The world of AI-powered Search has taken a groundbreaking leap with OpenAI Deep Search. Unlike traditional LLMs (Large Language Models) or even standard ChatGPT queries, Deep Search goes beyond the conventional outcomes in researching, analysing, and synthesising information—accomplishing everything in minutes that would take endless human hours.
With ChatGPT already a staple for many, any new feature sparks curiosity, and Deep Search is no different. But what sets it apart? I have explored OpenAI's official release and broken down everything you need to know about this feature: what's new, how it works, and if it is worth using. So, let's dive in.
What is OpenAI Deep Search?
According to the official release by OpenAI, Deep Search is a new agentic capability that conducts multi-step research on the Internet for complex tasks. It can form a report on the level of a research analyst. It is powered by the OpenAI O3 model, ideal for web browsing, leveraging reasoning to research PDFs, and thousands of other online resources.
The feature's ability to synthesise knowledge contributes to the broader goal of developing AGI (artificial general intelligence). This feature will likely be the first step towards a much brighter AI-powered knowledge era.
Why is OpenAI Deep Search built?
Deep Search can do independent research and reasoning and simplify the data or knowledge available online. It was trained using the same reinforcement methods as o1, OpenAI's first reasoning model. To acquire its capabilities, it was trained on real-world scenarios requiring browser and Python tools.
While the first model was excellent in math, coding, and other technical domains, it lacked decision-making skills in many real-world scenarios that require extensive research. Deep research was built to fill these gaps left by the first OpenAI model.
The feature is highly beneficial for people who do intensive knowledge work in finance, science, law, etc. Every output by Deep Search is research-based, fully documented, and even has citations. As a scrum master, I can foresee people using it in developing product guidebooks and market research for the introduction of any new feature. How DevOps with AI is Shaping the Future of IT Teams?
How to use Deep Search?
It's pretty straightforward: Go to chatGPT, select "Deep Search" in the message composer, and enter your query. You can even attach files and Excel sheets to add context to your questions. Once it starts running, sources and citations are displayed on the right side, which is used for the content produced. This process is ideal for multi-facet and domain-specific research and can take 5- 30 minutes, depending on the level of research required.
Please note: Deep Search is available for all users on paid plans on all platforms: web, iOS, Android, macOS, and Windows.
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Difference between ChatGPT and Deep Search
Let me pinpoint the key differences between ChatGPT and OpenAI Deep Search so you can decide which one to choose.
I used ChatGPT and Deep Search with the same query- (Explain in 500 words why scrum masters are the future with relevant data points backing it up). To understand the difference better, here are the results for you:
Generated by ChatGPT
As organisations increasingly embrace Agile methodologies, the role of the Scrum Master has become more crucial than ever. With 74% of companies adopting Agile practices for their software development and business processes (State of Agile Report, 2023), Scrum Masters are at the forefront of this transformation. They play a pivotal role in enhancing productivity, facilitating collaboration, and driving innovation—making them an indispensable asset in the evolving workplace.
1. The Growing Demand for Scrum Masters
The demand for Scrum Masters is rising globally, with LinkedIn ranking it among the Top 15 Emerging Jobs. According to the U.S. Bureau of Labor Statistics, the employment of management analysts (which includes Scrum Masters) is expected to grow 14% by 2030, much faster than the average for all occupations.
- Glassdoor reports that Scrum Masters earn an average salary of $110,000 annually in the U.S., reflecting their high demand.
- In India, Scrum Masters command salaries ranging from ₹12 to ₹25 lakh per annum, depending on experience and industry.
- The Certified Scrum Master (CSM) certification is among the top-paying Agile certifications, further proving the profession's value.
2. Agile Adoption Beyond IT
Scrum Masters are no longer limited to IT and software development. Agile principles are now applied across finance, healthcare, manufacturing, and marketing, with over 50% of Agile transformations happening outside IT (Business Agility Report, 2023).
Companies like Amazon, Google, and Tesla leverage Agile to enhance operational efficiency, and Scrum Masters serve as key enablers of these transformations. As more industries recognise the benefits of Agile, Scrum Masters will continue to be in high demand to facilitate change and drive efficiency.
3. The Rise of Remote & Hybrid Work
With 58% of knowledge workers now working remotely or in hybrid setups (Gallup, 2023), organisations need skilled professionals who can keep teams aligned, motivated, and productive in virtual environments.
Scrum Masters help:
- Foster collaboration in remote teams by ensuring smooth communication.
- Maintain team focus by removing roadblocks and promoting Agile best practices.
- Increase efficiency, with Agile teams being 25% more productive than traditional teams (Harvard Business Review, 2022).
4. Business Agility & Competitive Advantage
Organisations must adapt quickly to stay competitive in an era of rapid technological advancements. Scrum Masters play a crucial role in achieving business agility, allowing companies to:
- Deliver products faster – Agile teams release features 37% faster than traditional teams.
- Enhance customer satisfaction – Agile projects see a 20-30% higher customer satisfaction rate.
- Reduce costs – Agile adoption leads to 15-25% cost savings by improving efficiency.
5. Final Thoughts
Scrum Masters are not just facilitators of Agile—they are change agents, problem-solvers, and strategic enablers. As companies continue to embrace Agile for speed, adaptability, and innovation, the Scrum Master role will only grow in importance. With strong demand, competitive salaries, and cross-industry applications, Scrum Masters are undeniably the future of work.
Generated by Deep Search
Scrum Masters emerge as indispensable leaders in today's fast-paced, ever-changing business landscape. As organisations worldwide shift toward agile methodologies, Scrum Masters have become the linchpin in driving team efficiency, collaboration, and continuous improvement. By facilitating daily stand-ups, sprint planning in agile, and retrospectives, they ensure that teams adhere to the Scrum framework and adapt quickly to evolving market demands. This capability makes Scrum Masters crucial for any organisation striving to stay competitive.
1. Agile practices have witnessed a remarkable surge in adoption. Recent studies reveal that approximately 66% of organisations currently use Scrum, underscoring its vital role in modern project management
2. Moreover, teams implementing Scrum report dramatic improvements in productivity and product quality—with some seeing quality outcome increases of up to 250% due to iterative feedback and continuous learning cycles
In this environment, the Scrum Master functions not merely as a facilitator but as a servant leader who removes impediments, nurtures cross-functional collaboration, and cultivates an agile mindset within teams.
3. The future for Scrum Masters is further brightened by expanding responsibilities beyond traditional roles. As digital transformation accelerates, companies seek agile professionals who integrate emerging technologies such as artificial intelligence and automation into their workflows. This integration enhances decision-making through data-driven insights and streamlines processes, positioning Scrum Masters as key agents of innovation. Projections indicate that the demand for Scrum Masters will grow by around 24% by 2026, reflecting the increasing reliance on agile practices to navigate complex business challenges
4. Financially, the Scrum Master career path is auspicious. In the United States, the median base salary for a Scrum Master hovers around $100,000, and LinkedIn reports a staggering 400% year-over-year job growth in this role
5. Globally, similar trends are observable. For example, in India, the average salary for a Scrum Master is about Rs 14 lakh per annum—a figure that is poised to rise as agile methodologies further penetrate the IT and business sectors
6. These attractive compensation packages, combined with the relatively low barrier to entry through certification programs, make the role an appealing career option.
In addition, the versatility of the Scrum Master role contributes to its future relevance. Many professionals use their experience as a springboard to transition into Agile Coach, Product Owner, or How to become a Release Train Engineer roles. This adaptability is crucial as organisations seek leaders who can bridge the gap between technical execution and strategic business objectives
7. As companies evolve, they increasingly value professionals who manage projects and drive cultural and organisational change.
In summary, Scrum Masters are at the forefront of the Agile revolution, serving as catalysts for enhanced productivity, innovation, and adaptability. With robust data supporting their growing demand and competitive compensation levels, it is clear that the role of the Scrum Master is not only central today but also poised to be a cornerstone of future organisational success. Investing in Scrum Master skills and certifications is a strategic move for professionals looking to thrive in the dynamic world of agile project management.
The Potential of Deep Search
Talking about the tool's potential, it is not far-fetched that this feature will help OpenAI build a more substantial market, catering to a broader audience. As discussed, deep research was conducted on end-to-end reinforcement learning on complicated browsing and reasoning tasks across numerous domains. With the help of the training, the model could execute a multi-step trajectory to discover the data required for the research, backtracking and reacting to the real-time information wherever it was needed. With the help of the Python tool, Deep Search can read through graphs, browse user-generated files, and produce new images or graphs in the responses. The feature is a boon for researchers who spend hours searching for secondary data research as this tool also minimises the time by providing the sources.
Humanity's Last Exam
Humanity's Last Exam sets up a benchmark for LLM capabilities. It comprises a dataset with 2700 challenging questions to determine the cability of the AI model. The model powering Deep Search has set a new high score at 26.6% accuracy. The model was tested with the help of thousands of questions across domains, and we could see a significant improvement in the result from the first GPT-4o. The betterment was seen mainly in chemistry, social science, and mathematics. The model portrayed a human-like approach, which is great news for researchers. Moreover, the numbers are promising, attracting professionals across industries.
Here is the score for all the knowledge-based AI models launched to date:
Will it be the right choice for you?
Now that I have told you everything about Deep Search, its potential, and the significant difference between ChatGPT and DeepSearch, the question is: Should I use it? Do you know what are the 7 ways to use AI in Automation Testing.
Deep Search has plenty of potential, but you need to be cautious. It can save time if you need complex research, but verify the information before deploying it. Deep Search states that the tool can sometimes hallucinate facts and interfere with the original information. The program might also face difficulty differentiating between factual, original information and rumours.
Deep Search can help in many ways, but as it is in its early stages, “I recommend not relying on it for your critical research requirements.” Wait until the bugs are resolved and the tool provides undisputed original and trusted data.
