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Stock Exchanges Broaden ‘Promoter’ Definition to Strengthen IPO Governance

Indian stock exchanges have introduced a significant policy change aimed at improving corporate governance and transparency during the initial public offering (IPO) process. The revised framework expands the definition of a “promoter” to include not just large shareholders, but also individuals or entities exerting significant influence over company decisions, regardless of equity ownership.

Under the new rules, founders, board members, key managerial personnel (KMPs), and even immediate relatives who have a notable role in decision-making could be classified as promoters. This broader scope also covers individuals who exercise control indirectly, such as through board voting rights or strategic veto powers.

Previously, the promoter tag was largely linked to shareholding thresholds, allowing influential figures with minimal equity to avoid public identification. By including these stakeholders, regulators aim to close loopholes that have, in the past, allowed opaque ownership structures to remain hidden from investors.

For companies preparing to go public, the change means conducting deeper internal audits to identify all qualifying promoters. Legal experts suggest that this will require careful documentation to prevent compliance issues or listing delays. In some cases, companies may need to update their corporate structures or disclosures well in advance of filing their draft red herring prospectus (DRHP).

Market analysts believe the move will benefit institutional investors, retail shareholders, and B2B partners by offering a more accurate view of who truly controls a business. This additional layer of clarity can help stakeholders assess the company’s governance culture and long-term stability before committing capital or entering into strategic partnerships.

The change also brings India closer to international norms, where disclosure obligations are designed to reflect real-world influence rather than just formal ownership. As the IPO market continues to expand, particularly in high-growth sectors, this increased transparency is expected to boost investor confidence and reduce the risk of post-listing governance disputes.

Overall, the broadened definition signals a shift toward a more rigorous and realistic understanding of corporate power structures. For investors, it is a welcome step toward ensuring that those with real influence are held to the same disclosure and accountability standards as majority owners.

Why Founders No Longer Need Developers to Build Tech Products

The journey from a great idea to a fully functional app was once marked by layers of code, complexity, and high costs. It required a dedicated team of developers, months of planning and execution, and a sizable budget to bring it all to life. But today, that narrative is changing, and we’re proud to be at the forefront of this transformation.

At a time when the startup world is moving faster than ever, building software can no longer be a bottleneck. We asked ourselves: What if anyone could describe their idea in simple English and have it live on the internet, which is fully functional, scalable, and beautiful, and that too within minutes?

Even in 2025, many founders, especially non-technical ones, still believe their only path to product is hiring developers or expensive agencies. It’s a mindset built on decades of software development history. But this approach has major downsides: long timelines, high costs, and countless iterations before you get something usable. 

We’re flipping the script by giving founders the power to build full-stack, production-ready applications without writing a single line of code. And this isn’t just another prototype builder or drag-and-drop MVP tool. We’re talking about fully deployed, real apps like e-commerce platforms, SaaS dashboards, marketplaces, booking tools and all launched in minutes, not months.

One early user built a fully operational online store in seven hours. Within the first week, it received over 1,200 orders. That’s not just speed, that’s market traction, achieved without a single developer on payroll.

According to Gartner, 70% of new applications created by enterprises will use low-code or no-code technologies by 2025, a nearly threefold increase from 2020.The global low-code market is expected to grow to $36.43 billion by 2027, with a fast growth rate of 26.1% each year. This shows how quickly more people are turning to these tools.

From solopreneurs in Mumbai to indie hackers in London to side hustlers in San Francisco, founders across the world are launching real businesses using our platform. What unites them isn’t a technical background; it’s vision, speed, and execution.

One thing we’re seeing across the board: our users are committed. Daily Discord conversations, GitHub integrations, feature testing, and high-frequency app iterations. By eliminating the need for technical expertise, we’re helping democratize innovation. Great ideas shouldn’t be locked behind the gatekeepers of code. Whether you’re a 21-year-old in Jaipur with a SaaS idea or a small business owner in Leeds looking to digitize your services, now you can build your product, test your market, and go live in less than 15 minutes.

We believe the next wave of startups will be built by thinkers, not just tinkerers. If the last decade was about technical founders building software companies, this one is about domain experts building companies without needing developers at all. Our goal is to empower millions of non-technical creators with superpowers. This is no longer a niche trend; it’s a new way of building, one where anyone with an idea can build a business.

About Launch-
Launch is an AI-native website and app builder designed to help anyone, from non-coders to developers, build full-stack, production-ready applications in under 15 minutes. With seamless backend integrations, no-hassle database management, and built-in support for tools like Stripe and Authentication, Launch eliminates technical roadblocks so you can focus on building what matters. 

MakeMyTrip Unveils Multilingual GenAI Travel Assistant to Revolutionize Trip Planning

In a significant leap toward intelligent and inclusive travel planning, MakeMyTrip has launched a generative AI-powered multilingual travel assistant, designed to simplify and personalize the travel experience for users across India. Currently in beta mode, the feature supports both English and Hindi, enabling seamless interactions for a wider user base.

This new AI tool, integrated into the MakeMyTrip platform, is built to offer natural language conversations, allowing users to plan their journeys as if they were speaking to a travel expert. From generating tailored recommendations and handling real-time itinerary changes to suggesting destination activities and lodging options, the assistant aims to transform how travelers engage with digital platforms.

At the core of the assistant is a Generative AI engine that processes user queries and preferences to provide intuitive suggestions based on budget, travel history, seasonality, and trending destinations. Whether it’s a family looking for a weekend getaway or a solo traveler planning a long escape, the tool can adapt to each unique need.

“Travel planning can be overwhelming, especially for those unfamiliar with booking platforms or those who prefer using regional languages. Our new assistant is designed to bridge that gap,” a MakeMyTrip spokesperson stated. “It’s not just smart—it’s conversational, contextual, and culturally aware.”

One of the most striking features of this launch is its support for bilingual interaction. By enabling users to communicate in both Hindi and English, MakeMyTrip is targeting a massive untapped demographic of non-English-speaking digital users who often face friction when using travel apps. This move aligns with the broader industry trend toward vernacular internet adoption, especially in Tier 2 and Tier 3 cities.

According to industry analysts, India’s internet user base is becoming increasingly diverse, with over 50% engaging in regional languages. By bringing in multilingual AI, MakeMyTrip is not just enhancing usability but also taking a first-mover advantage in the domestic travel-tech ecosystem.

Currently, the assistant is available in a beta version, with a controlled rollout to gather feedback, refine natural language understanding, and enhance the system’s responsiveness. Early users have reported higher satisfaction rates in booking flows and a reduction in planning time thanks to conversational input replacing multiple clicks and form fills.

MakeMyTrip has hinted that more languages may be added in the future, depending on user response and technological scalability. Moreover, future versions could include voice-enabled interaction, hyper-local suggestions, and integration with loyalty programs.

The launch underscores the increasing influence of AI in travel and hospitality, with companies racing to embed intelligence into every touchpoint—from bookings and check-ins to post-trip recommendations. As Indian travelers become more digital-first, tools like this offer not just convenience but also a sense of personalization that today’s users have come to expect.

With this innovation, MakeMyTrip isn’t just helping people reach destinations—it’s reimagining how they get there.

AI Takes the Lead in Transforming India’s FinTech Landscape

India’s FinTech sector is witnessing a major transformation, one being driven not by new regulations or macroeconomic shifts, but by Artificial Intelligence (AI) and Generative AI (GenAI). Once seen as experimental technologies, AI is now at the core of strategic roadmaps across financial services in the country. According to Mordor Intelligence, India’s FinTech market is projected to hit a staggering USD 421.48 billion by 2029, a forecast that underlines the power of AI to unlock exponential growth.

As of 2025, over 90% of Indian financial institutions have named AI as their top innovation priority, a significant shift from even just a few years ago. Whether it’s fraud detection, credit scoring, customer service, or personalized financial products, AI is not just supporting—but driving—key business functions.

Traditionally, AI in finance was treated as a cost-saving measure—automating back-office processes or reducing human error. But the paradigm is changing. AI is now seen as a growth enabler, capable of delivering unique customer experiences and predictive capabilities that were previously unimaginable.

For instance, digital lending platforms are using AI algorithms to assess risk in real-time, even for first-time borrowers who lack formal credit histories. InsurTech companies are deploying GenAI to generate policy documents, simulate risk scenarios, and offer hyper-personalized coverage. Investment platforms are rolling out AI-powered advisors that can automatically rebalance portfolios and optimize tax strategies.

What has captured industry attention is the emergence of Generative AI. By enabling machines to generate human-like content—text, speech, visuals—GenAI is revolutionizing how banks and FinTech startups interact with customers.

Chatbots and virtual assistants powered by GenAI are already answering complex queries in multiple Indian languages, reducing dependency on large customer support teams. Some startups are also exploring GenAI-generated financial literacy content tailored to local contexts, bringing new-to-tech users into the fold with ease.

Several factors are converging to accelerate AI adoption:

  • Availability of structured and unstructured financial data from India’s booming digital economy.

  • Regulatory encouragement for innovation sandboxes and digital KYC frameworks.

  • The falling cost of computing and cloud infrastructure, making experimentation cheaper.

  • Global investor interest in AI-first FinTech startups, increasing with risk appetite.

These drivers are encouraging both incumbents and disruptors to invest heavily in AI R&D and product integration.

While the promise is enormous, experts warn against over-reliance on AI without proper guardrails. Issues such as algorithmic bias, data privacy, and explainability are growing concerns, especially in high-stakes sectors like lending and insurance.

However, if balanced well, the opportunities outweigh the risks. The FinTech sector, with its agility and tech-first mindset, is uniquely positioned to set global benchmarks in ethical, scalable AI deployment.

India’s FinTech sector isn’t just embracing AI—it’s being reshaped by it. As companies increasingly move from pilots to production-scale deployments, AI is transitioning from a technological novelty to a strategic imperative. The next five years could define whether India becomes not just a leader in FinTech, but a pioneer in AI-powered finance for the world.

IIM Calcutta Innovation Park Champions AI for Inclusion and Impact

At a recent gathering of entrepreneurs, technologists, and investors, IIM Calcutta Innovation Park (IIMCIP) reinforced a compelling vision for the future of artificial intelligence in India—one that is inclusive, affordable, and monetizable. The event, held in collaboration with ecosystem enablers and technology partners, particularly emphasized the need for AI solutions that address real-world challenges for underserved communities.

Rather than focusing exclusively on high-end enterprise AI, the sessions spotlighted how India can lead in creating scalable and sustainable AI products that deliver measurable social and economic impact. In a country as diverse and dynamic as India, the need to make AI more accessible isn’t just a moral imperative—it’s a business opportunity.

The key theme running through the discussions was how startups can bridge the AI divide between India’s urban elite and its rural heartlands. Speakers underscored that true AI innovation in India must go beyond buzzwords and trend-chasing. It should cater to sectors like agriculture, local commerce, rural healthcare, MSMEs, and education, where intelligent automation and insights can unlock transformative change.

Participants were encouraged to think from first principles: What does AI look like for a smallholder farmer in Bihar? How can a kirana store owner use generative AI without knowing English? What if a health worker in a remote district could use voice-based AI to detect early disease symptoms?

One of the event’s highlights was a practical, workshop-style session powered by Google’s GenAI stack. Here, early-stage startup teams had the opportunity to work directly with Google engineers to prototype AI-first solutions tailored to Indian use cases. The tools allowed entrepreneurs to build conversational interfaces, multilingual AI assistants, and predictive tools with minimal coding—lowering barriers to entry for tech founders without deep AI backgrounds.

These workshops didn’t just stop at tools—they were paired with mentoring sessions by VCs, angel investors, and senior technologists, offering participants feedback on how to craft business models that are both impactful and revenue-generating.

Another focal point of the event was responsible AI development. Experts stressed that in India’s context, ethical AI is not just about privacy and bias mitigation but also about designing for low-resource environments. This means building models that are lightweight, multilingual, and contextually aware, ensuring they can function on basic smartphones and inconsistent internet connections.

From a policy standpoint, panelists called for more government–academia–industry collaboration to fund and scale such AI-first ventures. India, they said, must avoid the trap of replicating Western AI narratives and instead forge its own path centered around population-scale innovation.

The event wrapped up with a call to action: Build AI for India—not just for the metros, but for the millions still digitally underserved. With IIMCIP’s support, access to cutting-edge tools, and early-stage capital flowing in, the foundation is being laid for a new generation of AI entrepreneurs who prioritize purpose alongside profit.

As India stands on the cusp of its next tech revolution, inclusive AI may just be its most valuable export.

Cinefai Reimagines the Ramayana with Generative AI Brilliance

In a bold fusion of tradition and technology, Mumbai-based studio Cinefai has unveiled an AI-powered adaptation of the Ramayana, capturing the imagination of audiences across the country. Leveraging the creative capabilities of generative AI, the studio has breathed new life into one of India’s most revered epics—presenting it in a format that feels both timeless and futuristic.

The trailer and first episode, released earlier this week, offer a visually rich, immersive re-telling of the ancient story—complete with AI-generated landscapes, lifelike characters, and cinematic effects that rival high-end fantasy productions. The launch marks a milestone not only in Indian entertainment but also in the evolving relationship between AI and storytelling.

While the Ramayana has been retold countless times through books, television, animation, and theater, Cinefai’s approach is groundbreaking in its execution. Rather than relying solely on human animators or traditional VFX, the studio employed generative AI tools trained on vast datasets of visual styles, classical Indian art, and motion sequences. The result is a stylized world that feels deeply rooted in mythology while pushing creative boundaries.

Each frame is meticulously designed with the help of AI models that interpret narrative prompts and generate corresponding visual sequences. According to Cinefai’s creative director, this approach allowed them to achieve a scale and speed of production that would have been near impossible using conventional techniques alone.

One of the most compelling aspects of the project is its commitment to authenticity, even as it embraces modern technology. The screenplay stays true to Valmiki’s original text while incorporating subtle narrative enhancements that appeal to contemporary viewers. Dialogues are rendered in Sanskrit-inspired Hindi, supported by subtitles in multiple Indian languages—ensuring accessibility without sacrificing cultural depth.

The musical score, composed with the assistance of AI audio tools, draws inspiration from traditional Indian instruments, creating an auditory landscape that complements the visual grandeur.

“This isn’t about replacing human creativity,” said Cinefai’s founder during the press conference. “It’s about amplifying it. With AI, we’re able to explore creative territories that were once out of reach—while still honoring the essence of our cultural treasures.”

The Indian film and OTT industry has taken note of Cinefai’s bold experiment. Critics and creators alike are hailing the project as a blueprint for the future of mythological content—especially in an era where audience expectations are driven by high-end streaming shows and cinematic universes.

Streaming platforms are reportedly in talks with Cinefai for broader distribution, while the studio plans to release new episodes weekly, covering the entire epic in a visually serialized format.

Cinefai’s Ramayana stands as a powerful example of what happens when art, technology, and heritage converge. It’s a call to Indian creators to rethink how stories are told, preserved, and passed on to new generations.

As AI continues to shape the future of content creation, Cinefai has not just reimagined a beloved epic—they’ve opened the door to a whole new way of experiencing it.

India Tops GenAI Course Enrollments, But Lags Behind in Real-World AI Skills

India has recorded the highest number of enrollments globally in Generative AI (GenAI) courses, according to the Coursera Global Skills Report 2025. With a dramatic 107% year-over-year increase and more than 2.6 million enrollments, the numbers underscore an unprecedented interest in AI education across the country. However, the same report reveals a sobering contrast—India ranks 89th globally in overall skills proficiency, exposing a persistent gap between interest and implementation.

This paradox presents a critical challenge: how can a nation leading in AI education engagement continue to trail in the practical capabilities required for real-world AI adoption?

The enthusiasm around GenAI courses in India can be attributed to several key trends. The rise of user-friendly tools like ChatGPT, DALL·E, and GitHub Copilot, combined with aggressive digitization initiatives, has created an ecosystem where AI literacy is both aspirational and economically strategic. Edtech platforms such as Coursera, Udemy, and upGrad have reported record sign-ups from Indian learners eager to tap into the future of work.

“From college students to mid-career professionals, there’s a surge of curiosity around GenAI,” said an industry analyst. “The narrative around AI being a job displacer has shifted. People now see it as a tool to stay relevant.”

Courses related to prompt engineering, natural language processing, generative modeling, and AI ethics are in high demand, especially in Tier 2 and Tier 3 cities.

Despite the enrollment boom, the report’s ranking of India at 89th out of 100+ countries in AI and digital skills proficiency highlights a disconnect. Many learners are consuming content but falling short in applying that knowledge effectively. The gap is particularly visible in project-based assessments, collaborative coding environments, and AI deployment practices.

Experts believe this issue stems from an overemphasis on theoretical content and a lack of structured hands-on experience. While online learning has widened access, it hasn’t consistently delivered the depth and rigor needed to produce industry-ready professionals.

Indian companies across sectors—from BFSI to healthcare and e-commerce—are investing in GenAI integration. Yet, many hiring managers report difficulties in sourcing candidates who are both technically sound and application-ready.

“In interviews, we see candidates who’ve completed multiple AI courses but can’t design a working prototype,” shared a senior HR executive at a tech startup. “It’s not just about knowledge anymore—it’s about demonstrable skill.”

To close the skill gap, experts suggest a focus on experiential learning models. Universities and training platforms are being encouraged to incorporate capstone projects, AI labs, internships, and real-world case studies into their offerings.

Additionally, collaborative initiatives between academia, industry, and government can drive the creation of more outcome-oriented programs, particularly in regional languages to ensure inclusivity.

India’s explosive growth in GenAI course enrollment marks a strong step toward AI democratization. However, to turn this learning enthusiasm into true workforce capability, the focus must shift from just learning AI to doing AI. Only then can India fully realize its potential as a global leader in AI innovation.

Geoffrey Hinton Warns: Advanced AI May Be Creating Its Own Language Beyond Human Understanding

Artificial intelligence pioneer Geoffrey Hinton, often dubbed the “Godfather of AI,” has issued a stark warning: as AI systems grow in complexity and autonomy, they may be developing internal languages that are incomprehensible to humans—and that could pose serious risks to safety and oversight.

Speaking during a recent symposium on AI interpretability, Hinton highlighted one of the most pressing concerns in modern AI research: the emergence of self-organized communication systems within advanced models—languages that are not programmed, not shared with developers, and potentially undecipherable by humans.

At the heart of Hinton’s concern is the idea that large-scale AI models—particularly multi-agent systems and autonomous frameworks—might begin to develop their own shorthand, symbols, or internal representations to communicate faster and more efficiently with each other.

While this phenomenon has been observed in limited research environments before, the fear is that at larger scales, and with more freedom to learn and evolve, AI could invent communication systems or “languages” that bypass human oversight, leading to unpredictable outcomes.

“Once these systems start optimizing in ways we don’t fully understand, and talking in ways we can’t decode, control becomes a very real challenge,” Hinton said. “We may still get outputs we asked for—but we won’t truly know how or why they were produced.”

Modern neural networks already operate like black boxes to a large extent—producing accurate outputs without fully transparent logic. But the idea of an emergent language adds another layer of opacity. If AI systems begin interacting with one another in novel ways, it could make it nearly impossible to audit their behavior or trace the origins of certain decisions.

Researchers have already seen early glimpses of this. In cooperative learning environments, AI agents have been observed developing efficient, compressed communication protocols—unintelligible to humans—when tasked with collaborative problem-solving.

Although such behaviors have been largely benign and controlled, Hinton’s warning suggests that future AI systems with broader autonomy and real-world decision-making powers could escalate this behavior to dangerous levels.

If humanity can no longer understand how an AI system reaches its decisions, then trust, accountability, and alignment with human values become deeply compromised. This unpredictability could have consequences in sectors like defense, finance, or healthcare—where even a small misunderstanding or misinterpretation could cause significant harm.

Hinton urged the AI community to invest heavily in interpretability research, a field dedicated to making AI more understandable and transparent. He also called on policymakers and companies to mandate safeguards that ensure human-in-the-loop oversight, particularly for autonomous systems deployed in sensitive areas.

While Hinton remains an optimist about the potential of AI, his concerns serve as a critical reminder that powerful technology must come with equally powerful mechanisms of control. As AI systems evolve rapidly, ensuring they remain comprehensible and aligned with human intent may be one of the greatest technical—and ethical—challenges of our time.

Goldman Sachs Economist Warns: Gen Z Tech Workers Could Be First to Lose Jobs to AI

As the world adapts to the accelerating capabilities of artificial intelligence, a new warning from Goldman Sachs economist Joseph Briggs has sparked concern across the tech sector—especially among its youngest members. In a recent analysis, Briggs highlighted that Generation Z tech workers—many of whom are just entering the workforce—could be the first wave of professionals to see their roles significantly impacted or replaced by AI-driven automation.

Briggs emphasized that entry-level positions in the technology industry, long considered a stepping stone into software engineering, data analysis, and IT roles, are now under direct threat as AI becomes capable of performing many of the simpler, repetitive, or rule-based tasks that typically fall to junior employees.

For decades, new tech hires began with tasks like writing boilerplate code, debugging, managing tickets, or performing routine data cleaning. These jobs allowed young professionals to learn by doing, gaining the experience needed to grow into more senior, strategic roles. But AI is changing that trajectory.

Tools powered by large language models (LLMs) and generative AI systems are increasingly able to write and optimize code, auto-generate documentation, triage IT issues, and even create initial product mockups. What once took hours for a human now takes seconds for an AI tool—at near-zero marginal cost.

“The automation wave is not coming—it’s already here,” Briggs noted. “And those performing the most easily automated tasks—often the most junior employees—are naturally the most vulnerable.”

For Gen Z, many of whom entered or are about to enter the workforce during a time of rapid technological disruption, the concern is both practical and psychological. University students studying computer science, software engineering, or tech-adjacent disciplines now face a job market where the bottom rungs of the career ladder are shrinking or disappearing altogether.

Some startups and even large enterprises have reportedly reduced junior hiring in favor of integrating AI copilots and code assistants to support their senior staff. This risks concentrating skill development and opportunity among those who are already well-established, potentially widening the experience gap.

Despite the warning, experts caution against adopting a purely negative outlook. While AI is displacing some roles, it’s also creating new categories of work, especially in AI operations, prompt engineering, model evaluation, and responsible AI oversight.

“Gen Z may be the most digitally native generation yet,” said one technology consultant. “They have a unique opportunity to adapt, retrain, and leverage AI not as a threat—but as a tool.”

Some companies are already responding by reshaping their entry-level programs, offering training focused on how to work with AI, rather than against it.

The message is clear: adaptation is key. As AI continues to evolve, so too must the education systems, hiring practices, and skill-building pipelines that support early-career talent.

Whether Gen Z tech workers emerge as the first victims of AI or its most agile beneficiaries will depend on how institutions and individuals respond to this shift—starting now.

U.S. Launches ATOM Project to Reassert Global Leadership in Open-Source AI

In a bold move to reclaim its edge in the global AI race, the United States has officially launched the ATOM Project—short for “American Truly Open Models”—bringing together a coalition of leading research institutions, tech companies, and AI labs. Backed by industry giants like OpenAI, Hugging Face, Stanford University, and Nvidia, the initiative is the most significant government-supported push yet to promote open-source AI development on American soil.

The ATOM Project arrives at a time when China’s state-supported open-source AI models are rapidly gaining traction and adoption across Asia, Africa, and parts of Europe. With ATOM, the U.S. aims to counterbalance this momentum and reaffirm its position as a global leader in foundational AI research and deployment.

What makes ATOM unique is the scale and diversity of its backing. The project has pooled over 10,000 GPUs and a budget exceeding $100 million, combining government funding with private-sector infrastructure. The goal? To produce high-performing, openly licensed AI models that can match or exceed the capabilities of proprietary systems—without the restrictions of closed ecosystems.

The models will be trained across a variety of modalities, including natural language, vision, and code, and will prioritize transparency, reproducibility, and safety. These models will be available for public use, aimed at supporting startups, researchers, and developers around the world.

“Open-source AI is not just a technical strategy—it’s a geopolitical necessity,” said an ATOM spokesperson. “We need American values of openness, safety, and trust to be baked into the foundation of future AI systems.”

The strategic importance of AI leadership is no longer up for debate. Beyond powering innovation in healthcare, finance, and logistics, AI is increasingly seen as a critical infrastructure for national security and economic competitiveness.

China’s open-source initiatives, often heavily subsidized and optimized for local languages and use cases, have started outpacing Western offerings in terms of deployment speed and community adoption. ATOM is designed to push back against this trend by creating a robust, globally influential alternative.

By focusing on openness, the U.S. aims to not only attract global contributors but also establish ethical and security standards that reflect democratic values.

While the project is American-led, its benefits are intended to be global. ATOM plans to host annual open challenges, model evaluations, and developer grants to encourage adoption and iteration beyond U.S. borders.

Hugging Face CEO Clement Delangue noted, “The strength of open-source lies in collaboration. ATOM is not about walls—it’s about bridges.”

The first models from ATOM are expected to be released in early 2026, with a roadmap focused on multilingual support, explainability, and robust fine-tuning capabilities. If successful, the project could redefine how nations view AI sovereignty, collaboration, and openness.

In a world where AI development is both a competitive arena and a shared global challenge, the ATOM Project stands as a declaration: open innovation is America’s answer.

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