 2.0 Works

    Fund of Funds 2.0 uses a “fund-of-funds” structure: rather than investing directly in startups, the scheme channels government capital into SEBI-registered alternative investment funds. This approach relies on regulated intermediaries to direct capital toward startups.

    The scheme identifies four focus areas: deeptech, micro VCs, manufacturing, and sector-agnostic funds. Deeptech typically involves longer development timelines from research to commercialization compared to software-only models. Manufacturing focus indicates an interest in capital-intensive ventures with supply-chain complexity. Micro VCs can support early-stage technical teams that larger funds may not target due to ticket size constraints. Sector-agnostic funds allow AIF managers to pursue opportunities across multiple industries while aligning with program priorities.

    SIDBI is named as the lead implementer. Fund-of-funds programs depend on how intermediaries are selected, monitored, and held to reporting standards. The assignment of implementation responsibility to SIDBI indicates it will be central to converting the Rs 10,000 crore corpus into investable commitments.

    The initiative will deploy capital over upcoming Finance Commission cycles, indicating a multi-year deployment strategy rather than a single-year allocation. This pacing can affect how quickly startups access funding and how AIFs structure their fundraising and investment timelines.

    What This Means for the Startup Ecosystem

    The announced design raises several questions for tech founders, investors, and ecosystem participants:

    Deeptech and manufacturing focus: The explicit emphasis on these areas could direct capital toward technology development with longer timelines and higher technical risk. The extent of this effect will depend on how AIF managers align their strategies with the scheme’s priorities.

    Micro VC support: If micro VCs receive backing through the fund-of-funds mechanism, the startup pipeline could include a wider range of early-stage technical experiments and founder profiles. The actual impact will depend on program eligibility rules and how “micro” is defined.

    Sector flexibility with targeted priorities: The combination of sector-agnostic funds with deeptech and manufacturing emphasis could result in portfolios that are both flexible and aligned with government priorities. How these elements interact will become clearer through program guidelines and AIF disclosures.

    Gradual deployment: Multi-cycle deployment could allow AIFs to structure longer-term commitments and may reduce short-term investment volatility. The actual timeline will depend on SIDBI’s implementation schedule and capital deployment milestones.

    Looking Ahead

    Fund of Funds 2.0 is notable for how it attempts to shape the investment infrastructure around startups by funding regulated AIFs and naming technical and industrial priorities. The next phase will be whether SIDBI’s implementation and the criteria applied to SEBI-registered AIFs translate the stated focus areas into investable strategies and measurable startup outcomes.

    Source: Tech-Economic Times

  • YMTC Plans New Factories Amid US Export Controls on Chipmaking Tools

    This article was generated by AI and cites original sources.

    Chinese memory-chip maker YMTC is reportedly planning new manufacturing facilities as US–Sino trade tensions intensify and the United States seeks to limit China’s semiconductor progress. The move, described in a Tech-Economic Times report, arrives alongside renewed US political efforts to restrict shipments of chipmaking tools to China—an area that directly affects how advanced semiconductors can be produced.

    Policy focus on manufacturing equipment

    China has sought to reduce reliance on foreign technologies in critical sectors such as semiconductors, while the US has pursued restrictions on Chinese semiconductor advancement. The key policy lever targets not only finished chips, but the tools used to manufacture them.

    From a technology standpoint, chipmaking equipment determines yields, process precision, and the feasibility of scaling to newer generations of memory and logic. According to the source, this month a cross-party group of US politicians proposed imposing additional restrictions on exports of chipmaking tools to China.

    Supply chain implications

    Semiconductor manufacturing depends on the combination of design capability, process know-how, and manufacturing tooling. Tool-export controls could affect multiple aspects of production:

    • Manufacturing scalability: New capacity requires equipment procurement and integration, which are constrained if tool exports are limited.

    • Process development: Advanced manufacturing typically relies on specialized equipment, so reduced access can slow iterative improvements.

    • Production continuity: If restrictions expand, companies may need to re-plan maintenance and upgrades for existing production lines.

    Because the policy target is explicitly the tools used for chipmaking, the strategic contest is being waged at a technical bottleneck rather than only at the market level.

    YMTC’s expansion plans

    YMTC plans new factories amid the heightened trade environment. The timing of these factory plans alongside proposed tool-export restrictions creates a scenario where capacity expansion and equipment access become linked. For industry observers, this signals that semiconductor manufacturing capacity decisions increasingly need to account for export controls affecting the equipment supply chain.

    Source: Tech-Economic Times

  • Practo Appoints Srijesh Kumar as Global CPTO Ahead of Pre-IPO Funding Round

    This article was generated by AI and cites original sources.

    Practo, the Bengaluru-based healthtech platform preparing for a public listing, has appointed Srijesh Kumar as its global chief product and technology officer (CPTO), according to Inc42 Media. The move comes as the company targets a $100 million to $125 million pre-IPO funding round—described as a mix of equity and debt—and accelerates its integration of AI across its platform to improve care delivery, patient experience, and provider tools.

    The appointment is part of Practo’s operational preparations for a public listing. The company is now expected to go public in 2027, after earlier targeting a listing this year. The CPTO hire signals how Practo plans to connect product execution with technology delivery as it scales globally and expands AI-driven workflows.

    CPTO Role and Kumar’s Background

    Kumar will lead the product and technology teams, with a focus on helping the platform grow as it scales globally. According to the announcement, his role involves bringing product and technology together, improving the platform, and using healthcare data to build “better and more consistent solutions.”

    Kumar brings over two decades of experience building enterprise-scale platforms. He served as a vice president at Salesforce, where he led product engineering for industries cloud. Prior roles include leadership positions at Expedia Group and Punchh, with earlier experience at Adobe and Microsoft.

    The CPTO structure aligns roadmap decisions (what features to build) with system decisions (how to build and scale them). This coordination is particularly relevant as Practo expands its use of healthcare data and AI across its platform.

    AI Integration and Data Scale

    Practo is accelerating integration of AI to improve care delivery, patient experience, and provider tools. Last month, the company appointed Cijo George as vice president of AI to lead its AI strategy across the platform.

    Practo’s platform operates at significant scale. The company audited over 1,000 healthcare establishments and processed more than 4 crore clinical data points in FY25. The platform connects users with over 7 lakh doctors and healthcare providers and expanded its network to over 1.5 lakh hospitals.

    The platform includes multiple patient and provider touchpoints: users can find doctors, book consultations, order medicines, and schedule diagnostic tests. On the provider side, Practo offers SaaS tools for clinics and hospitals, including appointment management and digital health records. The company also operates Insta, its hospital management system, used by more than 500 clients across 1,200+ facilities globally.

    Pre-IPO Funding and Financial Performance

    Practo seeks to raise $100 million to $125 million in a pre-IPO funding round expected to be a mix of equity and debt. The round is likely to be led by a global private equity firm, with participation from existing investors, at a reported valuation of around $700 million.

    The company plans to use the fresh capital to fund expansion and acquisitions. According to sources cited by Inc42, Practo’s revenue grew approximately 35% year-over-year in FY26, translating to about ₹315 crore. The company reported operating EBITDA of ₹15 crore on revenue of ₹234 crore in FY25, compared to an EBITDA loss of ₹17 crore in FY24.

    Global Expansion and Company Background

    Practo continues to scale in India while expanding into international markets including the US and the UAE. Global expansion introduces differences in workflows, systems integration, and data handling needs—areas where product and technology leadership influence standardization efforts.

    Founded in 2008 by Shashank ND and Abhinav Lal, Practo has raised nearly $230 million to date from investors including Tencent, Peak XV Partners, Z47 Partners, and Sofina.

    Why it matters: The CPTO appointment, the stated acceleration of AI integration, and the pre-IPO funding plan indicate Practo is coordinating product delivery and technology execution as it increases both global scale and AI-driven functionality. As Practo moves toward a 2027 public listing, the company’s ability to connect AI strategy to platform components that handle patient and provider workflows, and whether its data scale translates into measurable product improvements, will be areas to monitor.

    Source: Inc42 Media

  • India Launches Rs 10,000 Crore Startup India Fund of Funds 2.0 for Deep-Tech and Manufacturing

    This article was generated by AI and cites original sources.

    The Announcement

    India’s government has notified the Rs 10,000 crore Startup India Fund of Funds (FoF 2.0) to mobilise venture and growth capital for deep-tech startups, early growth-stage companies, and tech-driven manufacturing ventures. The fund will invest via SEBI-registered Alternative Investment Funds (AIFs) in the equity and equity-linked instruments of government-recognised startups.

    How FoF 2.0 Works

    FoF 2.0 operates as an “investor for investors” model, channelling capital through SEBI-registered AIFs rather than investing directly in startups. This structure allows the government to aggregate and allocate capital across the existing investment infrastructure, enabling AIF managers to select, monitor, and invest in startup portfolios.

    Building on FoF 1.0

    FoF 2.0 follows the earlier Fund of Funds for Startups (FFS 1.0), launched in 2016 under the Startup India programme. FoF 1.0 is managed by the Small Industries Development Bank of India under the Department for Promotion of Industry and Internal Trade and has supported over 1,370 startups. FoF 2.0 applies the same “investor for investors” approach while explicitly targeting deep-tech, early growth-stage, and tech-driven manufacturing segments.

    Four Segments of Capital Allocation

    FoF 2.0 is structured into four segments:

    • Deep-tech
    • Micro VCs backing early growth-stage startups
    • Tech-driven manufacturing
    • Sector-agnostic funds

    These segments separate different risk profiles and business models. Deep-tech and tech-driven manufacturing are explicitly targeted, while micro VCs receive dedicated support for early growth-stage companies. The sector-agnostic segment allows for opportunities that do not fit into the other defined categories.

    Operational Guidelines and Governance

    The Department for Promotion of Industry and Internal Trade will issue detailed operational guidelines covering eligibility criteria, fund selection, monitoring, disbursal mechanisms, reporting requirements, and investment committee structure. A committee chaired by the Secretary of the department will oversee implementation and performance. The practical effect on startup financing will depend on how these guidelines define and interpret categories like “deep-tech” and “tech-driven manufacturing.”

    Implications for India’s Startup Ecosystem

    FoF 2.0’s Rs 10,000 crore allocation is designed to influence capital flows into deep-tech startups, early growth-stage companies via micro VCs, and tech-driven manufacturing ventures. The fund’s structure signals a policy focus on technology-intensive development pathways. The actual impact will depend on how the guidelines define eligibility criteria and how SEBI-registered AIFs interpret these segments when selecting startups for investment.

    Source: Entrackr : Latest Posts

  • OpenAI acquires Hiro, expanding into AI-driven personal finance planning

    This article was generated by AI and cites original sources.

    OpenAI has acquired AI personal finance startup Hiro Finance, according to Tech-Economic Times. The deal brings OpenAI into a product category with defined workflows—users provide income and obligations, and the software generates scenario-based guidance—while highlighting how quickly AI startups are being absorbed into larger platforms.

    What Hiro built: scenario modeling for personal finance

    Hiro Finance was founded in 2023 and received backing from VC firms Ribbit, General Catalyst, and Restive. The startup launched an AI-based financial planning tool approximately five months before the acquisition.

    According to the source, the product works as follows: users input their salary, debt, and monthly expenses. The app then models various scenarios designed to guide financial decisions. Hiro’s core functionality pairs an AI-enabled planning interface with scenario generation—transforming structured personal financial data into alternative outcomes that users can compare.

    Why this acquisition matters for AI product strategy

    From a product perspective, acquisitions like this signal where capabilities are being consolidated. The source describes Hiro as an AI-driven personal finance planning app, and the acquisition by OpenAI indicates interest in bringing consumer-facing financial planning workflows into a major AI developer’s platform.

    Based on the stated product description, this could suggest OpenAI is looking to integrate or adapt scenario-based planning for personal finance use cases. The source does not specify whether Hiro’s tool will remain standalone, be integrated into another product, or be rebuilt on OpenAI technology. However, the structured input-to-scenario pipeline is the type of interaction pattern that can be paired with AI systems to produce user-specific guidance.

    Implications for AI finance: scenario-based decision support

    The source emphasizes that Hiro’s tool “enabled AI-powered financial planning for consumers” by allowing users to enter financial details and “model various scenarios to guide financial decisions.” This points to a specific technical focus: the system produces scenario comparisons based on user data rather than only generating text or answering questions.

    Scenario-based planning typically requires:

    • Structured inputs (salary, debt, monthly expenses)
    • Computation or rule-based logic to create alternative outcomes
    • Consistency across scenarios for meaningful comparisons
    • Result presentation that supports consumer decision-making

    This acquisition could reflect a broader trend toward AI systems that support decision workflows—where user inputs are transformed into modeled alternatives. This approach can be more directly measurable than open-ended assistance, as outputs can be framed as scenario outcomes derived from known inputs.

    Startup timeline and consolidation context

    Hiro’s timeline is notable: founded in 2023 and launching its AI planning tool approximately five months before acquisition. The relatively short window between product launch and acquisition by a major AI player suggests that working product experiences and clear use cases may be attractive to larger firms seeking to expand their capabilities.

    The combination of a recent product launch and acquisition by a major platform player is consistent with a market where distribution and integration can be significant factors in acquisition decisions.

    Source: Tech-Economic Times

  • Amazon reportedly nears acquisition of Globalstar—what the potential satellite move could mean for connectivity

    This article was generated by AI and cites original sources.

    Amazon is reportedly nearing a deal to buy Globalstar, with a transaction that “could be announced as soon as Tuesday,” according to a report cited by Tech-Economic Times and attributed to Bloomberg. The report, dated 2026-04-14, focuses on deal timing rather than technical specifics—an important limitation for readers trying to understand what would change technically if the acquisition proceeds.

    What’s being reported

    The source states that an acquisition transaction “could be announced as soon as Tuesday,” citing “people familiar with the matter.” The excerpt does not provide deal terms, valuation, regulatory status, or the scope of what Amazon would acquire.

    Because the source is limited to deal timing, the technology-focused question becomes less “what new feature is launching?” and more “what capabilities could Amazon be positioned to control or integrate?” In other words, the technical implications depend on what Globalstar’s assets and services are, but the provided text does not describe them.

    Why a satellite-communications acquisition matters technically

    Satellite communications are typically central to how providers build coverage for remote areas, mobility, and specialized connectivity use cases. In industry terms, acquiring a satellite operator can shift the balance between relying on third-party capacity and owning or operating core infrastructure. That distinction matters for engineering teams because it can affect latency characteristics, provisioning timelines, and the ability to coordinate network changes with application requirements.

    However, the source does not state what Globalstar’s network capabilities would be used for in Amazon’s plans. Any discussion of use cases—such as whether the technology would support consumer devices, enterprise connectivity, or back-end services—would be speculative and should be treated as an open question rather than a reported fact.

    How integration could play out

    From a technology operations standpoint, a satellite communications acquisition typically raises integration questions that are concrete even when deal terms are unknown. For example, teams typically need to align:

    Network operations and control: how satellite capacity is managed, how scheduling and routing decisions are made, and how service-level targets are tracked.

    Ground segment interfaces: how user data and signaling connect to terrestrial systems, including APIs and operational workflows.

    Service provisioning pipelines: how customers are onboarded, how authentication and authorization are handled, and how changes propagate from infrastructure to software.

    Security and compliance: how operational telemetry, monitoring, and incident response are integrated into the acquirer’s processes.

    The provided excerpt does not mention any of these items directly. If the acquisition is announced as soon as Tuesday, observers may watch for follow-on reporting that addresses integration scope, continuity of service, and technical roadmaps. The source’s lack of specifics means the immediate news value is about momentum toward a transaction, not about the engineering plan.

    Implications for the connectivity ecosystem

    If Amazon’s reported approach to acquiring Globalstar proceeds, it could suggest a shift in how connectivity capacity is sourced and packaged. In many markets, satellite capacity can be a differentiator for coverage and resilience; ownership or deeper control could affect pricing models, bundling strategies, and the cadence of network upgrades. However, none of these points are stated in the excerpt, so they should be framed as plausible industry dynamics rather than reported outcomes.

    For technologists and enterprise buyers, what matters is whether such a move reduces friction between infrastructure providers and application developers. Satellite connectivity intersects with software-defined networking, device and modem support, and application-layer expectations. A tighter integration between satellite operations and software services could improve the ability to iterate—though the source does not confirm any product changes or timelines.

    The reporting’s emphasis on timing—”could be announced as soon as Tuesday”—highlights how deal announcements can arrive quickly once internal processes are complete. For the industry, that means technical communities may need to prepare for rapid shifts in partner relationships, procurement assumptions, and integration planning once more detailed information becomes available.

    Bottom line

    Based on the Tech-Economic Times report citing Bloomberg, Amazon is nearing a deal to buy Globalstar, with an announcement potentially as soon as Tuesday. The excerpt provides limited technical detail, so the immediate story is about deal progress rather than engineering specifics. Any acquisition of satellite communications infrastructure would be a meaningful variable for the connectivity stack, and readers may look to subsequent coverage for details on what capabilities would be integrated and how service and network operations would evolve.

    Source: Tech-Economic Times