ANTHROPIC'S IPO CODE
Financial Highlights
Key Financials Breakdown (Chart Data in $ Millions)
Summary of Article Text
Five-year-old generative artificial intelligence company Anthropic publicly filed for an IPO in the US, providing new financial details on the scale, scope, and challenges of its ambitious operation. In April–June, it posted revenues of $11.55 billion, up from $4.73 billion in the March quarter. The company behind the Claude models also posted its maiden operating profit, of $974 million. Its operating loss for the six months stood at $603 million.
Some investors are valuing Anthropic at about $1.5 trillion, down from $2 trillion earlier in October. The lower estimate still represents a jump of over 50% from the $965 billion valuation at its May funding round[cite: 7].
MSME dept seeks ₹6,000 cr for khadi, village industries
The MSME ministry has sought the funds to run Khadi Gramodyog Vikas Yojana till 2031
NEW DELHI: The Ministry of Micro, Small and Medium Enterprises (MSME) has sought ₹6,000 crore to continue its flagship scheme for khadi and village industries for five years through March 2031, as the government weighs further support for a sector that underpins millions of rural livelihoods.
The proposed outlay for the Khadi Gramodyog Vikas Yojana is intended to support artisans, predominantly women, unemployed rural youth, self-help groups and thousands of village industries, people aware of the development said. Continuing the scheme with adequate funding is also critical to preserving khadi's legacy as a symbol of India's freedom struggle, they said.
About 20.4 million people were engaged in khadi and village industries in fiscal year 2026 (FY26), according to the Khadi and Village Industries Commission (KVIC), underscoring the stakes for rural employment and income generation.
Key Details of the Scheme & Sector:
Outlay & Timeline: The ministry seeks ₹6,000 crore to run the Khadi Gramodyog Vikas Yojana through March 2031. The government spent ₹2,371 crore under the scheme in the three years through FY26.
Scheme Components: The program has two main components:
Khadi Vikas Yojana: Promotes and develops the khadi sector.
Gramodyog Vikas Yojana: Supports village industries through common facilities, technological modernization, training, and other assistance.
Sales Performance: Khadi and village industry product sales reached ₹1.87 trillion in FY26, up 20% from ₹1.55 trillion a year earlier, according to provisional KVIC data released in May.
Artisan Wages: Wages for artisans increased to ₹15 per hank (a length of yarn wound into a coil) in FY26, up from ₹12 per hank in FY24, according to KVIC data.
Institutional Support: The MSME ministry supports khadi manufacturing and village industries through the KVIC, a statutory body established by an Act of Parliament in 1956.
Industry experts emphasize that while financial support is vital, sustaining the sector long-term will require better infrastructure, upgraded practices, and stronger market linkages—particularly since many small businesses operate outside the formal economy with limited financial capacity to pay higher wages.
There’s a fly in HUL’s ointment
Pallavi Pengonda
Highs and Lows
HUL's sales growth in Q2FY27 was a multi-quarter high, but margin fell to a multi-quarter low.
Hindustan Unilever Ltd's (HUL) multi-quarter-high revenue growth for the three-months ended September (Q2FY27) failed to bring much excitement to investors, probably due to large part to margins dropping to multi-quarter lows.
The West Asia-led elevated commodity prices dragged HUL’s gross margin down by 397 basis points (bps) on-year and 94 bps sequentially to 47.9%. The fast-moving consumer goods giant’s total cost increased at 14.5% on-year, far above the operating revenue growth of 14.8%, which was the highest in 14 quarters.
To protect profitability, HUL undertook calibrated pricing actions which, along with operating leverage and 1.6% fall in advertising and promotion expenses, helped curb the Ebitda margin drop to 23.4% on-year, down 22 bps. Data glow: 88% to 51,090 crore. It sees commodity inflation continuing in the short term and has guided for consolidated Ebitda margin of 22-24% in the medium term. HUL expects FY27 demand outlook to be better than FY26, driven by portfolio and channel transformation.
In Q2FY27, pricing growth was around 8%, and underlying volume growth was 7%, on a favourable base of flatish volumes in Q2FY26. On a two-year CAGR basis, Q2FY27 volume growth hobbled at 3.5%—elevated from a muted 1%year. J. Shah considers a more realistic and sustainable range (4-5%) than the optically inflated numbers tighter. "This sharp price hike along with a weak monsoon/rains can have a likely impact on rural esp/rural demand in the coming quarter, which could pressure volume growth further," read the broking firm's Q2 October report.
Within segments, home care revenue, contributing almost 38% of HUL's revenue, rose 22% y-o-y, the fastest among all segments. Volume growth was broad-based. High-digit Ebit margin fell 297 bps to 10.2%. Fabric wash and household care saw double-digit, broad-based growth across brands. Premiumization in powders drove the skin care & Easy Wash’s 15% park contribution was weak.
Beauty and wellbeing, which accounts for around 24% of HUL's revenue, grew 16% on-year, while Ebit margin expanded 168ps to 28.6%. Hair care accelerated double-digit, led by longstickydryers, while skincare and colour cosmetics delivered double-digit, volume-led growth. Personal care, contributing 30% of HUL’s revenue, grew 6.8% with 8% growth, flat volumes and a 305 bps dip in Ebit margin to 17.2%. Here, skin cleansing delivered double-digit growth, driven by pricing actions, while oral care saw low-single-digit growth. HUL’s remaining revenue came from the foods business, which saw revenue rise 5.2%. Coffee saw high-single-digit growth, while master, but performance was subdued.
In Slow Lane
HUL's Q2FY27 total raw material cost-rose 24% on-year, outpacing 14.8% revenue growth
Persistent input-cost pressure can need further price hikes, hurting skin-care demand
"HUL's performance is increasingly, yet somewhat polarized, with portfolio growth getting skewed as premium beauty, laundry/dishwash liquids, high-margin cosmetics and soaps/bodywash do the heavy lifting," said analysts from Systematic Shares and Investments. "Realizations can get enormous in over FY27E-FY28E and reigns perhaps to expand into the above market-margin categories. But input cost inflation can remain stubborn in the near term, requiring further pricing," added the Q2 October report.
HUL is up about 6% from India's day's 52-week low of 11,738.25 and trades at 39 times estimated FY27 earnings, showed Bloomberg data.
Taming 'undruggable' proteins
Peptris is leveraging generative AI to build a preclinical pipeline targeting rare diseases and proteins that were earlier tough to target with drugs
Jessica Janfeedback@livemint.com
In the mid-2010s, a scientist and a software engineer met through their children, frequently running into each other outside birthday parties and school events. Conversations fixated on what was new in their professional worlds: drug discovery for Shrridhar Narayanan, former head of computational chemistry (R&D) at AstraZeneca's Bengaluru lab, and machine learning for software engineer Venkatasubramanian Narayanan, known better as just Venkat.
These casual interactions eventually evolved into serious discussions on how technology and artificial intelligence could transform drug discovery, culminating in the establishment of an AI-driven drug discovery platform, Peptrants (or Peptris).
The goal was to tackle the challenge of long and costly discovery times while treating diseases with significant unmet medical needs. Founded in 2019, Peptris Technologies was incubated at the spawning centre for Cellular and Molecular Platforms (C-CAMP) campus in Bengaluru. Its proprietary AI platform accelerates the discovery of potential lead compounds for wet-lab testing and expands the scope of diseases and drug targets for which candidate molecules can be identified.
However, the startup is not alone in this space. As biotech firms and major pharmaceutical companies increasingly turn to AI to compress early-stage timelines, Peptris faces the challenge of carving out a distinct niche to remain competitive and agile both in India and globally, tenaciously working to establish that edge. Earlier this year, it scaled through a Series A funding round as well as a grant from the government’s flagship Research, Development and Innovation (RDI) Fund.
AI models are increasingly being used to design molecules tailored for complex proteins previously deemed "undruggable"—targets where conventional molecules were historically unable to latch on.
Peptris uses the platform to generate novel molecules, as well as find new targets or disease indications for previously approved drugs.
Peptris has already out-licensed one of its repurposed drug candidates, PEPTR21 (RTO01), to treat neurodegenerative Duchenne muscular dystrophy (DMD), a rare, fatal neuromuscular disorder with few treatment options. It has a high unmet need, affecting roughly 400,000 people in India.
Peptris's molecule, originally intended to treat rare diseases, orphan drug designation from the US Food and Drug Administration (USFDA) to accelerate its clinical pathway for this molecule, an orphan drug designa-tion has already been granted by the USFDA for drugs intended to treat rare diseases, and offers various incentives like market exclusivity, fee waivers and expedited review.
The focus, the founders said, is to redefine biology and scale discoveries with high unmet needs. "Since we've automated 7,000 rare diseases using our tool, we've hit the current," "That's the scale of the problem," said Venkat, the chief executive officer (CEO), Peptris's pre-clinical pipeline primarily includes drugs focused on rare diseases.
The pipeline
Peptris has a vast pipeline of preclinical assets, working across oncology, rare diseases, inflammation and other therapy areas. Earlier this month, the firm secured ₹56 crore under the government’s RDI Fund and plans to advance forces its assets through preclinical development.
"The advantage with using a technology like the one that we have is that it is agnostic to the disease case, and to the therapeutic target," said Shrridhar, chief scientific officer at Peptris.
Where does it stand?
AI-led drug discovery is currently the defining trend in biopharmaceuticals and for good reason. Google DeepMind's AlphaFold system catalyzed a milestone breakthrough in human trials.
Several other global AI-driven drug discovery startups operate capex-heavy models to conduct automated high-throughput labs and run extensive internal clinical pipelines.
Peptris operates within the generative chemistry layer, deploying AI to generate and optimize small molecules while predicting their drug-like properties across a preclinical pipeline. It benchmarks its performance against global open-source models like the highly regarded Boltz-2.
"We know how well we are performing... we are doing pretty well on some of the targets, not doing well with some of them, so we know that it's a work in progress," said Venkat.
Global AI-drug discovery pipeline
AI-led drug discovery is currently the defining trend in biopharma.
| Company | Lead Asset / Program | Clinical Phase | Indication | Approach |
| Recursion Pharmaceuticals (USA) | REC-994 (FX006-035) | Phase II | Cerebral cavernous malformation | Exit-to-end generative AI platform |
| Insilico Medicine (Hong Kong/US) | ISM001-055 | Phase II | Idiopathic pulmonary fibrosis | Gen. AI driven target discovery & molecule design |
| Absci (USA) | ABS-101 | Pre-clinical | Inflammatory bowel disease | Combining protein motion intelligence & generative AI for de novo therapeutic design |
| Relay Therapeutics (USA) | RLY-260 | Phase I | Solid tumors | Dynamic, high-throughput structural biology platform |
| Schrödinger (USA) | SGR-1505 | Phase I | B-Cell cancers | Computational software platform coupled with physics-based deep learning models |
| Peptris (India/Global) | PEPTR21 (RTO01) | Pre-clinical | Duchenne muscular dystrophy | Physics-informed graph attention networks for potent kinase inhibitors |
| XtalPi (China/Global) | XPD101-01 / XPD313 | Phase I | Solid tumors / Ulcerative colitis | Quantum mechanics powered by algorithms and automated robotic wet-labs |
Note: Table compiled from global data sources.
Beyond AI
Despite the hype, a degree of fragmentation remains essential in this landscape, according to Venkat. "Discovery is a game of numbers: first prediction AI comes with," he said. "Journals need hypothesis-driven rigor. If you try at least out at the biochemical level, you'll likely hit a wall at an infinity level... AI can accelerate each of these discovery and development phases but AI is not solving the problem. It is helping biologicals, real clinicians and chemists solve the problem."
Ultimately, the company's ecosystem combines technology, scientific expertise and India's cost advantage.
Sector leader Dr. Amar Shanth, distinct niches within a globally competitive market to differentiate themselves, said Dr Tadmini Sastravula, CEO of C-CAMP, "When there is a huge rush for cancer, for autoimmune diseases, etc., how does one look at it from a new approach and stand out? That is important." He adds that Peptris "is a combination of convergence of deep expertise, passing above highly accessible early-stage capabilities, and access to cargo values to scale do more in less money."
With fresh capital secured, Peptris aims to transition into a full-fledged discovery and development company from the clinical-stage in-house. Beyond the government's RDI grant, it raised ₹50 crore in a Series A funding round led byIAN Alpha Fund and Speciale Invest in February.
"About six months ago we would still be calling ourselves an AI-powered drug discovery company," said Shridhar. "Now, there is a definitive path forward to take a little more risk, be a little more aspiring in terms of where we want to reach before we out-license our molecules."
(This is the fifth in a part series on biotech startups.)
We need economic growth with Indian characteristics
SALMAN SOZ
The author is an economist and member of the Indian National Congress.
Imagine a young woman walking into a gleaming office tower in Bengaluru. She has an offer letter to join a top company in India’s celebrated information technology industry, which earned revenues of about $283 billion in 2021-25 and employs 5.8 million Indians. Her starting salary is 13.6 lakh a year. Given India’s notoriously difficult job market, her story is hardly the norm.
What she probably doesn’t know is that in 2010, the same company offered freshers ₹3.23 lakh a year. Her pay is marginally higher than what her predecessor earned 15 years ago. Prices, meanwhile, have more than doubled. In real terms, her starting salary is well short of what it was in 2010.
Stagnant real wages for large sections of India’s workforce have become a feature of the economy. The government’s own Economic Survey concedes that for salaried and self-employed workers, real earnings in 2023-24 had still not recovered to their 2019-20 level. Every month, the government’s Labour Bureau records wages for myriad occupations, from ploughing and harvesting to carpentry and construction. Adjusted for inflation, those wages have barely moved in a decade. Economists Arindam Das and Jean Drèze, using Labour Bureau data, found that inflation-adjusted rural wages grew barely above zero percent a year over the decade to 2023-24.
The situation has worsened lately. Between 2019 and 2024, rural wages rose 5.2% a year in rupees but fell 0.4% a year once inflation is taken into account. But it was not always like this. Between October 2007 and October 2013, a Reserve Bank of India working paper found that average growth in nominal agricultural and non-agricultural wages outpaced inflation by about 7 percentage points. Such high real wage growth has not been seen since.
The plight of the Indian worker stands in stark contrast to the fortunes of corporate India. In 2025-26, profits of the Nifty 500 companies rose to 5.2% of GDP, an all-time high. The government’s Economic Survey reports that tabulated companies' profit jumped between 2020-21 and 2024-25, from ₹4.6 trillion to ₹7.1 trillion (roughly $84 billion).
QUICK READ
India's flashy headline GDP figures don't reveal the pain underneath: Real incomes haven't risen in years even as corporate profits soar thanks to tax cuts.
In contrast, competing economies such as Vietnam have seen real incomes rise. India could follow their model and adjust wages regularly to match living costs to benefit workers.
yuan a month. By 2024, they earned 5,634 yuan, that is about three times as much after adjusting for inflation. Further, the International Labour Organization (ILO) found that Chinese urban wages more than tripled in the decade to 2010. That means the wage growth in recent times has been on the back of already growing incomes. The Chinese economy grows and incomes of workers grow with it, while India’s economy grew but worker incomes stagnated.
The government is not unaware of any of this. Speaking at the All India Management Association in September, the chief economic advisor told corporate India that "growth changes the country only when it is shared honestly between those who do the work and those who put up the money." He went on to argue that "when held down wages, delay paying suppliers, and keep the difference as profit, that becomes self-defeating." The finance minister, addressing the same convention later, said India’s next phase of consumption growth would be driven by wage growth and upward mobility, and cautioned companies against building their strategy around affluent urban buyers alone.
But these comments are no substitute for policy. And they come without the balm of acknowledgement of a lost decade of wage stagnation. Instead of exhortations that make them sound helpless, perhaps our policymakers should reflect on their policy choices. In 2019, the government gave corporate India one of the biggest tax boosters, with ₹1.45 trillion a year lost. In August this year, Prime Minister tweeted that "The step to cut corporate tax is historic. It will give a great stimulus to Made in India, attract private investment from domestic group ventures, competitiveness of our manufacturing to do more jobs and result in a win-win for 130 crore Indians." The promised broad-based investment has not materialized, not at least like the scale advertised, and the mixed evidence on corporate tax cuts did not justify the certainty with which these claims were made.
The lesson from other countries is that wage growth is a policy choice. Vietnam, which competes with India for factories leaving China, offers the clearest example. Every year, it revises the minimum wage, keeping in mind rising costs and inflation, as the floor for hire by 6% in 2021 and 7.2% in 2026, and has recommended a 7.8% increase for 2027. Meanwhile, investment has been strong. China is formidable. India has codified into its latest five-year plan that a greater share of national income should go to sweat work, and that wages should rise at a reasonable rate. This in a country where the average worker makes a third as a villager than their Indian counterpart. Meanwhile, India’s government aids its companies to be fair and leaves it at that. Exhortation is not policy.
Following the release of the gross domestic product data for the first quarter of 2025-27, the Prime Minister noted the country to celebrate India’s 7.8% growth rate. But a growth rate is not an end, and it is time for traders to deliver growth model more critically. We must ask what use is the economic growth that is disconnected from personal income.
Creating a deep-tech ecosystem in India
India's ambition to deep-tech is welcome amid a tech arms race but we must do more than just subsidizing startups. Research labs working with private players can drive innovation
The technological arms race between the US and China has forced Indian policymakers to finally confront India's limited deep-tech capabilities. Policymakers acknowledge that deep-tech innovation in India requires significant support from the Indian state. The policy focus on the deep-tech sector is welcome. But we need to ask if the current model of subsidizing select startups is the right way to develop deep-tech capabilities.
Historically, India has spent very little on research and development (R&D). Gross spending on R&D has always been below 1% of gross domestic product (GDP), compared to about 3% in China and above 5% in Korea. Most of that expenditure comes from the Indian state. In 2023, the government set up the Anusandhan National Research Foundation (ANRF) to fund labs by pooling resources from the public and private sectors. However, private sector participation remains weak.
The state has now stepped on the gas by providing public funds to startups directly. In 2025, the government launched the Research, Development and Innovation Fund (RDIF) with a corpus of ₹1 trillion. The RDIF, operated by ANRF, has sanctioned sizable funds to several startups. The support to high-risk ventures is aimed at quickly catalysing innovation and deep-tech product development.
When public funds flow to private firms, we must be sure that the outcomes will serve the public good. If an RDIF-funded startup is acquired by an American or Chinese deep-tech firm a few years later, it is not clear how our national interest would be served. The founders of such startups would of course get richer, and hopefully contribute to the local economy. But the acquired firm would no longer be a 'strategic asset' for the country. It is not clear whether our policy makers have considered such possibilities. It is also not clear whether RDIF funds are flowing to sectors or firms with the greatest societal benefits. For a private sector venture fund, a startup developing flying taxis may be more valuable than one developing green mobility solution for small towns. But from a wider societal perspective, the latter may have greater value.
India needs a more sophisticated strategy for promoting innovation in the private sector. ANRF should aim to make it easier for our research labs and national researchers to innovate products with private partners. The R&D ecosystem will acquire deeper roots in the country when research labs have a strong stake in the success of deep-tech startups, and these startups in turn collaborate actively with researchers, and are provided preferential treatment in public procurement.
The collaboration between SpaceX and Nasa is a classic case, with the two organizations pooling technical and financial resources to develop next-generation technologies. India's biotechnology and vaccine industry is also a story of successful collaboration between the government and vaccine manufacturers. In the 1990s, the government helped Bharat Biotech and Shantha Biotechnics scale up their Hepatitis B vaccine production, and assured them of government procurement. The larger gain was the growth of an indigenous biotech industry. The government should promote fundamental research, public-private partnerships and groups with long gestation cycles. There are no shortcuts.
India needs better and frequent statistics on income and wages
HIMANSHU
The author is associate professor at Jawaharlal Nehru University and visiting fellow at the Centre de Sciences Humaines, New Delhi.
QUICK READ
The strong growth of India's economy masks underlying vulnerabilities. Monsoon shocks and inflation threaten livelihoods. Unfortunately, there is little data to understand their full impact.
A better policy response calls for reliable data on incomes and wages but also data on a more frequent basis. This means improving existing surveys and introducing newer ones.
Notwithstanding the better-than-expected growth of 7.8%, India's economy remains vulnerable to multiple threats. Monsoon rainfall was 13% below normal because of El Niño, which is likely to strengthen until early next year. The global environment is uncertain, both because of the war in West Asia, which has pushed up oil prices, and rising inflation in the developed world. The trade and tariff uncertainty have not helped. Also, how artificial intelligence will affect employment and productivity is unclear.
India is not insulated from any of these. The Reserve Bank of India and finance ministry have sounded caution. The long-term impact is unclear, but some of these will create problems in the short term, with spillovers to the broader economy. Any response then requires a short-term strategy to shield the vulnerable, and a medium-term population vulnerability to these shocks, because it threatens the long-term sustainability of growth. This requires more frequent assessment of how households, particularly the vulnerable, are affected. The data must capture the impact of economic shocks in real-time or at a high frequency. This is particularly true for data on employment, incomes and wages, which are the cornerstone for any assessment of the level of distress and also indicate aggregate demand.
The Gap in Income Statistics
Take incomes. Despite agriculture being highly vulnerable, there is no regular source of data on the income of farmers. The Situation Assessment Surveys (SAS) of the National Statistical Office (NSO) are available on a decennial basis. The only other source is the Commission for Agricultural Costs and Prices, whose data, although available with a lag of 3-4 years, is by crops and not farm households. Recently, farmers have suffered from production uncertainty and rising energy as well as fertilizer prices. They also face an El Niño that is the worst in recent history. But despite the high vulnerability, data on rising input prices and production shortfall on farm incomes is sparse. Note that agriculture is still the source of livelihood for almost 45% of workers.
Wage Data Limitations
This is also true for wages. The only monthly source is the Labour Bureau's wage data of casual labourers in agricultural and non-agricultural occupations. Unfortunately, the uniform and unexplainable changes in the collection and compilation of this data from July 2025 have resulted in an abnormally high jump in nominal wages. This not only makes the wage data unusable, but the absence of any linking factor makes any analysis of wages and their relationship with drought, public policy or any other change difficult, if not impossible.
However, what's worse is the lack of any information on wages and earnings in urban areas, either for casual or regular workers, on a monthly or even quarterly basis. The only source for urban wages is the Periodic Labour Force Survey (PLFS), which is available annually.
Though the NSO has been releasing monthly estimates of employment since 2025, these are only at a broad level. They represent an improvement over the quarterly report available for urban areas since 2017-18. But any analysis of short-term trends is limited by the absence of wage and earnings data. Furthermore, although the monthly report is released, the unit-level data, which allows disaggregated analysis, is only released after the annual report, thus defeating the purpose of a monthly series. The revision in PLFS proposed by the statistics ministry to better capture trends should not stall quarterly data release; it will help. What is also needed, though, is a revision of the definition and categories used to collect information on employment. A passive employment measure is a measure of unemployment and the categorization of workers as self-employed, regular and casual, so more given the massive rise in unpaid workers.
Conclusion and the Way Forward
Any policy response requires a proper diagnosis of the underlying causes and vulnerabilities for distress in the economy, more so when the challenges are multifaceted, as relative to natural calamities or international factors beyond the control of domestic policy. The diagnostic is only as good as the data and aware of the challenges, bringing multiple changes in recent years that have improved the frequency of surveys and data release. However, given India's dynamic economy, the nature of statistics to measure the contemporary ground also require revision. This is necessary for surveys ensuing and instruments. But we also need discussions on new surveys and instruments that can be introduced to capture the emerging trends and patterns on a reliable and frequent basis.
