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Data Scientists on AI’s chopping block? Microsoft Research sounds a career alarm

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For years, ‘data scientist’ was the crown jewel of tech careers—the high priesthood of big data, commanding fat paychecks and corporate reverence. But if Microsoft Research’s latest study is anything to go by, the golden halo may be slipping. Their analysis of over 200,000 Bing Copilot interactions warns that data science is among the jobs most exposed to generative AI replacement, raising a brutal question: Is the very craft of data science at risk of automation?Also read: 40 jobs that AI cannot touch

The uncomfortable truth: AI is eating its own maker’s lunch

Microsoft’s findings do not mince words. The very tools designed to accelerate data processing, model building, and analytics now threaten to undercut the human experts who built the field. Tasks once billed as ‘complex human judgment’—feature engineering, predictive modelling, even advanced analytics—are now being executed by AutoML pipelines and generative AI in minutes, not weeks.What was once a niche skillset is rapidly commoditised by automation. The data scientist of today faces a paradox: The more powerful their tools, the less scarce their skill appears to be.

Not extinction, but demotion: The looming skills shift

The Microsoft study stops short of announcing the death of data science, but it sketches a future where routine number-crunching is fully machine-led, and humans are relegated to supervisory, strategic, and ethical oversight roles.Hiring trends already hint at this shift:

  • Job postings are quietly pivoting from ‘Python + ML modeling’ to ‘business insight, AI interpretability, and cross-functional leadership.’
  • Companies increasingly prefer candidates who can audit AI outputs, manage data ethics, and translate algorithmic outcomes into boardroom decisions, rather than hand-craft models line by line.

In other words, data science is no longer about coding clever models—it’s about curating, questioning, and governing machines that do the modelling for you.

The harsh career reality for students

For students burning midnight oil on coding bootcamps and hackathons, Microsoft’s research reads like a career curveball. Investing purely in technical chops might not guarantee future-proof employability. Here’s why:

  1. AI eats low-level modelling jobs first. Entry-level roles in data prep, cleaning, and regression modelling may vanish fastest.
  2. Senior positions are safe—but harder to reach. The AI-infused workplace values contextual expertise, leadership, and ethics. These can’t be automated but take years to build.
  3. Domain knowledge will trump generic data skills. Data science married to finance, biotech, or climate science remains scarce and in demand. So, the ‘generalist’ data science could vanish soon.

How to outsmart the machine: A career survival plan

Microsoft Research has handed the data science community an uncomfortable mirror: the profession that rose to prominence by automating decisions is itself on the cusp of automation. The next decade won’t kill data scientists, but it will brutally separate those who ride AI as a tool of leverage from those replaced by it.For ambitious students and mid-career professionals, the message is clear: stop being the algorithm’s hands—start being its brain.

  • Stop competing with AI; start commanding it. Learn to design AI workflows, not just models. Skills in prompt engineering, AI governance, explainability, and bias mitigation are future currency.
  • Stack your skills. Pair data science with a domain speciality (healthcare analytics, energy markets, defence simulations). Machines can crunch numbers; they can’t understand nuanced sectoral problems yet.
  • Sharpen human edges. Storytelling with data, strategic decision-making, leadership in cross-disciplinary teams—these remain stubbornly human.
  • Reskill, relentlessly. Expect to pivot every 2–3 years. Certifications in AI ethics, advanced ML ops, or policy-tech intersections may be more valuable than a traditional degree by 2030.





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Confort Lab, a manufacturing operation management AX (artificial intelligence conversion) solution c..

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48 minutes ago

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September 2, 2025

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Easy Manufacturing AX Strength Accelerates AI Transformation for Small and Medium Businesses

Confort Lab Logo [Kakao Ventures]

Confort Lab, a manufacturing operation management AX (artificial intelligence conversion) solution company, announced on the 2nd that it has attracted pre-series A investment from Kakao Ventures.

Based on manufacturing data, ConfortLab develops solutions that help automate operational management and transition to AI. From various facilities and lines at manufacturing sites to IT systems, different data are easily integrated and standardized to be converted into data assets that AI can analyze.

Based on this, it integrates systems and AI-based automation functions essential for manufacturing operations such as production (MES), facility (EAM), quality (QMS), and energy (EMS) management, and helps manufacturing companies of all sizes accelerate AI transformation beyond digital transformation. Most small and medium-sized factories are improving their manual work and skill-dependent work methods with AI-based quality tracking, control condition AI recommendation, and data-based AI facility maintenance functions to reduce defect rates and increase productivity.

The core product, PORTA, a no-code-based AX platform, consists of “PORACON,” facility and sensor data collection equipment, “PORTA Neurobase,” a web-based no-code development tool “PORTA STUDIO,” and a manufacturing operation management system “PORTA Apps,” which integrates AI agents. The construction of infrastructure, which took several months, was shortened to less than three days and the cost was also reduced to one-fifth, greatly lowering the barriers to introducing the site. Based on attracting investment, ConfortLab plans to strengthen its position as an AX innovation partner in the manufacturing site.

Confort Lab consists of CEO Kim Ki-joong, who has accumulated technical experience and expertise in industrial system solutions through TmaxSoft, Doosan Energy, and SAP Labs, Vice CEO Kim Ha-na, a global marketing expert from Hancom Group Strategic Marketing, and CTO Lee Sung-geun, a cloud and system software expert from TmaxSoft and SAP Labs. Strong teamwork accumulated by working together for more than a decade and system engineering capabilities accumulated in complex manufacturing sites are considered strengths. It is said that it is an optimized team for building high-quality integrated solutions from manufacturing equipment installation to network and data platforms and leading automation of manufacturing sites.

Kim Young-moo, a Kakao Ventures judge, said, “It is expected to create a core foundation that advances the digital transformation of the manufacturing industry beyond the level of simple automation as a starting point for object ontology that binds factory operations into one language.”

“In order for the manufacturing industry to take off again, innovation in small and medium-sized factories, which account for 99% of the total, is essential,” said Kim Ki-joong, CEO of Confort Lab. “Conport Lab will grow into a ‘Palantir of Korea’ that creates AX solutions that the field really needs and designs the future of manufacturing on the global stage beyond Korea.”



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Bublik reacts on social media after losing to Sinner: “It’s Artificial Intelligence”

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3 hours ago

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September 2, 2025

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A few minutes after losing to Jannik Sinner at the US Open 2025 with a convincing score against him, Alexander Bublik reacted on social media to the incredible performance of the world number one. The Kazakh player commented on a picture with the result: “AI,” once again referring to the Italian as Artificial Intelligence, always as a compliment to his amazing level on the court.

 

This news is an automatic translation. You can read the original news, Bublik reacciona en redes sociales tras perder contra Sinner: “Es Inteligencia Artificial”





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Indonesia unveils national AI roadmap

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5 hours ago

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September 2, 2025

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Artificial Intelligence (AI) could help Indonesia achieve its vision of Golden Indonesia 2045 with the right strategy and governance, according to Minister of Communication and Digital Affairs, Meutya Hafid. 

Stating this in her forward to Indonesia’s National AI Roadmap White Paper, she said the AI roadmap would provide policy direction to accelerate AI ecosystem development to ensure the country was not to be left behind in a field increasingly dominated by advanced countries and global tech giants. 

The White Paper, drafted by the AI Roadmap Task Force, a 443-member body representing government, academia, industry, civil society, and the media, was launched by the Ministry of Communication and Digital in early August.

It has been envisaged as a strategic document that would serve as the country’s reference for adopting and developing AI technology in a more focused, inclusive, and ethical manner. The document has been circulated for public consultation to gather wider input from stakeholders. 

This initiative builds on the National AI Strategy 2020-2045, which was an initial framework developed by the Collaborative Research and Industrial Innovation in AI (KORIKA), an organisation formed by scientists, technocrats and industry leaders to accelerate the AI ecosystem in Indonesia. 

However, that strategy has struggled to keep up with the rapid breakthroughs in generative AI (GenAI) since late 2022. 

Three major action plans 

The national AI roadmap outlines three main action plans: AI ecosystems, AI development priorities, and AI financing – all anchored in ethical guidance and regulation.

This roadmap also breaks down the action plan into three-time horizons: short term (2025-2027), medium term (2028-2035) and long term (2035-2045).

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The national AI roadmap contains three main action plans, covering AI ecosystem governance, national AI development priorities, and AI financing. Image: Ministry of Communication and Digital Affairs

Indonesia’s AI ecosystem development would focus on three main pillars.  

The first pillar was talent development.  

Indonesia aimed to nurture a large pool of skilled professionals who could both use and create AI innovation. 

The roadmap sets an ambitious target of producing 100,000 AI talents annually. Around 30 per cent would be developers, divided further into AI specialists (30 per cent) and practitioners (70 per cent), and the remaining 70 per cent would be AI end-users. 

The government also aimed to ensure 20 million citizens are AI-literate by 2029.  

The next pillar was research and industrial innovation.  

The roadmap emphasised advanced, relevant, and sustainable AI research that delivered real benefits to society. 

To achieve this, the government would encourage agencies, universities, and industries to strengthen AI programmes in priority sectors.  

A cross-sectoral open sandbox platform would also be developed to support experimentation and collaboration. 

The last pillar in Indonesia’s AI ecosystem was infrastructure and data.  

To foster domestic AI innovation, the government planned to expand digital infrastructure, including high-performance computing, GPUs/TPUs, and a national cloud hosted in sovereign data centres to ensure secure and regulated data management. 

The white paper also outlined plans to promote the development of green data centres through public–private partnerships. 

Strategic priorities in AI development 

The roadmap focuses on developing AI for strategic use cases, ensuring that AI adoption delivers meaningful and sustainable impact.

These priorities closely align with the country’s national development agenda and President Prabowo’s Asta Cita vision.  

The priority sectors for AI include food security, healthcare, education, economy and finance, bureaucratic reform, politics and security, energy, environment, housing, transport and logistics, as well as arts, culture, and the creative economy.  

Public services were also identified as an immediate priority for the 2025–2027 term. In healthcare, AI would be applied for early disease detection, remote patient monitoring, and optimising the distribution of medicines and vaccines.  

In education, the focus would be on adaptive learning and digital platforms for personalised teaching materials. The government also plans to develop automated evaluation systems to ease assessment processes in schools. 

In governance, AI applications would centre on intelligent chatbots for public services and data-driven policy analytics.  

For transport and mobility, development would be directed towards smart traffic systems, public transport management, and the optimisation of national logistics.  

Financing the national AI agenda  

The roadmap outlined a phased financing strategy, combining state budget allocations, private sector contributions, and external partnerships through bilateral and multilateral collaborations.

Over the next two decades, the government aimed to establish a sustainable financing ecosystem driven by industry participation and international investment. To achieve this, Indonesia will expand fiscal incentives to encourage AI-related investments.  

A notable feature of the roadmap was the role of Danantara, Indonesia’s newly established sovereign wealth fund, which has been tasked with spearheading AI financing.  

Danantara would design innovative financial instruments, establish a Sovereign AI Fund, and develop blended financing models for the country’s strategic AI projects.  

In the initial phase, financing would target fundamental research, pilot projects in the public sector, and the development of data and computing infrastructure.  

Subsequent stages would extend funding to industries, research institutions, universities, and domestic AI start-ups, with the goal of strengthening Indonesia’s AI ecosystem and boosting its global competitiveness. 



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