Рубрика: AI

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There is no doubt that gaming and AI are deeply intertwined. Beyond the fact that several veteran AI builders are avid players of strategy games like Dota 2, firms like OpenAI and Google DeepMind have long been training AI agents within such game environments.

Now, however, the day seems closer when AI, specifically generative AI, is edging towards actually creating games. That too, with just input of prompts on vibe coding tools.

For instance, Stepan Parunashvili, co-founder and CTO of InstantDB, did not set out to write a manifesto for the next phase of AI development. He only wanted to see what would happen if the year’s most powerful models tried to build the same thing under pressure.

His choice was not a text parser or an algorithmic puzzle. It was Counter-Strike—or at least he tried to make it look like a first-person shooter game. There are mixed opinions on Hacker News about the results, with some calling it great and others saying it’s like a junior developer project.

For Parunashvili, the rules were simple. The game had to run in the browser, it had to be 3D, it had to be multiplayer, and it had to be built by the model itself. No human patches. No hand-coded rescue missions.

The result, or the goal, was not just a working game from each model; it offered a new way to measure AI systems.

The New Benchmarks are the Same

For context, in November, AI labs released their sharpest tools. GPT-5.1 Codex Max, Gemini 3 Pro and Claude Opus 4.5 arrived almost on top of each other. Instead of comparing them on sterile benchmarks, Parunashvili asked a cleaner question: If you hand them a real project where everything can break at once, how do they behave?

Parunashvili, in his YouTube video, walked through each step. Watching the agents build, break, adjust, rebuild and finally stabilise a multiplayer shooter gives a strange new picture of AI progress.

Suhail Doshi, former CEO of Mixpanel, described the challenge as “one way you can sense what’s coming next as a result of AI progress.” And that’s what it is. What made the experiment striking was not the success but the split personality of the results.

Claude built the nicest world. Its maps had shape. Its characters looked almost human. Its gun animations felt natural.

Gemini handled the backend like a seasoned systems engineer. It synced movement across players, handled rooms and saved maps without drama.

Codex Max landed somewhere in between. It fixed its mistakes, held the project together and rarely became confused.

These differences are the same ones visible in coding tests and benchmarks as we covered before.

Read: GPT-5.1 vs Gemini 3 Pro vs Claude Opus 4.5

Claude becomes the careful executor when the work demands clarity. Gemini becomes a deep reader when the work demands structure. Codex becomes a dependable worker when the work demands long sessions without losing track.

A comparison of the three models on separate coding challenges mapped neatly onto the Counter-Strike results.

Opus 4.5 handled ambiguous engineering tasks better than anyone. Codex-Max stayed alert across long debugging loops. Gemini excelled at reasoning tasks that required long context and tight logic.

So What’s the Outtake?

TL:DR performance:
Opus 4.5 won the frontend. It made better maps and better models
Gemini 3 Pro won the backend. It got more done in one shot.
Codex got the most "2nd place": it was good but not great at both frontend and backend.
Here's the scorecard: pic.twitter.com/MF7d56jcAu

— Instant (@instant_db) December 1, 2025

The Counter-Strike test compressed all of this into a few hours of building maps, enemies, guns, sound and multiplayer rooms.

At the frontend stage, Claude won everything from polygons to sound effects. When the task switched to presence, shooting logic and persistence, Gemini became the strongest. Codex stayed steady. It rarely produced the prettiest output or the deepest insight, but it adapted without falling over.

The one place where Claude stumbled was in the React refactor. useEffect ran twice, two canvases appeared and the animation loops duplicated. It was the same kind of trouble Claude faces in messy codebases. Parunashvili pointed out that this was not a model problem but a broader developer experience problem.

Humans also get tripped by the same hooks. He said the task showed the gap between “strictly vibe coding” and real engineering. That gap is where the next generation of tools must operate.

The multiplayer pass exposed another truth.

Gemini kept running builds to find errors before the user noticed. Codex relied on the introspection of libraries. Claude read the document step by step.

These styles matter because they shape how the future of automated coding feels. A model that tests itself takes work off developers’ plates. A model that reads documents but does not experiment will move carefully but slowly.

All three models produced almost “working” Counter-Strike clones with no human code. That is important. A game forces the entire stack into motion. Physics, lighting, sound, UI, networking, persistence, permissions and refactoring collide in a small space.

The test becomes a live arena where a model’s style can be seen as clearly as its skills.

The takeaway is sharper than any benchmark. Benchmarks tell you how a model performs on a clean question. Counter-Strike tells you how a model behaves when the work is dirty.

Claude builds beautiful worlds until the foundation shifts. Gemini handles chaos in the backend without blinking. Codex quietly finishes the job.

Parunashvili’s simple prompt has become a lens for where AI tools are going next. It is also a warning. “The promise that you never have to look at the code doesn’t quite feel real yet,” he said.

The question now is not whether AI can vibe-code games. It is whether game-building becomes the new baseline for judging what an AI model is capable of.

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The tenth anniversary of Russia’s flagship artificial intelligence conference, AI Journey, was never going to be a modest affair. In Moscow, the country’s largest bank, Sber, now a fully-fledged technology group, used the event to parade an ecosystem that many in the West had assumed would be impossible under sanctions: large language models trained on domestic data, industrial-grade robotics and a new generation of “intelligent” devices built almost entirely on a Russian stack.

One of the most revealing exhibits was the humanoid robot and a cash machine. Notably, the robot boasts voice communication capabilities enabled by integrating GigaChat’s conversational function.

Sber’s new ATM looks like a minor prop in a science-fiction film. It has dual screens, a dense array of sensors and a voice interface powered by the group’s GigaChat assistant. ATM authenticates customers biometrically, adjusts to their behaviour, and, at least in principle, can flag signs of distress or confusion.

In much of Europe, ATMs remain sturdy but dull boxes that have changed little in twenty years. In Moscow, the bank is quietly using them to test its vision of AI-driven retail finance.

The obvious question is how this happened. How has a country subjected to one of the most far-reaching regimes of technological restrictions not simply kept moving but started to set its own standards in some corners of the innovation race?

Part of the answer is historical. Long before 2022, Russia poured money and talent into artificial intelligence. Sber, in particular, spent a decade hiring researchers, building data centres, and positioning itself less as a lender than as an operating system for everyday life. When access to Western vendors narrowed, there was already enough accumulated competence to improvise. There is something else at work that sits awkwardly with the idea of a “technological isolation” strategy. Rather than retreating behind a digital curtain, Russia has chosen, at least selectively, to publish the very tools that underpin its ambitions.

At AI Journey, Sber announced that it was opening the weights of two new flagship mixture-of-experts models in its GigaChat family, Ultra-Preview and Lightning. They were built from scratch for Russian-language tasks, along with the latest generation of GigaAM-v3 speech recognition models. Furthermore, all image and video generation models from the latest Kandinsky 5.0 lineup — Video Pro, Video Lite and Image Lite — are also available open source.

Additionally, Sber opens weights to compression models K-VAE 1.0, essential for training visual content generation models. For developers and start-ups, these are not glossy marketing slogans but usable artefacts: code, documentation and pretrained systems that can be adapted, fine-tuned and embedded into products any company.

In other words, a country that is supposed to be technologically quarantined is placing part of its AI “crown jewels” into the global open-source commons. The message is not subtle: Russia intends to be a standards-maker, not a standards-taker.

Open models are also a way to gather feedback and normalise Russian technologies in international workflows. Yet the fact remains that, while much of the West is busy closing corporate models and erecting legal fences around training data, Russian engineers are betting that influence will belong to those who contribute bricks to the shared infrastructure of global AI.

Sber is the most visible face of this strategy and, for now, its safest bet. The group has capital and a captive market of tens of millions of users. It is also increasingly framed at home as the guarantor of the country’s “technological sovereignty”: if foreign platforms disappear, Sber’s stack is meant to fill the void.

A country that was meant to be technologically contained is not only still in the race but also, in some domains, starting to run in its own lane and inviting others to follow.

For policymakers who believed that isolation would quietly solve the “Russia problem” in technology, that is an inconvenient development. For the rest of us, it is a reminder that code respects talent, incentives and scale more than it respects sanction lists. The world tried to push Russia to the margins of the digital map. Events in Moscow suggest it may have succeeded instead in creating another centre of technology gravity.

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Japanese multinational tech giant NTT Global Data Centres is expanding its data centre footprint in Karnataka.

It announced a new data centre campus in Devanahalli, with the first facility scheduled to be operational from December 3.

The data centre is designed for a total IT load of over 67.2 MW within a larger 100 MW project footprint.

The announcement took place at the ‘Karnataka: The Destination for Futuristic Data Centres – Sustainability, Scalability, Security’ event hosted by The Associated Chambers of Commerce & Industry of India (ASSOCHAM).

Alok Bajpai, managing director of NTT Data India, said the development marks one of the company’s most important milestones in the state.

NTT’s fourth Bengaluru facility—its newest data centre in Devanahalli—is situated on an 18-acre campus, with the first building going live immediately and a marquee Bengaluru-based customer scheduled to be onboarded this month, according to Bajpai.

Calling it a “large campus”, Bajpai emphasised its strategic importance in the company’s national expansion plans.

He said NTT has already invested about ₹1,700 crore in Bengaluru’s data centre operations and has committed an additional ₹2,400 crore for the new Devanahalli campus.

NTT’s Bengaluru footprint now has four data centres: the DC2, DC3 and the DC3X, along with the new one in Devenahalli.

Across India, NTT’s footprint covers West (Mumbai/Navi Mumbai), North (Delhi NCR/Noida), South (Bengaluru, Chennai) and East (Kolkata).

Bajpai, outlining NTT’s current India footprint, said, “We have almost 400 megawatts (MW) of IT capacity, which is live, and there is another 200 MW under construction and development.”

“In India, we are the number one global data centre provider with about 22 live data centres and a couple of them under construction right now.”

According to a Cushman & Wakefield report from October 2025, Bengaluru hosts 15 data centres with an operational capacity of 76 MW. The new NTT inauguration significantly boosts data centre capacity in Bengaluru.

At the same event, Sunil Gupta, co-founder and CEO of Yotta, positioned Karnataka as the state best placed to lead India’s AI and high-performance computing transition.

He said the state is evolving from being the country’s IT nerve centre to potentially becoming “India’s large compute capital”.

“The Karnataka IT Policy 2025-30, backed by over ₹967 crore in strategic incentives, sets the stage for this transition from IT capital to compute capital and reinforces the state’s commitment to building the digital backbone of the future,” Gupta said.

He added that, with its progressive data centre policy, strong renewable energy ecosystem and deep talent pool, Karnataka is now positioned to become India’s AI and green hyperscale data centre capital.

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Anthropic announced on Wednesday that Claude Code’s run-rate revenue now stands at $1 billion. The company has achieved the milestone in six months since it was made publicly available.

“Claude Code has grown from its origins as an internal engineering experiment into a critical tool for many of the world’s category-leading enterprises, including Netflix, Spotify, KPMG, L’Oreal, and Salesforce,” the company said in the announcement.

In addition, Anthropic announced that it is acquiring the startup Bun, which provides an open-source toolkit to simplify and accelerate full-stack JavaScript/TypeScript development.

The company calls Bun’s toolkit an ‘essential infrastructure’ for AI-led software engineering, as it combines a runtime, package manager, bundler and test runner.

“Bun has improved the JavaScript and TypeScript developer experience by optimising for reliability, speed, and delight. For those using Claude Code, this acquisition means faster performance, improved stability, and new capabilities,” the company said.

Bun, which gets more than 7 million monthly downloads and has earned 82,000 stars on GitHub, will continue to remain open-source and MIT-licensed. Anthropic did not reveal any financial details of the acquisition.

At the same time, a report from Financial Times stated that Anthropic is working on its initial public offering, which would value the company at more than $300 billion. The report added that the company will work with the law firm Wilson Sonsini, which has advised Anthropic since 2022, and has worked with other high-profile tech IPOs such as Google, LinkedIn, and Lyft.

However, an Anthropic spokesperson told the media outlet that it is a ‘standard practice’ for companies at such a scale to operate as a ‘publicly traded company’, but the company has not yet decided whether to go public.

The developments have occurred as OpenAI is reportedly planning to go public, even as CFO Sarah Friar publicly denied them.

Last month, CNBC reported that Microsoft will invest up to $5 billion, and NVIDIA up to $10 billion in Anthropic, which would value the company at $350 billion.

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IBM has teamed up with Karnataka Bank Limited (KBL) to improve its digital banking framework utilising IBM Cloud Pak for Integration on Red Hat OpenShift.

Carried out by Fyrii, an IBM ecosystem partner, alongside the IBM Customer Success Team, this has enabled Karnataka Bank to build a secure, scalable, and flexible application programming interface (API) platform while reducing the total cost of ownership and enhancing its digital infrastructure.

Through this joint innovation, Karnataka Bank has developed a contemporary, secure, and scalable API platform that enhances the bank’s digital infrastructure and lowers operational expenses.

“With IBM Cloud Pak for Integration on Red Hat OpenShift, we now have an agile and secure platform that allows us to scale operations across India, simplify system management, and reduce costs, all while improving the overall customer experience,” Venkat Krishnan, chief information officer at KBL, said.

This platform enables faster deployment of services such as digital payments, loan processing, and third-party integrations, while ensuring secure connections to internal and external systems, the press release said.

The upgraded API infrastructure boosts security, increases scalability by 50%, and reduces operational costs by 30% through a container-based platform designed for efficient microservices. It allows the bank to manage all API traffic via digital gateways and enables external partners to access the bank’s AI models through the AI Gateway.

The release said that this setup ensures seamless communication with internal, external, and cloud systems while integrating with UIDAI, CERSAI, GST, Reg-Tech, and CBDT services, supporting quick adaptation to market and regulatory changes.

“Today’s banking sector is more complex than ever, comprising many systems and data sources in constant use. To stay ahead, banks require intelligent automation that not only streamlines operations but also anticipates issues before they arise,” said Viswanath Ramaswamy, VP of Technology at IBM India & South Asia.

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This year, AI has rapidly moved from tech labs into police stations, courtrooms and crowded streets across India. Several states embraced AI to address long-standing problems, including slow investigations, missing offenders, procedural lapses and overstretched personnel.

From facial-recognition systems that claim to solve robberies in hours, to biometric databases mapping criminal histories, to chatbots explaining legal procedures, these tools promise efficiency and precision. But every deployment comes with trade-offs. Critics warn of mass surveillance, misidentification, uninformed decision-making and weak safeguards.

Here’s a list of six instances in 2025 in which Indian states leveraged AI in everyday policing.

1. Delhi

Delhi Police updated their advanced facial recognition system (FRS) this year to address robbery and burglary cases. By late February, reports indicated its effective use, with notable successes including the solving of an ₹80-lakh robbery in Chandni Chowk within 24 hours, attributed to Israeli/Corsight technology. The system analyses CCTV footage and links it to existing databases, significantly expediting investigations and resulting in arrests and the recovery of stolen goods.

However, criticisms emerged regarding privacy concerns, wrongful arrests and the potential for bias, highlighted by a Pulitzer Center report from July.

2. Maharashtra

Maharashtra CID’s AI-based biometric data collection unit in Pune is part of the state’s larger Maharashtra Research and Vigilance for Enhanced Law Enforcement (MARVEL) initiative, which aims to modernise policing through analytics and digital evidence. Launched at Pune Rural Police headquarters, the unit captures multi-angle facial photographs, fingerprints and iris scans of accused individuals. The AI system standardises data and links it to the Crime and Criminal Tracking Network and Systems (CCTNS), enabling the police to track suspects across districts and recognise repeat offenders even if they alter their appearance.

MARVEL’s integration promise is ambitious, but its success depends on accuracy, governance, and transparency, not just technology. As of now, the system’s benefits are mostly projected, while debates over privacy and oversight remain unresolved.

3. West Bengal

West Bengal Police’s AI legal-assistant bot, introduced in December, is designed to reduce procedural errors in investigations and case files. Rolled out to roughly 400 investigators across eight police units, the tool was developed jointly by Birbhum Police and a Pune-based firm. It contains over 50,000 pages of legal material, including case law, NHRC and MHA guidelines, training manuals and victim-support procedures. Investigators enter a brief case description, and the bot recommends relevant IPC/BNS sections, correct procedures and documentation requirements.

The project’s main aim is to cut “legal slips” that can weaken charges or lead to acquittals, offering timely legal references to younger or overstretched officers. However, senior officials caution against over-reliance: AI cannot replace field judgement. Critics also highlight opacity, potential bias in the training data and unclear accountability if wrongful actions result from AI suggestions.

4. Uttar Pradesh

Uttar Pradesh Police and IIT-Kanpur’s RAG-based chatbot, unveiled in mid-2025, aims to make police procedures clearer for both officers and citizens. It indexes more than 1,000 Hindi police circulars, guidelines, and departmental instructions. Users can type everyday questions, such as how to report a vehicle theft, steps in passport verification or election-duty protocols, and the chatbot provides procedural answers in plain language.

For police personnel, this reduces dependence on senior officers or manual document searches, helping avoid procedural delays and misinformation at stations. For citizens, it counters the typical “come tomorrow” response by showing official rules directly. However, outdated or misinterpreted answers could mislead complainants, and query logs may expose sensitive personal information if data retention and privacy safeguards are not clearly defined.

5. Odisha

Odisha Police’s AI-enabled Integrated Command and Control Centre (CCC) in Puri was heavily deployed for the 2025 Rath Yatra to assist with crowd management using AI-processed CCTV feeds, drone footage and density analytics. The system was supposed to detect choke points, alert officials in real time and coordinate warnings via LED panels. However, during the June 29 stampede, which killed three people and injured several others, the technology failed to deliver actionable alerts.

Investigations found that only 123 of 275 cameras were functional, feeds were inconsistent and drones were under-utilised. Authorities recommended blacklisting the vendor and disciplinary action was initiated against seven senior officers for negligence.

6. Telangana

Hyderabad police expanded AI use in November across surveillance, crime analysis and cyber investigations. The city leverages AI-enhanced CCTV systems, facial recognition tools and video analytics to identify suspects, missing persons and risky behaviours in crowded spaces. Thousands of cameras feed data into command centres, where algorithms flag anomalies and help trace movement patterns.
Alongside this, police publicly discussed AI-driven tools for cybercrime, including blockchain analysis and social media monitoring to detect fraud networks and online threats. These systems could provide faster case turnaround and improved tracking in dense urban areas. As deployment scales, the city risks trading public safety for privacy, transparency, algorithmic bias and democratic accountability.

Country-Wide

India proposed the use of AI facial recognition at major railway stations, which emerged in mid-2025, when the Union government informed the Supreme Court that it planned to deploy FRS at seven high-traffic stations to monitor convicted and repeat sex offenders. The system would scan live CCTV feeds, compare faces against law-enforcement databases and alert authorities if a match appears.

However, the plan effectively creates mass surveillance zones that scan millions of passengers without their consent. It is worth noting that railway CCTV often has poor angles, lighting and image quality, which increases the risk of misidentification. It can also be argued that India lacks dedicated legislation governing facial recognition, including use, retention and appeal rights, raising questions about proportionality, privacy and accountability before deployment.

The post 6 Indian States Integrating AI Into Everyday Policing appeared first on Analytics India Magazine.

Despite AI transforming high-volume tech hiring, India’s senior IT roles remain sealed inside private networks and trust loops.

Sindhuja Maddela, founder of Plarty, a culture-tech platform that focuses on bringing people together through shared Indian experiences, realised how opaque senior hiring truly is when a ₹96-lakh CXO role disappeared before it ever went public.

According to Maddela, who said she was privy to the process, there was no LinkedIn post, no application pipeline, no screening, just a founder sending a casual message to someone he trusted: “Hey, I need a CXO. Any names?”

Within a day the conversations began; the following day the offer was issued.

“People like to believe senior hiring is structured. It isn’t,” she said. “At that level, nobody wants to take risks. Leaders pick people they already know, or someone a trusted voice vouches for.”

Maddela acknowledges that she has long benefitted from this quiet hiring circuit, but the ease came with a hidden downside.

“Over the years, getting roles through references became a disadvantage, because you stop preparing for interviews or updating yourself. When you finally have to apply without a referral, you feel stuck and the imposter syndrome hits,” she told AIM.

The comfort of referrals, she added, also narrowed her path. “I never went through rigorous FAANG-style interviews or tried for something bigger because I kept slipping into roles too easily.”

She believes companies need a deliberate counterbalance. “References give quick access, but others deserve a fair shot. Businesses should maintain a quota for open recruitment so new people and new ideas actually enter the system.”

Her experience reflects the broader patterns India’s largest talent platforms are now observing across the industry.

Academic research has long shown that referral-driven hiring is not just common but structurally influential in labour markets.

One study, The Role of Referrals in Immobility, Inequality, and Inefficiency in Labor Markets, argues that when companies rely heavily on referrals, it can limit mobility, reinforce inequality and create inefficiencies in how talent is matched to roles.

Another paper, Social Networks as a Mechanism for Discrimination, demonstrates how hiring through personal networks can inadvertently disadvantage certain groups, showing that network-based recruitment can shape access to opportunity as much as skill, or experience.
Clubbed together, these studies offer a useful lens for understanding India’s tech sector, where AI is reshaping hiring at the bottom even as networks continue to dominate the top.

LinkedIn’s global survey shows that nearly 80% of professionals believe networking is critical for career success, and 70% of people hired in 2016 landed roles at companies where they already had a connection.

In India’s IT sector, this pattern is as pronounced. AI now drives much of the junior and mid-level screening, but senior mandates, from CXO roles to practice heads, continue to move largely through private networks, trusted referrals and closed-door conversations.

This widening divide between tech-driven hiring at the bottom and trust-driven hiring at the top is reshaping who gets seen, who gets considered and how leadership pipelines are formed.

Aditya Narayan Mishra, MD and CEO of CIEL HR, said AI has transformed hiring at the base and mid layers, where volumes are high and skill maps are predictable, but has barely moved the needle on senior recruitment.

“AI helps screen faster, match profiles better and maintain consistency. But leadership hiring is far more layered,” he said.

Strategic thinking, maturity, cultural alignment and the ability to lead complex teams are all qualities AI still cannot evaluate with the nuance that clients demand. As a result, senior mandates still rely on “experience, trust and established networks,” he said.

Interestingly, Mishra noted that AI is beginning to add value by surfacing senior candidates outside the usual inner circles, leaders who built practices in mid-sized firms, or scaled emerging roles.

He said that hiring for senior roles in Indian IT can be relationship driven. Sometimes that narrows the view. AI-enabled tools help widen it, he added.

Yet, he acknowledged that time pressure pushes organisations back toward low-risk, known profiles.

For AI to play a material role in senior hiring, companies must build trust in how insights are generated and adopt a mindset that blends data with human judgment.

Kartik Narayan, CEO–jobs marketplace at Apna, argued that the gap between AI’s actual capabilities and its adoption in senior hiring is larger than most realise.

“AI has moved faster than our intuition,” he said. Apna’s AI calling agent already conducts structured conversations in English and Hindi, produces transcripts and evaluates problem solving and clarity at a scale impossible for human panels. More than 3,000 employers now use it in early screening.

Narayan believes the barrier in senior hiring is not capability but comfort. “For seasoned developers, architects and technical leads, AI can already assess functional rigour and decision making with confidence. Companies simply want more human engagement later because conversations shift from skill to context.”

Leadership hiring, he said, involves assessing how someone carries culture, influences people and navigates ambiguity, qualities that require long, unstructured conversations.

Still, he sees AI as a powerful equaliser earlier in careers. “When the first look is an AI conversation, candidates are evaluated on skill, not pedigree or proximity.”

Over time, AI-driven skill graphs could help identify leadership potential earlier, reducing dependence on closed networks.

The dominance of networks at the top is something staffing firms openly acknowledge.

Neeti Sharma, CEO of TeamLease Digital, said the pattern is unmistakable. “Senior roles are rarely published in the open market. They move through known networks,” she said, adding that over two-thirds of roles such as delivery heads, practice leads and vertical heads are filled through internal or referral candidates.

Companies often post senior openings publicly “more for signalling,” she said, even when the real hire is made through internal candidates, references or search firms.

Sharma explained that business urgency reinforces this behaviour. “A known candidate ramps up faster, and for critical openings that matters.” Referrals confer an advantage because cultural fit and working style are already known.

But, she believes fairness is still possible if companies assess external candidates alongside internal ones, maintain transparent evaluation stages, and use unbiased panels.

The post Why AI Stops at the Door of Senior Hiring in Indian IT appeared first on Analytics India Magazine.