by Csongor Fekete | Oct 17, 2025 | AI, Business, Machine Learning
China's AI landscape, once driven by government support and fierce internal competition, now faces challenges that reflect a broader phenomenon called “involution.” According to Bloomberg, this term defines an environment of excessive internal intensity that leads to diminishing returns—an unsustainable race that puts output and innovation at risk despite massive input.
Key companies like Baidu and SenseTime are experiencing setbacks in global expansion, struggling with monetization of large language models (LLMs) and facing regulatory scrutiny abroad. The domestic market, meanwhile, shows signs of saturation and overengineering, further complicating profitability.
The article outlines several critical learnings:
- Intense domestic rivalry can stifle long-term growth if not aligned with global standards.
- Custom AI models need clear commercial pathways to be sustainable.
- Strategic alliances and niche export opportunities may offer more value than broad-scale LLM rollouts.
- The success of AI technology must be measured by real-world business impact, not just volume of model deployment.
A tangible business use-case emerging from this insight is in developing custom AI models tuned for specific industries like retail, real estate, or wellness—domains where customer data is abundant but underutilized. A company like HolistiCrm, with its holistic approach to martech and AI consultancy, can capture significant value by crafting tailored AI tools that improve marketing performance, predict buyer intent, and boost customer satisfaction.
Instead of replicating China’s large-scale AI investments, the path forward lies in deploying precision Machine Learning models that solve local business pain points. This focused strategy can outpace generalized models in cost-effectiveness and relevance, creating long-term competitive advantage.
original article: https://news.google.com/rss/articles/CBMiowFBVV95cUxPQ2pBbG1BTEhPVFJzbEhVc0FtRFZ6YmdzYkRfT3NabGI3REpEaVFscVFiR05TVDV6Qk9TYUZ6X0NSTWc2dlNkV2tfYk5Wd1hPLVBoVENoQWNXYmgzQjdkaV81OV9WWXNpdGtqeTlnRVo4Q3lMZ2lfUEVoRjFxem9jZUJTX05rQkY1VXAtV3N4cFVkR2RiWE5tQmcwVXdfdVpJclpB?oc=5
by Csongor Fekete | Oct 16, 2025 | AI, Business, Machine Learning
Google’s latest move to bring its powerful AI image editing model, dubbed the "Photoshop-killer," to Search, Google Photos, and NotebookLM signifies a pivotal shift in accessibility and utility of generative AI. The model, known as Imagen 2, enables advanced image manipulation such as realistic inpainting, object removal, background modification, and context-aware transformations—all from plain language prompts.
The rollout of Imagen 2 to widely used platforms like Google Photos and NotebookLM reflects a key trend: embedding advanced AI into everyday productivity tools to enhance user experience, creative control, and efficiency. The implications for martech and marketing teams are significant. Imagine product managers or designers being able to generate branded content on-the-fly, remove distractions from campaign imagery, or tailor visual content to different demographics—all without requiring a dedicated creative team.
From a business value perspective, organizations leveraging custom AI models like Imagen 2 for visual content can dramatically reduce turnaround time and production costs. A potential use-case is retail personalization: e-commerce platforms could automatically customize product thumbnails or promotional banners based on localized preferences or seasonal trends, increasing click-through rates and customer satisfaction. Integrating such capabilities into a holistic martech stack propels performance, streamlining content creation and customer engagement efforts.
Brands seeking to enrich customer connections and marketing efficiency stand to benefit by engaging an AI consultancy or AI agency that tailors machine learning models to specific use-cases—be it content automation, visual search enhancement, or personalized media creation.
Original article: https://news.google.com/rss/articles/CBMiuAFBVV95cUxNR2lGTUdTZUdXOTRpUDAtU1l2RzVjT1lKdGhQTFRVekhBQjVpR2s2T09ST2Uydk45ZEgybG5MMlNGcTQ0dTZsLUNJUXpBbzRhMUp6UGNjaW1hYmZSeWNSSC1mWVNTU0FBeHkxU0hLVTFFQklGQTRlZ1JaM2ViOXdpT3NPYWNqRWUxUXRvVGZHS2l3bTlTMlVfdG4xOGxkaEdUUFhVWE1IamxUbW5PcEpBblVHM2dLSXdJ?oc=5
by Csongor Fekete | Oct 16, 2025 | AI, Business, Machine Learning
AI models are helping prevent child malnutrition – and that has big implications for business, too.
A recent initiative in Kenya, led by Gavi in collaboration with IBM’s Data Science and AI Elite team and Action Against Hunger, demonstrates how Machine Learning models can be used not just to trace problems, but to anticipate them. A custom AI model was trained on diverse data sets such as rainfall, vegetation levels, staple food prices, and malnutrition screenings. This model can now forecast child malnutrition rates up to six months in advance, enabling healthcare and government responses before situations become critical.
This predictive approach has proven to be over 80% accurate—significantly enhancing response efficiency and ultimately driving better outcomes for vulnerable populations. The model is also holistic, combining environmental, economic, and health data streams to provide actionable foresight.
For businesses in the martech or customer-centric sectors, there are clear parallels. A similar predictive model, customized for marketing or sales environments, could anticipate when a customer may churn, when campaign fatigue may set in, or when a product may fall out of favor—months before it happens. Custom AI models that incorporate customer engagement data, external market signals, and buying behavior can empower teams to take preemptive action, increasing satisfaction and retention.
AI consultancies like HolistiCrm can apply such methodologies across business domains to enhance predictive precision, operational performance, and strategic agility. Investing in advanced Machine Learning models is not solely the realm of public health—it’s an essential strategy for any customer-facing business aiming for long-term stability and value.
original article: https://news.google.com/rss/articles/CBMirgFBVV95cUxONXAzbFlkSmJ1VzhnbTZrMmk5Mkh1Q2Qwa0hyZnBwR3l3RHFtdTFPRzA3NXViOFlYZkM2NkpISkdwMWRzaVFmMk1UcjR6RFlOQ0laSXVhR1F1OGVlMlNHbXhOXzRGVVp0Q0FZSDRxTk1paGU3bXVjdzA1TmI3SlJ5Ymc4VjJCT3Z2aDQ5NGhhOGJfcVZxVlFKVEVCa2lUeUJWOE10djRibEZ0MkR3Ymc?oc=5
by Csongor Fekete | Oct 15, 2025 | AI, Business, Machine Learning
Ant Group, the Chinese fintech powerhouse behind Alipay, has launched a large language model (LLM) known as Bailing, entering the competitive field alongside DeepSeek and OpenAI. The new LLM comes in two main versions—Bailing Expert and Bailing General—with the Expert version specifically optimized for the finance sector. This marks a strategic move to integrate custom AI models into financial services, focusing on enhanced performance, security, and domain-specific precision.
Key developments driven by the Bailing model include:
- Deployment of over 600 LLM-based use cases by Ant Group.
- Integration across digital financial services ranging from intelligent customer service to fraud detection.
- Emphasis on responsible AI deployment, with safety and compliance as top priorities.
- Strong performance benchmarks against other AI providers in both general NLP tasks and finance-specific applications.
The significance of Bailing's launch extends far beyond the Chinese market. For AI consultancies and martech players, this development illustrates the growing opportunity for industry-specific and use-case driven AI solutions. By leveraging custom AI models like Bailing Expert in a tailored manner—especially in finance, e-commerce, or customer service—enterprises can unlock new business value through automation, real-time decision intelligence, and sharper personalization strategies.
A concrete use-case enabled by HolistiCrm could involve integrating a finance-optimized Machine Learning model like Bailing Expert into a CRM platform to deliver predictive insights about high-value clients, increase customer satisfaction through intelligent support automation, or drive targeted marketing using a holistic understanding of user behavior and financial profile. Such applications drive direct ROI and reinforce the strategic role of AI-as-a-service in business transformation.
original article: https://news.google.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?oc=5
by Csongor Fekete | Oct 14, 2025 | AI, Business, Machine Learning
Samsung’s recent achievement in AI model development is poised to influence how businesses approach AI strategy and deployment. The company’s new lightweight model has outperformed large-scale models like Google’s Gemini 2.5 Pro on the ARC-AGI benchmark—an evaluation designed to assess general intelligence through complex reasoning puzzles.
Key learnings from the announcement center around efficiency. Samsung’s custom AI model used only 1.14 billion parameters, a fraction of the size of typical large language models (LLMs), yet delivered exceptional performance. This breakthrough illustrates that smaller, more specialized Machine Learning models can outperform massive models when purpose-built and fine-tuned for specific tasks.
For businesses navigating martech and customer satisfaction challenges, the success of Samsung's compact AI demonstrates that size isn’t everything. A Holistic approach—focusing on alignment with business goals rather than maximum model size—can yield high value in performance and cost-efficiency. Companies working with an AI agency or AI consultancy, such as HolistiCrm, can benefit by moving away from generic LLMs and investing in custom AI models optimized for domain-specific tasks like predictive marketing, churn analysis, or personalized content generation.
A use-case derived from Samsung's innovation could involve ecommerce platforms leveraging smaller, high-performance models to power intelligent product recommendations or customer service chatbots—delivering fast, relevant interactions with significantly reduced infrastructure costs. This shift not only enhances satisfaction but makes AI adoption more scalable and sustainable.
The implications for AI experts and digital transformation leaders are clear: Strategic model design now rivals model size in importance. Focusing on form factor, interpretability, and task-alignment can lead to smarter, more responsible AI adoption.
Read the original article here: Samsung’s Tiny AI Model Outperforms Huge LLMs Like Gemini 2.5 Pro On ARC-AGI Puzzles – Wccftech
by Csongor Fekete | Oct 14, 2025 | AI, Business, Machine Learning
Ant Group has raised the bar in large language model (LLM) innovation with the release of the Ling family of models, topped by the groundbreaking Ling-1T — a trillion-parameter Machine Learning model. This leap reflects a strategic focus on performance, efficiency, and the deployment of custom AI models tailored for real-world business scenarios.
Key takeaways from the announcement include:
- Ling-1T is designed as a general-purpose foundational model with one trillion parameters, optimized for both high performance and low inference cost.
- The Ling family has already been integrated into over 600 real-world applications, impacting sectors like finance, public services, and manufacturing.
- Distinctively, Ant Group’s LLMs emphasize interpretability and alignment, integrating cross-modal reasoning and cognitive abilities with domain-specific customization.
- The models are optimized for a cost-efficient training/inference ratio of only 1:1.4 compared to 2:1 seen in other large models, reflecting a focus on sustainability and ROI.
From a business lens, adopting such models can drive significant value in martech initiatives. For example, in customer engagement, a Holistic CRM application powered by custom AI models like Ling-1T could enable dynamic content generation, predictive personalization, and real-time customer sentiment analysis. These capabilities not only increase customer satisfaction but directly enhance marketing performance.
AI agencies and AI consultancies should note this trend as a signal: large-scale, intelligent models that support real-time, domain-aware, low-latency decision-making are becoming critical differentiators for forward-looking enterprises. HolistiCrm encourages businesses to align strategy and implementation with these technological advances to push marketing capabilities into a new era of intelligent automation.
Original article: Ant Group Unveils Ling AI Model Family and Launches Trillion-Parameter Language Model Ling-1T
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