Meta is reportedly weighing layoffs that could impact at least 20% of its workforce as the tech giant looks to offset rising artificial intelligence costs.

The cuts come as the technology company aims to offset the cost of artificial intelligence infrastructure and prepare for greater efficiency brought about by AI-assisted workers, three sources familiar with the matter told Reuters.

The outlet added that the timing and size of the potential layoffs have not been finalized.

When reached for comment, a Meta spokesperson told FOX Business, “This is a speculative report about theoretical approaches.”

META CUTS OVER 1,000 JOBS IN MAJOR METAVERSE RETREAT

According to Reuters, top Meta executives recently shared plans for the proposed layoffs with other senior leaders at the company.

If the company were to slash 20% of its employees, the layoffs would amount to Meta’s largest restructuring since 2022 and early 2023, the outlet said.

Meta laid off 11,000 workers in November 2022 — around 13% of its workforce at the time, Reuters reported.

The company cut another 10,000 jobs months later.

JUDGE BLOCKS META FROM INTRODUCING ‘EXAGGERATED’ CLAIMS IN SOCIAL MEDIA TRIAL

Meta employed nearly 79,000 people as of Dec. 31, according to its latest filing.

Other major companies, including Amazon, have recently announced large-scale layoffs tied to AI developments.

In January, Amazon cut around 16,000 jobs and signaled at the time that more reductions could follow.

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The company previously announced a first round of cuts totaling about 14,000 white-collar layoffs in October, bringing its corporate reductions to roughly 30,000 roles.

In making the cuts, which represented nearly 10% of its white-collar workforce, Amazon cited efficiency gains from artificial intelligence and broader cultural changes.

FOX Business’ Bradford Betz contributed to this report.

Sadanand Date assumed charge as Executive Director at Sebi on March 4 to head the investigations department, the markets regulator said on Friday.

Date is a 2007-batch IPS officer of the Uttarakhand cadre.

Prior to joining Sebi, he was on central deputation to the Central Bureau of Investigation (CBI), where he served in several key roles, including Superintendent of Police in the Anti-Corruption Branch (ACB) and Bank Securities and Fraud Cell (BSFC), the regulator said in a statement.

He also headed multiple branches in Mumbai, including the Economic Offences Branch, Special Crime Branch, Special Task Branch and Anti-Corruption Branch.

During his tenure with Uttarakhand Police, Date held several leadership positions and served as Superintendent of Police or Senior Superintendent of Police in various districts, such as Uttarkashi, Nainital, Haridwar, Udham Singh Nagar and Dehradun.

He also briefly served as Inspector General (Headquarters) and Director (Traffic) before moving to Sebi.
Date is a medical graduate and holds an MBBS degree from Grant Medical College & Sir JJ Group of Hospitals, Mumbai. He also holds a Master’s degree in Police Management from Osmania University, along with MA (Economics), LLB and LLM degrees from the University of Mumbai.

In addition, he is a Certified Fraud Examiner (CFE). He is also a recipient of the President’s Police Medal for Meritorious Service.

Stock exchanges BSE and NSE are conducting mock trading sessions for equity, commodity and currency derivatives on Saturday from the primary site (PR) and Disaster Recovery Site (DR). The mock trading is merely for the purpose of testing and familiarisation. The trades resulting from such mock trading will not attract any margin obligation or pay-in and pay-out obligation, and they will not create any rights and liabilities.

Trading members using third-party trading platforms can also use this opportunity to test their respective trading applications during the mock trading session for various functionalities (including exceptional market conditions), viz., various types of call auction sessions, risk-reduction mode, trading halt, block deals, etc.

Here’s the schedule of trading sessions:

– Log-in – 09:15 am to 09:45 am
– Morning Block Deal Window (PR): 09:45 am to 10:00 am
– Continuous Trading T+1 (PR): 10:15 am to 01:00 pm
– Continuous Trading T+0 (PR): 10:15 am to 12:30 pm

– Closing: 04:00 pm to 04:10 pm
– Post-closing: 04:10 pm to 04:20 pm
– Trade Modification T+1: 04:30 pm
– Trade Modification T+0: 03:45 pmThe exchanges have urged market participants to participate actively in the mock trading sessions.

Exchanges routinely conduct mock trading sessions to test their systems to be able to provide their members with a robust & efficient system for trading with better features.

They also seek feedback from all members. The members can give their feedback for the mock trading session to exchanges by 5:00 pm.

Indian benchmark indices fell sharply on Friday, recording their third successive decline as the Iran-Israel/US war continued to dent market sentiments. The biggest drags were metals, auto, and financial stocks. In a volatile session, the broader Nifty plunged 488.05 points, or 2.06%, to close at 23,151.10, while the 30-share Sensex declined 1470.50 points, or 1.93%, to settle at 74,563.92.

(Disclaimer: The recommendations, suggestions, views, and opinions given by the experts are their own. These do not represent the views of The Economic Times.)

Investing has traditionally been shaped by human judgment—port analysing companies, interpreting macroeconomic signals, and making decisions based on experience and intuition. While this approach has produced many successful strategies, it is inherently constrained by human bandwidth and individual bias.

Considering the above, research teams can track a limited number of companies, process a finite volume of information, and react within time-bound constraints.

Systematic investing represents a meaningful evolution in this framework. It combines human expertise with machine-driven analytical power to create a more structured and scalable investment process.

In essence, systematic investing brings together two complementary strengths:

• Human insight — experience, judgment, and economic understanding
• Machine intelligence — speed, scale, and analytical precision

This fusion allows the investment team to analyse vast datasets, evaluate market signals in real time, and apply consistent decision-making frameworks.

The result is an investment approach that is disciplined, repeatable, and resilient, which are qualities that are increasingly valuable in modern markets.

Why India Is an Ideal Market for Systematic Investing

India’s capital markets are undergoing a structural transformation. Over the past decade, the ecosystem has been shaped by several powerful trends, these include rapid growth in retail investor participation, Digitisation and faster dissemination of information, increasing market depth and sectoral diversity along with Greater liquidity and trading activityIn such an environment, the ability to process information quickly and identify signals efficiently can become a powerful competitive advantage.

This is where the Systematic Active Equity (SAE) strategies stand out.

SAE combines the alpha-seeking intent of active management with rules-based, data-driven execution frameworks that are cost-controlled and risk-managed. This allows investment strategies to identify opportunities more efficiently and implement them with discipline and precision at lower cost.

The Core Pillars of Systematic Active Equity

1. Data-Driven Decision Making at Scale

One of the defining characteristics of SAE strategies is their ability to process vast and diverse datasets. These include traditional financial metrics such as earnings, valuations, balance sheet indicators, Market-based signals like price momentum and liquidity trends. Furthermore, the strategies also include Alternative datasets such as News sentiment analysis, Social media signals, Satellite and geospatial data, amongst others.

The objective is to identify repeatable patterns and predictive signals that can inform investment decisions. Over time, models continuously learn from new information, refine their insights, and adapt to evolving market dynamics.

2. Dynamic and Adaptive Portfolio Construction

Unlike static portfolios or purely benchmark-hugging strategies, SAE portfolios are inherently dynamic. They continuously adjust based on:

• Signal strength
• Changing market conditions
• Factor performance cycles

This enables portfolios to rebalance efficiently and allocate capital where opportunities looks strong. In markets like India—where sector leadership and market themes can rotate rapidly—this adaptability becomes an important source of investment edge.

3. Integrated Risk Management

Risk management in systematic strategies like SAE is not a separate layer applied after portfolio construction. Instead, it is embedded within the investment framework itself.

This includes:

• Volatility targeting
• Position sizing/weighting frameworks
• Diversification across sectors and market caps
• Active Risk control mechanisms
• Analyzing factor exposures and tilting them based on strategy goals
• Focusing on risk-return metrics like IR (Information Ratio) Alpha consistency as a target
• Eliminating key-man risk

The goal is not only to generate returns but also to ensure consistency of outcomes across market cycles.

How Systematic Investing Reduces Behavioural Biases

Traditional discretionary investing, while driven by expertise, can sometimes be influenced by behavioural biases such as, Recency bias, Overconfidence etc

By reducing the influence of emotion and subjectivity, systematic strategies enable a more consistent and forward-looking investment process, thereby eliminating human biases by relying on , pre-defined investment rules, Data-backed signals and Objective decision frameworks

Ensuring Continuity Beyond Individuals

Another structural advantage of SAE lies in its process-driven nature. In traditional setups, fund performance can sometimes be closely associated with individual portfolio managers and hence lead to key man risk. Changes in personnel may lead to shifts in strategy or portfolio construction leading to very different risk and return orientations than originally anticipated. Systematic investing reduces this dependency. Despite changes in the investment team, the underlying models remain constant as data pipelines continue operating ensuring the overall investment philosophy remains undisturbed.

In many ways, it is like changing the driver while the navigation system guiding the journey remains the same.

Combining Human Expertise with Machine Precision

Despite common perception, systematic investing is not about replacing human decision-making. Instead, it is about augmenting human expertise with technology.

Humans play a critical role in

Designing robust and efficient investment frameworks is important to avoid GIGO (Garbage-In, Garbage-Out)

• Selecting relevant signals
• Interpreting macroeconomic context to decide on active risk levels
• Monitoring and refining models

Machines, in turn, excel at:

• Processing vast datasets
• Identifying patterns across markets
• Executing strategies with speed and consistency

Together, this partnership creates a powerful investment engine—where humans define the “what” and “why,” and machines optimise the “how” and “when.”

A New Paradigm for India’s Investors

As India’s markets become more complex, information-rich, and competitive, investors increasingly require strategies that can combine discipline, scalability, and adaptability.

Systematic Active Equity addresses this need by integrating:

• Data-driven intelligence
• Machine efficiency
• AI/ML techniques
• Human oversight and governance

The outcome is a robust and repeatable investment approach designed to navigate volatility, capture opportunities, and deliver alpha over time with controlled risk and reduced cost.

For Indian investors, this represents a shift towards a more institutional-grade investment framework incorporating global best practices.

(The author is CIO at JioBlackRock Asset Management)