by Csongor Fekete | Aug 22, 2025 | AI, Business, Machine Learning
The latest piece from The Wall Street Journal, “Gamblers Now Bet on AI Models Like Racehorses,” dives deep into a surprising new subculture where individual AI models are treated as competitive entities, staked on like thoroughbreds. Algorithmic “bettors” are now selecting specific models based on expected performance, tracking them across tasks, and assigning monetary value to their predictive success. This signals a shift in how people are evaluating and interacting with artificial intelligence—moving from generalized tools to individual AI personalities with distinct reputations.
Key takeaways from the article include:
- AI model “championships” are emerging, with performance scores and leaderboards
- Investors and enthusiasts view these custom AI models as assets capable of delivering returns
- The focus is on nuanced differences in outcomes driven by highly specialized, fine-tuned algorithms
- Events resemble digital racetracks where model accuracy and adaptability determine winners
This trend highlights the evolving importance of fine-tuning AI engines for specific tasks. In the context of a business setting, this opens doors for building high-performance, custom AI models that cater holistically to industry-specific use cases. For example, in martech, precision-tuned Machine Learning models can radically improve customer satisfaction by enhancing content targeting, optimizing campaign performance, and personalizing engagement strategies.
HolistiCrm sees significant opportunity here. When companies treat AI models as strategic assets—aided by expert AI consultancy and model performance tracking—they can transform their data into competitive advantage. With the right AI agency support, a custom model can become not just a tool but a value-creating mechanism in business development, marketing, and CRM strategies.
Betting on the right model in the market isn’t unlike guiding the right AI model on behalf of a customer. The race is on, and performance now defines ROI more clearly than ever before.
Reference: original article
by Csongor Fekete | Aug 22, 2025 | AI, Business, Machine Learning
The recent article by Futurism highlights a crucial, if sobering, reality in the AI world: despite skyrocketing interest and record-setting investments, the AI industry remains far from profitability. The astronomical costs of developing, training, and maintaining large-scale models — particularly those underpinning general-purpose tools like ChatGPT — continue to outpace revenue. Infrastructure costs, such as compute power and storage, alongside fierce competition and limited monetization strategies, are placing immense financial pressure on even the leading players.
Key takeaways from the article include:
- AI giants are burning through billions to keep models operational with little financial return.
- Many AI tools, although impressive, are underutilized or fail to deliver tangible business outcomes.
- The lack of domain-specific implementation and ROI-driven strategies hinders profitability.
This context underscores the strategic value for businesses to shift focus towards Holistic AI applications: purpose-built, custom AI models that directly solve industry-specific problems. Instead of general intelligence, tailored Machine Learning models—developed with domain data and aligned with performance metrics—usher in measurable results. For example, in martech, a retail brand using a custom recommendation engine can boost conversions and customer satisfaction by serving contextually relevant product suggestions in real time. This targeted AI approach drives marketing and operational efficiency without the overhead of maintaining massive, generalized models.
An AI consultancy or AI agency that delivers lightweight, high-impact applications designed with a business-first mindset can bridge the profitability gap the broader AI industry is struggling with. Businesses that prioritize performance, scalability, and pragmatic adoption of AI will carve out real value amid the hype.
original article: https://news.google.com/rss/articles/CBMiYkFVX3lxTFBteG9QQS03dXVjRXFxRHpxcTdEVklDX1pJUlVSeUpzUWVGOHJ3eHg0aEdMOEJVYWk1ZmhnYkcxbVVpSHlxNjhEQ2djbDZwM0dKTjVPQm5oQkViX21xSUd1OWhB?oc=5
by Csongor Fekete | Aug 21, 2025 | AI, Business, Machine Learning
Sequoia Capital's recent article, "The $1T Opportunity to Build the Next Amazon in Retail," outlines the massive disruption potential in consumer commerce. While Amazon has dominated the e-commerce landscape for over two decades, shifts in consumer behavior, mobile-first platforms, and hyper-personalized experiences signal a new phase in retail innovation.
The article highlights how traditional storefronts and digital incumbents are increasingly misaligned with how the next generation wants to discover, experience, and buy products. This opens the door for new entrants—especially those focused on speed, personalization, relationships, and community. The blend of social content, experiential commerce, and creator-driven platforms offers rich terrain for AI-driven innovation and martech transformation.
One of the key insights is that building a competitive edge in this new retail era will demand custom AI models that understand nuanced customer signals far beyond demographic data. Advanced predictive modeling, real-time recommendation engines, customer segmentation, and dynamic pricing are now essential tools—not just enhancements.
For businesses pursuing next-gen customer satisfaction and loyalty, integrating Machine Learning models to tailor marketing, inventory planning, and omni-channel experiences creates tangible performance gains. For example, a retail startup could deploy a custom AI model trained on customer interaction and sales data to recommend hyper-personalized product bundles. This boosts conversion rates and increases average order size—driving not only customer delight but also revenue performance across digital touchpoints.
In this $1 trillion opportunity, businesses that embrace a holistic, AI-first approach—partnering with an AI consultancy or AI expert to build proprietary martech infrastructure—have the chance to define the next decade of retail.
original article: https://news.google.com/rss/articles/CBMiaEFVX3lxTFB1U05NcUt1VmJtNEVmYjZjRFdKNFZmc3U5ZWhNMVlkM0FFWUkwN3IyMEhBT3p0S0tTWnpUdnp0LVc0MlNfZ05QLTNHdlh5bEZ0bzQ3bE5XcTk4RFgwWXVHcnFQX2JCT3RN?oc=5
by Csongor Fekete | Aug 21, 2025 | AI, Business, Machine Learning
DeepSeek, a Chinese generative AI company, has delayed the launch of its latest AI model due to a shortage of cutting-edge Huawei chips, according to a recent FT report. The delay highlights the growing bottleneck in AI development caused by limited access to high-performance infrastructure—a critical issue for any organization aiming to scale AI-driven solutions.
The article points out that despite the growing demand for custom AI models, especially in the martech and customer service domains, hardware supply chain disruptions are emerging as a major risk factor in execution timelines. DeepSeek's challenges underscore the importance of diversified infrastructure strategies and partnerships in AI development.
From a business perspective, this situation reveals essential learnings: investing in alternative cloud or chip providers, optimizing workloads for available infrastructure, and planning for AI deployment contingency are all crucial. For marketing teams and CRM platforms, this is a reminder that the performance of AI-driven campaigns, Machine Learning model efficiency, and customer satisfaction depend on resilient back-end technology.
A relevant use-case would be a holistic martech platform utilizing a Machine Learning model for real-time customer segmentation. If such a platform faced model deployment delays due to hardware shortages, it could miss critical campaign windows, reducing performance and negatively impacting customer engagement. Collaborating with an AI consultancy or AI agency ensures contingency solutions—like model compression or edge deployment—can be implemented to safeguard business value.
As HolistiCrm continues to align marketing goals with AI capabilities, recognizing these infrastructural dependencies is essential for delivering uninterrupted value to customers.
Read the original article: https://news.google.com/rss/articles/CBMiugFBVV95cUxNaVFmbHdtcnBoQk1DWUIyT0EtWlozT2dseUZXTTEya1ctdmJ3UlVDdG1QT093MDhVbHlqd0p6OVRhTHMyUVVCZmF1b29oWVV5ckcwQS1fUmNXOTdCZ2Z5MjMyWFNzTDhpU1dmVjBoOXBpWGluQkpBUWlROWpzSjduUjM0OEpqaks4U1ItaWVrelRieHcxUUg0elBDZ1A2WWFUNFFnR2JldnJZUWt1OXQwT1dxOUZGUGJRckE?oc=5
by Csongor Fekete | Aug 20, 2025 | AI, Business, Machine Learning
As the AI industry accelerates, new tensions around the governance, transparency, and strategic direction of foundational technologies are beginning to surface. In the Washington Post’s recent article Opinion | How ‘Altman’s Pause’ could knock the AI industry off course, the spotlight is on a controversial decision by OpenAI CEO Sam Altman to temporarily halt further development of GPT-5. The article raises critical concerns around concentration of power, ecosystem fragmentation, and the potential hazards of a centralized approach to artificial intelligence innovation.
The key takeaway is the fragility introduced when powerful AI progress is tied to the decisions of a few corporate leaders rather than robust open ecosystems or regulatory frameworks. Such a “pause” may stifle competition, slow down innovation, and hinder the broader adoption of AI in diverse sectors, including marketing, healthcare, and customer engagement.
From a business perspective, this moment underscores why companies must invest in their own holistic AI capabilities—particularly by developing custom AI models tailored to specific needs instead of over-relying on generalized, commercially controlled platforms. For customer-centric operations such as CRM, the value of maintaining control over performance, privacy, and model alignment cannot be overstated.
A relevant use-case is marketing automation in martech platforms. By building custom Machine Learning models designed to predict churn or optimize customer journey mapping, businesses can improve satisfaction scores, increase lifetime value, and outperform competitors—regardless of external developments from foundational model providers. An AI expert or AI consultancy like HolistiCrm ensures that these solutions are sustainably integrated and continuously refined, creating real business value independent of industry pauses or policy shifts.
original article: https://news.google.com/rss/articles/CBMijgFBVV95cUxPTFI5M0NJTmYyV2lJMWx2VEdnY1VvRDhLbjFJdGZOdWVJTkI5QmpSblBWNFdLd0JzeTJkRmRSZW9SX0YyWkkyUWpuVER2ZWxuVHp3NmduU01QSjFReWNwa3NDbGdUSWstVTF3YkxhTzE2eklYeF80ZlRNSXZUdG5WOTdPTVQwMjFiMkRIa3d3?oc=5
by Csongor Fekete | Aug 20, 2025 | AI, Business, Machine Learning
OpenAI's recent deployment of a "model router" within GPT-5 has sparked a wave of concern among some users but also introduced a glimpse into the powerful direction AI can take. The router dynamically selects which underlying model—such as GPT-4, GPT-4 Turbo, or prototype sub-models—responds to a user query. This model-switching strategy promises enhanced performance and efficiency but has raised transparency and trust issues due to the lack of user visibility into which model is being used at any given time.
The key insight: routing between models based on performance optimization can dramatically scale intelligent output, reduce latency, and lower costs. However, lack of transparency about model-switching can impact customer satisfaction, especially for power users with specific needs or preferences.
For businesses adopting custom AI applications—particularly in martech and customer engagement—model routing provides valuable inspiration. A dynamic Machine Learning model router can be trained to select the best-fitting model for various user inputs, tailoring experiences in real time. For example, a CRM using HolistiCrm's AI consultancy services could deploy a model router that differentiates between customer service inquiries, upsell opportunities, and churn risks—activating different custom AI models based on intent and urgency. This provides holistic personalization at scale while optimizing workload distribution among models.
Incorporating adaptive routing also allows AI agencies to ensure high-performance infrastructure without over-relying on single models. As AI usage deepens in marketing and sales ecosystems, adopting intelligent routing enhances model precision, efficiency, and robustness—core to any holistic AI strategy.
This development signals a growing shift towards multi-model orchestration as the framework for next-gen AI engagement platforms.
Read the original article: https://news.google.com/rss/articles/CBMiggFBVV95cUxQRWsxNlgyM1dTSnB0NjRkUEVPZzgtTERSbWxOZkI3V042cGcwTXMzbTVLVWRmXzhrU0NKMkRLaUhXamVvRV9veEpTb3NwTGxrd2o4X2dESGxVUXlmY3VhRGI1dlYzS0VEdFRsSDFKa3JzZVIxQmV3Z2s2N2ZQMER3UTJR?oc=5
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