OpenAI is preparing for a future where artificial intelligence systems could improve themselves without direct human involvement, according to recent reports. The company is now hiring safety researchers to study the risks linked to this development, known as recursive self-improvement, where AI systems train better versions of themselves. This shift comes as AI capabilities continue to grow rapidly, with companies exploring how far automation can go in research and software development.<h2>What is self-improving AI and why it matters</h2>Recursive self-improvement refers to AI systems that can upgrade their own performance without relying heavily on human input. In simple terms, an AI system could build a smarter version of itself, which could then repeat the process. This idea, once limited to theory, is now being actively explored as AI tools improve. Reports suggest that the amount of work AI systems can handle is increasing quickly, with some studies indicating that their capabilities are doubling roughly every seven months. If this trend continues, AI systems could eventually handle complex tasks that currently require human researchers and engineers.<h2>Why OpenAI is hiring safety researchers</h2>OpenAI’s hiring move shows that the company is not only building advanced systems but also preparing for the risks they may bring. The role focuses on studying problems that may arise in the future, even if they do not exist today.The researchers will work on areas such as: Preventing data poisoning attacks Understanding how AI systems make decisions Studying risks linked to highly autonomous systems Tracking how AI tools are replacing human technical work The company has described this work as urgent, with potential impact on both industry and society.<h2>AI automation and impact on jobs</h2>One of the key points in the job listing is the need to track the “automation of technical staff”. This includes measuring how AI tools, such as coding assistants, are replacing or supporting human engineers.Industry research suggests that AI could soon handle a large share of software tasks that currently take humans days or weeks to complete. This raises questions about the future of jobs in research, engineering and development. At the same time, companies continue to hire experts to manage and guide these systems, showing a shift in the nature of work rather than a complete replacement.<h2>OpenAI’s long-term AI vision</h2>OpenAI CEO Sam Altman has previously outlined the company’s goals, including building an “automated AI research intern” in the near term and a fully automated AI researcher by 2028. While he has acknowledged that these goals may not be achieved, the company continues to invest in this direction, reflecting strong belief in the long-term potential of AI.<h2>Global race in self-improving AI</h2>Other companies are also exploring similar ideas. Anthropic has published research on using AI systems to monitor stronger AI models, while experts estimate there is a 60 per cent chance that AI research could happen without direct human involvement by the end of this decade. This shows that the race towards advanced AI is not limited to one company but is a broader industry trend. The development highlights a key shift in artificial intelligence: moving from tools that assist humans to systems that may operate independently. While this could improve efficiency and innovation, it also raises concerns about control, safety and employment. For now, companies like OpenAI are trying to balance progress with preparation, ensuring that as AI becomes more capable, its risks are understood and managed.

OpenAI is preparing for a future where artificial intelligence systems could improve themselves without direct human involvement, according to recent reports. 


OpenAI is preparing for a future where artificial intelligence systems could improve themselves without direct human involvement, according to recent reports. The company is now hiring safety researchers to study the risks linked to this development, known as recursive self-improvement, where AI systems train better versions of themselves. This shift comes as AI capabilities continue to grow rapidly, with companies exploring how far automation can go in research and software development. What is self-improving AI and why it matters Recursive self-improvement refers to AI systems that can upgrade their own performance without relying heavily on human input. In simple terms, an AI system could build a smarter version of itself, which could then repeat the process. This idea, once limited to theory, is now being actively explored as AI tools improve. Reports suggest that the amount of work AI systems can handle is increasing quickly, with some studies indicating that their capabilities are doubling roughly every seven months. If this trend continues, AI systems could eventually handle complex tasks that currently require human researchers and engineers. Why OpenAI is hiring safety researchers OpenAI’s hiring move shows that the company is not only building advanced systems but also preparing for the risks they may bring. The role focuses on studying problems that may arise in the future, even if they do not exist today. The researchers will work on areas such as: Preventing data poisoning attacks Understanding how AI systems make decisions Studying risks linked to highly autonomous systems Tracking how AI tools are replacing human technical work The company has described this work as urgent, with potential impact on both industry and society. AI automation and impact on jobs One of the key points in the job listing is the need to track the “automation of technical staff”. This includes measuring how AI tools, such as coding assistants, are replacing or supporting human engineers. Industry research suggests that AI could soon handle a large share of software tasks that currently take humans days or weeks to complete. This raises questions about the future of jobs in research, engineering and development. At the same time, companies continue to hire experts to manage and guide these systems, showing a shift in the nature of work rather than a complete replacement. OpenAI’s long-term AI vision OpenAI CEO Sam Altman has previously outlined the company’s goals, including building an “automated AI research intern” in the near term and a fully automated AI researcher by 2028. While he has acknowledged that these goals may not be achieved, the company continues to invest in this direction, reflecting strong belief in the long-term potential of AI. Global race in self-improving AI Other companies are also exploring similar ideas. Anthropic has published research on using AI systems to monitor stronger AI models, while experts estimate there is a 60 per cent chance that AI research could happen without direct human involvement by the end of this decade. This shows that the race towards advanced AI is not limited to one company but is a broader industry trend. The development highlights a key shift in artificial intelligence: moving from tools that assist humans to systems that may operate independently. While this could improve efficiency and innovation, it also raises concerns about control, safety and employment. For now, companies like OpenAI are trying to balance progress with preparation, ensuring that as AI becomes more capable, its risks are understood and managed.

OpenAI is preparing for a future where artificial intelligence systems could improve themselves without direct human involvement, according to recent reports.&nbsp;

‘Future of work’: How OpenAI’s self-improving AI model is preparing to replace research jobs

In this constantly evolving world of research and investments, it takes a truly visionary entrepreneur to stand out and make a difference. Sankarsh Chanda, the founder of <a href="https://app.adjust.com/10dgg299">Savart</a> has turned his passion for investing and&nbsp;research into an innovative wealth management app that empowers individuals to pursue their financial freedom.&nbsp;

<h2>Humble beginnings at the age of 15</h2>

Can you believe that a renowned stock market article fully filled with jargon is easy to understand for a 15-year-old kid?&nbsp;

Sankarsh Chanda’s curious brain made him enter a man’s world and deal with some of the toughest topics like volatility, inflation &amp; recessions of Stock Markets and the Global Economy.

Sankarsh was thrilled by the school of thought pursued by Benjamin Graham. Graham was well known for evaluating companies with precision. His investing style paid little attention to fluctuating market sentiments and focused on the real-world performance of their companies.

Sankarsh used his sister’s DEMAT account to invest his scholarship money. He invested all his money in companies that he understood through P&amp;Ls, and balance sheets of various domestic, and international businesses.

<h2>Markets aren't always a smooth ride</h2>

Sankarsh Chanda's first investment went well, but it was more due to luck than skill. After enjoying this early success, he faced a good amount of losses in his next investments.&nbsp;These setbacks made him realize he needed to learn more and improve his skills to be successful in the unpredictable world of stock markets and later made good (or) 20x returns out of it. &nbsp;

<h2>Founding Savart </h2>

“I invested around Rs 1.5 lakh over a period of two years. The market value of my shares had gone up to around Rs 13 lakh in two years out of which I used 8 lakhs to start <a href="https://www.wionews.com/india-news/the-story-of-sankarsh-chanda-and-how-savart-became-one-of-the-top-investment-platforms-in-india-591252">Savart</a>,” says Sankarsh. After conducting 6-month market research, Sankarsh started <a href="https://www.wionews.com/india-news/the-story-of-sankarsh-chanda-and-how-savart-became-one-of-the-top-investment-platforms-in-india-591252">Savart </a>in 2017. His business model was simple: to provide long-term investment out-performance and provide the luxury of portfolio customization, which was reserved for High-Net-Worth investors only, to every individual.

<h2>What sets Savart's technology at the top from others? </h2>

Savart’s AI automates not just advice but also research, making it unique among its peer human-run advisories. A traditional advisory uses a set of rules fed by inputs directly or indirectly from a human analyst, whereas Savart’s AI controls the end-to-end process.

<h2>Is Savart’s tech necessary? When you have already existing products?</h2>

Even with the presence of existing products in the market. Savart's value lies in

1. Unlimited Research Potential: Research &amp; advice that covers 1 Billion data points and is not limited by the number of analysts working.

2. Alpha Generation/ Return Generation: The data input and the insights that are generated helps clients outperform the markets i.e., 23.38% CAGR since inception compared to 11.4% CAGR of Nifty 50.

<img alt="Savart vs Nifty 50 comparison" data-entity-type="file" data-entity-uuid="8c8c4e6a-27fb-442b-a02e-52130d6add91" src="//cdn.wionews.com/sites/default/files/inline-images/savart%20nifty.PNG" />

3. Real-Time Portfolio Tracking: People sleep, AI doesn’t need to. Portfolios are tracked in real-time and necessary actions are swiftly communicated.

4. Affordable Subscription Prices: This cutting-edge technology with the ancient wisdom of patience, value, ethics and inclusive investing is available at an affordable price.

<h2>How does Savart’s AI help clients in their personalised investing journey?</h2>

1. Quantitative Analysis: Traditional analysts look at Profit &amp; Loss statements, Balance Sheets and Cash Flow statements to make decisions, which are often based on (biased) assumptions and estimates. <a href="https://www.wionews.com/india-news/the-story-of-sankarsh-chanda-and-how-savart-became-one-of-the-top-investment-platforms-in-india-591252">Savart </a>has gone a step further to overcome these biases to build the technology for long-term investments, i.e., using billions of data points with the help of machine learning.

2. Qualitative Analysis: While doing qualitative analysis, Savart’s AI uses textual, visual, and graphical data to generate insights. Though quantitative analysis provides a mathematical &amp; statistical edge to the research. This analysis focuses on investments’ subjective, intuitive, and abstract aspects. This analysis includes not just legal jargon but also topics like ethics, corporate governance, brand, moat and innovation.

3. Risk Profiling, Portfolio tracking &amp; rebalancing: Qualitative &amp; Quantitative analysis are important for drawing out good opportunities. Understanding whether the opportunity is right for you or not becomes a bigger deal. Savart, with its proprietary Emotional, Financial &amp; General Analysis, understands you, your goals, risk appetite, aspirations, comforts, and needs well and suggests personalised portfolios.

<h2>How to make Savart’s AI work for you?</h2>

&gt; To have AI-driven research that helped its clients achieve superior returns in the market work for you.

&gt; Once you download the app on the Play Store/iOS.

&gt; Use your mobile number to log in.

&gt; A simple KYC process lets you verify your details.

&gt; Savart App asks you to complete a simple form to understand your risk profile. (EFG Analysis)

&gt; After the EFG analysis, the Savart app lets you define your financial goals.

&gt; From here, Savart’s AI takes over and determines the right investments to meet your goals.

&gt; Within a few seconds, Savart will make relevant investment plans for you with the available opportunities in the market.

&gt; Since Investments are habit-driven, Savart assigns you a Client Delight Associate who helps you achieve your goal on a timely basis.

(Disclaimer: Above mentioned article is a sponsored feature, This article is a paid publication and does not have journalistic/editorial involvement of IDPL, and IDPL claims no responsibility whatsoever.)

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In this constantly evolving world of research and investments, it takes a truly visionary entrepreneur to stand out and make a difference. Sankarsh Chanda, the founder of Savart has turned his passion for investing and research into an innovative wealth management app that empowers individuals to pursue their financial freedom.

In this constantly evolving world of research and investments, it takes a truly visionary entrepreneur to stand out and make a difference. Sankarsh Chanda, the founder of Savart has turned his passion for investing and research into an innovative wealth management app that empowers individuals to pursue their financial freedom. Humble beginnings at the age of 15 Can you believe that a renowned stock market article fully filled with jargon is easy to understand for a 15-year-old kid? Sankarsh Chanda’s curious brain made him enter a man’s world and deal with some of the toughest topics like volatility, inflation & recessions of Stock Markets and the Global Economy. Sankarsh was thrilled by the school of thought pursued by Benjamin Graham. Graham was well known for evaluating companies with precision. His investing style paid little attention to fluctuating market sentiments and focused on the real-world performance of their companies. Sankarsh used his sister’s DEMAT account to invest his scholarship money. He invested all his money in companies that he understood through P&Ls, and balance sheets of various domestic, and international businesses. Markets aren't always a smooth ride Sankarsh Chanda's first investment went well, but it was more due to luck than skill. After enjoying this early success, he faced a good amount of losses in his next investments. These setbacks made him realize he needed to learn more and improve his skills to be successful in the unpredictable world of stock markets and later made good (or) 20x returns out of it. Founding Savart “I invested around Rs 1.5 lakh over a period of two years. The market value of my shares had gone up to around Rs 13 lakh in two years out of which I used 8 lakhs to start Savart,” says Sankarsh. After conducting 6-month market research, Sankarsh started Savart in 2017. His business model was simple: to provide long-term investment out-performance and provide the luxury of portfolio customization, which was reserved for High-Net-Worth investors only, to every individual. What sets Savart's technology at the top from others? Savart’s AI automates not just advice but also research, making it unique among its peer human-run advisories. A traditional advisory uses a set of rules fed by inputs directly or indirectly from a human analyst, whereas Savart’s AI controls the end-to-end process. Is Savart’s tech necessary? When you have already existing products? Even with the presence of existing products in the market. Savart's value lies in 1. Unlimited Research Potential: Research & advice that covers 1 Billion data points and is not limited by the number of analysts working. 2. Alpha Generation/ Return Generation: The data input and the insights that are generated helps clients outperform the markets i.e., 23.38% CAGR since inception compared to 11.4% CAGR of Nifty 50. 3. Real-Time Portfolio Tracking: People sleep, AI doesn’t need to. Portfolios are tracked in real-time and necessary actions are swiftly communicated. 4. Affordable Subscription Prices: This cutting-edge technology with the ancient wisdom of patience, value, ethics and inclusive investing is available at an affordable price. How does Savart’s AI help clients in their personalised investing journey? 1. Quantitative Analysis: Traditional analysts look at Profit & Loss statements, Balance Sheets and Cash Flow statements to make decisions, which are often based on (biased) assumptions and estimates. Savart has gone a step further to overcome these biases to build the technology for long-term investments, i.e., using billions of data points with the help of machine learning. 2. Qualitative Analysis: While doing qualitative analysis, Savart’s AI uses textual, visual, and graphical data to generate insights. Though quantitative analysis provides a mathematical & statistical edge to the research. This analysis focuses on investments’ subjective, intuitive, and abstract aspects. This analysis includes not just legal jargon but also topics like ethics, corporate governance, brand, moat and innovation. 3. Risk Profiling, Portfolio tracking & rebalancing: Qualitative & Quantitative analysis are important for drawing out good opportunities. Understanding whether the opportunity is right for you or not becomes a bigger deal. Savart, with its proprietary Emotional, Financial & General Analysis, understands you, your goals, risk appetite, aspirations, comforts, and needs well and suggests personalised portfolios. How to make Savart’s AI work for you? > To have AI-driven research that helped its clients achieve superior returns in the market work for you. > Once you download the app on the Play Store/iOS. > Use your mobile number to log in. > A simple KYC process lets you verify your details. > Savart App asks you to complete a simple form to understand your risk profile. (EFG Analysis) > After the EFG analysis, the Savart app lets you define your financial goals. > From here, Savart’s AI takes over and determines the right investments to meet your goals. > Within a few seconds, Savart will make relevant investment plans for you with the available opportunities in the market. > Since Investments are habit-driven, Savart assigns you a Client Delight Associate who helps you achieve your goal on a timely basis. (Disclaimer: Above mentioned article is a sponsored feature, This article is a paid publication and does not have journalistic/editorial involvement of IDPL, and IDPL claims no responsibility whatsoever.) WATCH WION LIVE HERE You can now write for wionews.com and be a part of the community. Share your stories and opinions with us here.

The story of Sankarsh Chanda and how Savart became one of the top investment platforms in India

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‘Future of work’: How OpenAI’s self-improving AI model is preparing to replace research jobs

The story of Sankarsh Chanda and how Savart became one of the top investment platforms in India