Gennaro Cuofano's Blog, page 69
January 26, 2025
Is Anthropic Disentangling From Amazon AWS?
Google is committing an additional $1 billion investment in Anthropic, a rising AI foundation model competitor valued at ~$60 billion.
This funding is separate from its earlier $2 billion commitment and a recent $2 billion funding round led by Lightspeed Venture Partners.
Anthropic’s annualized revenue has surged to $875 million, driven by direct sales and third-party cloud services, underscoring its rapid growth.
This move comes as Google seeks to strengthen its position in the competitive AI space, especially after Amazon doubled its stake in Anthropic to $8 billion in late 2024.
The escalating investments reflect growing confidence in Anthropic’s potential to challenge OpenAI and solidify its place in the foundation model market.
Google’s strategic alignment with Anthropic signals a high-stakes race among tech giants to dominate AI innovation, with significant financial and technological implications for the industry.
A key thing to understand right now is how tangled the landscape of AI models and cloud providers is.
That will break in the coming decade, as foundational AI model providers like OpenAI and Anthropic will try to go their way, at least to become more independent on the AI Cloyd Infrastructure side.
On the other end, Hyperscalers like Amazon and Google will try to become less entangled from a single foundational AI player, also to go native, and have their own internal AI model to drive differentiation on an otherwise easily commodifiable cloud-providing service!
Yet, for now, co-opetition (or borderline conflict of interest) is the rule.
Indeed, a few of these major deals might be disentangled in the coming years as antitrust comes into the picture, so be ready for it…
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Meta CapEx Plan Is A Panick Mode
Tech people can be easily impressed.
Indeed, Zuck’s PR transition has tried to make him look something else.
But the truth is Meta is probably undergoing one of the hardest transitions in his history.
I do believe Zuckerberg will be able to pull this off. However, right now, the whole thing is more panicked than else:
Source: RedditMeta is significantly boosting AI and data center investments, planning to allocate $60–$65 billion in 2025, marking a 70% increase from 2024. This bold move focuses on AI innovation, including tools for developers, advanced AI chatbots, and an AI engineer capable of writing code.
Central to this expansion is constructing a massive data center in Louisiana, comparable in size to parts of Manhattan, with plans to bring 1 gigawatt of computing power online by 2025.
By year-end, Meta aims to deploy over 1.3 million GPUs, which is critical for training sophisticated AI models.
This strategy positions Meta in direct competition with tech giants like Microsoft, Amazon, and Google in a multi-billion-dollar AI race, further accelerated by the Stargate project and U.S. government support for AI leadership.
Amid this expansion, Meta targets a 17% revenue growth, buoying investor optimism as it doubles down on AI-driven innovation and infrastructure expansion.
Like other players undergoing a similar transition (Google, Microsoft, Salesforce), Meta will undergo a defend-attack tactic before that can turn into a transform-create strategy.
Yet, as of now, Meta is primarily in defense mode, even though it has been trying to sell its move to the market as a “create” strategy!
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Is Chinese AI Open-Source Winning?
If the title above sounds weird, well, it is, and indeed the whole AI-tech industry was shaken last week by the DeepSeek Model Release.
China’s DeepSeek lab unveiled DeepSeek-R1, a reasoning AI model that rivals OpenAI’s o1 with competitive benchmark performance.
Why was this so impressive?
DeepSeek stands out in the AI landscape for its cutting-edge capabilities and exceptional performance metrics.
DeepSeek-V3 is claimed to be trained using just 2,048 NVIDIA H800 GPUs over two months, arriving at 2.8 million GPU hours. For context, a comparable model like the LLaMA 3 model required over 30.8 million GPU hours.
That has opened a massive debate in tech, with many impressed by it, claiming an architectural breakthrough, and many others doubting that DeepSeek has been able to really do that and actually arguing that the company has way more GPUs.
In other words, DeepSeek R1 has disrupted the AI community by matching OpenAI’s o1 at just 3%-5% of the cost.
This open-source model has captivated developers, with 109,000 downloads on HuggingFace so far, and its search feature now rivals Google’s Gemini.
Key innovation: DeepSeek skipped traditional supervised fine-tuning, relying on reinforcement learning to develop independent reasoning. This leaner approach delivered exceptional results despite using 50,000 GPUs compared to OpenAI’s 500,000.
For enterprises, DeepSeek democratizes AI access, challenging costly proprietary models.
While ethical and ROI concerns remain, DeepSeek reshapes AI development, sparking a shift toward cost-efficient innovation and transparency.
We’ll leave this discussion for a later issue, but for now, while DeepSeek has been a wake up call for most AI players, I believe there is a single player that DeepSeek has been able to impress, or if you wish, scare the hell out: Meta.
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Is Meta Getting Ready With Its AR Glasses?
Meta is embarking on a massive AI-driven transformation, introducing a wave of AI-based hardware products while integrating AI deeply into its ecosystem.
Among the highlights are Oakley-branded smart glasses for athletes, slated for 2025, alongside high-end glasses with built-in displays that showcase advanced technology.
Meta is also revisiting its smartwatch project and developing camera-equipped earbuds as an AirPods rival.
The company’s first AR glasses, codenamed “Artemis,” represent a leap into true augmented reality, with a planned release in 2027.
Meta’s Orion, still a prototype, can the company make it compact enough to fit it, into an AR glass? Source: Meta
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Meta’s Orion in actionThese initiatives reflect Reality Labs’ leadership and Meta’s dedicated division for hardware innovation, signaling the company’s ambition to dominate the AI and wearable technology markets.
AI integration will be central to all these devices, transforming their functionality and user experience.
With this pivot, Meta appears poised for a potential reorganization, similar to shifts seen at Google and Microsoft, as it aligns more closely with its AI strategy and hardware ambitions.
This move is, therefore, part of a broader transformation to fit into the new business landscape.
Before I jump into it, let me give you also another glimpse into why.
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Google Closer To An AR launch?
Since 2017, I’ve been tracking the development of Google’s projects around AR in the hope that the company would be back on track to launch its Google Glasses (which I found quite cool, even if launched with terrible timing).
The Google Glass, in 2014I believe Google is planning a massive launch for a potential AR device, as it acquired part of HTC’s extended reality (XR) business for $250 million.
For context, this follows Google’s 2017 purchase of HTC’s smartphone unit, where the company paid $1.1 billion to “acqui-hire” a significant portion of HTC’s engineering team, which would play a key role in building the successive iterations of the Pixel smartphones.
HTC will retain development rights in its XR division. The following deal aims to accelerate Android XR platform development.
The acquisition strengthens Google’s position in the competitive XR market, dominated by Apple and Meta, and it shows that a major move might be coming soon, potentially as an AR consumer device launch.
And if it’s unclear, Project Astra, on that device, will be the underlying AI assistant powering up future models of Google’s smart glasses.
In short, it all seems ready for a launch in 2025.
Project Astra has already given us a glimpse (from Google’s DeepMind offices in London’s Kings Cross) of what it can be!
https://www.youtube-nocookie.com/embed/nXVvvRhiGjI?start=1s&rel=0&autoplay=0&showinfo=0&enablejsapi=0Just recently, Google has undergone a massive re-org.
That makes it all ready for that sort of launch…
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OpenAI Entered Full Swing Into The Agentic Race, But With A Consumer-Twist
OpenAI has officially launched Operator, an AI agent designed to automate browser-based tasks like ordering groceries and filling forms.
Built on its CUA model, Operator leverages GPT-4o’s vision and reasoning capabilities to interact with graphical user interfaces (GUIs).
Currently available as a research preview for U.S. Pro users, Operator offers a glimpse into AI-driven task automation.
The tool excels at handling repetitive tasks and allows users to customize workflows with robust safety features such as takeover mode, task limitations, and user confirmations to ensure secure and reliable operation.
Additionally, Operator incorporates privacy safeguards, enabling users to delete data, opt out of training, and stay protected from malicious sites.
To enhance its real-world utility, OpenAI has formed ecosystem partnerships with companies like DoorDash, Instacart, OpenTable, and public sector collaborators.
However, Operator remains in its early stages, struggling with complex tasks like managing calendars and creating slideshows.
Future plans include expanded access, improved task capabilities, and seamless integration with ChatGPT, positioning Operator as a versatile productivity tool for both personal and professional use.
https://www.youtube-nocookie.com/embed/CSE77wAdDLg?start=1s&rel=0&autoplay=0&showinfo=0&enablejsapi=0A key take from it is while other similar ones, like Antrhopic’s “computer use,” might be primarily targeting business productivity, OpenAI is also a marketing Operator as a consumer personal assistant.
Able to perform every possible task, like buying concert tickets to daily tasks. This is important because it reminds us that OpenAI positions itself, very similarly to Google, as a consumer-first company.
In short, OpenAI will keep working to bring onboard major enterprise customers while also closing major infrastructure deals, like Stargate, its strength relies on keeping momentum as a consumer brand.
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The OpenAI-Microsoft Partnership
For one thing, Stargate has shown the unraveling of Microsoft and OpenAI’s partnership.
The partnership between Microsoft and OpenAI is undergoing significant changes, with Microsoft no longer serving as OpenAI’s exclusive cloud provider.
OpenAI now has the flexibility to collaborate with other providers, enabled by its Stargate infrastructure deal with SoftBank, Oracle, and others.
Despite this shift, Microsoft retains the “right of first refusal,” allowing it to prioritize hosting OpenAI’s workloads before competitors.
Key aspects of the partnership remain intact, including OpenAI’s continued use of Azure for products and training under a long-term agreement lasting through 2030.
However, the agreement includes an AGI clause, where Microsoft’s exclusive access to OpenAI’s technology would terminate if OpenAI achieves artificial general intelligence (AGI), generating $100 billion in profits.
This evolution opens the door for potential renegotiations, as OpenAI may seek additional funding from Microsoft.
The Stargate initiative signals a broader strategy for OpenAI to diversify its infrastructure and expand its research and model-training capabilities while maintaining a collaborative relationship with Microsoft.
In the meantime, OpenAI tries to keep its dominant consumer position with its agentic move, but for the consumer.
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January 23, 2025
Emerging Trends In AI
I’ve already explained in detail how the next cycle of development of this current AI paradigm might play out (of course, as a major directional macro-trend) in the next 10-30 years.
The AI Convergence
Many people mistakenly view the current AI paradigm as “another new industry.”
I’ve also put together some major trends that have shaped and built up in the last two years since the “ChatGPT Moment,” and that is further developing as we go into 2025.
20+ AI Business Trends For 2025!In the next couple of decades, we will be able to do things that would have seemed like magic to our grandparents.
In addition, I’ve covered how this AI paradigm (we call it so, as we had different AI paradigms at play in the last 50 years, but the last, driven by a transformer architecture, is the neural-net-based, Generative AI, which we now simplify in “AI) will expand the existing software industry!
Why AI Is 10xing SaaS!
Many discussions today around AI look into the technical aspect but are missing the broader picture.
Now, for this issue, I’ll take on the perspective of major players from the industry on what they see coming next: Microsoft, NVIDIA, and a16z.
Of course, Microsoft and NVIDIA play a key role in the new AI paradigm.
A16z also plays a vital role in business model development by financing key AI startups.
From the below, I hope you can understand where we’re going next as 2025 comes closer!
Ready? Go!
6 AI Trends to Watch in 2025, according to Microsoft
According to Microsoft, AI will transform work and daily life with more capable models, autonomous agents, and personalized companions.
Innovations will enhance sustainability, scientific breakthroughs, and responsible AI practices, including better testing and customization.
These advancements promise efficiency, problem-solving, and new possibilities across industries while maintaining a focus on safety and oversight.
More precisely:
More Capable and Specialized AI Models:AI models will become faster, more efficient, and capable of advanced reasoning.New approaches like Microsoft’s small Phi models and Orca series demonstrate how curated and synthetic data can improve AI performance.These advancements will enable broader and more precise applications in science, medicine, coding, and business.AI-Powered Agents Revolutionizing Work:Agents will handle complex tasks, such as supply chain management and HR support, freeing employees for high-value work.Tools like Copilot Studio will democratize agent creation, requiring no coding skills.Organizations will deploy constellations of agents to automate processes with human oversight ensuring safety and accountability.AI Companions Enhancing Daily Life:AI companions like Microsoft Copilot will simplify tasks such as managing information, decision-making, and shopping.Features like Copilot Daily and Copilot Vision will deliver tailored news, understand web pages, and assist with decisions in more intuitive ways.Future developments will focus on improving emotional intelligence for more seamless interactions.Resource-Efficient AI Systems:Despite increasing workloads, advances in efficiency have limited energy demand growth in datacenters.Innovations include custom silicon (Azure Maia and Cobalt) and sustainable cooling systems like liquid cooling heat exchangers.Upcoming datacenters will aim to consume zero water for cooling, supporting a sustainable AI infrastructure.Sustainable AI Infrastructure:Microsoft is investing in low-carbon building materials and clean energy sources like wind, nuclear, and solar.AI infrastructure will align with Microsoft’s goal to be carbon-negative, water-positive, and zero waste by 2030.Long-term energy investments aim to make AI infrastructure environmentally sustainable.Global AI Transformation:AI adoption jumped to 75% among business leaders in 2024, with 2025 set to see broader integration across work and home.AI will address global challenges, such as climate crisis solutions and improving healthcare access, driving transformative change in daily life and business.NVIDIA’s Vision For 2025
According to the NVIDIA leadership team, generative AI and agentic AI will transform industries in 2025, driving innovation in robotics, quantum computing, and data analytics. Liquid-cooled AI data centers and distributed compute fabrics will enable real-time applications.
New roles like prompt engineers emerge while AI enhances design, education, and agriculture.
Personalized AI agents and sustainable solutions will reshape workflows globally.
Generative AI Crosses the Chasm Generative AI adoption surged across industries, forecasted to generate $1.3 trillion in revenue by 2032.Enterprises move from experimentation to full-scale integration, driven by optimism around ROI thresholds.Agentic AI emerges as the next wave, with autonomous reasoning powered by advanced language models and data architectures. Inference and AI Factories Demand for efficient AI inference rises as models like OpenAI o1 and Llama 450B handle increasingly complex tasks, requiring new hardware innovations.Enterprises build AI factories to transform raw data into business intelligence for applications like supply chain optimization and market forecasting.Scalable, liquid-cooled AI data centers become essential for handling massive workloads efficiently. Agentic AI and AI Orchestrators AI agents evolve into orchestrators capable of managing diverse enterprise tasks such as HR, customer service, and supply chain management.Multilingual and multimodal capabilities empower agents to interpret and act on complex data types, driving higher productivity and decision-making. Humanoid Robotics and Edge AI Multimodal robot foundation models integrate vision, language, and action, advancing humanoid robots’ ability to handle arbitrary commands.Small, energy-efficient AI models enable edge inferencing, promoting mobile intelligence in applications like robotics and wearable devices. AI Transforming Industries Agriculture: AI optimizes the food chain, reduces greenhouse gas emissions, and improves supply chain design.Construction and Engineering: AI accelerates project timelines, enhances safety, and refines structural designs through physics-informed neural networks.Design: Generative AI streamlines conceptual design, combining keyword prompts and sketches into richly detailed visuals. Quantum Computing Convergence Quantum computing integrates with AI and supercomputing to solve challenges in error correction, drug discovery, and materials development.Low-latency quantum hardware colocated within supercomputers emerges to tackle complex computations. AI Gets Personal Advances in photorealism and emotional intelligence make AI interactions more humanlike and engaging.AI query engines revolutionize data analysis by seamlessly integrating natural language processing and machine learning, enabling smarter decisions. Sustainability and Scalability Liquid cooling and renewable energy integration reduce AI data centers’ environmental footprint.Enterprises embrace geographically dispersed data centers, leveraging renewable energy sources to scale sustainably. New Roles in AI Workforce Rise of prompt engineers and AI personality designers to optimize AI interactions and create unique, brand-aligned agent personalities.Metrics like revenue per employee (RPE) become critical for startups to measure the impact of AI-augmented workforces. AI in Entertainment and Realism Hyperrealistic generative AI advances revolutionize gaming, movies, and digital experiences.Generated pixels replace traditional graphics, lowering production costs for sophisticated visual media.a16z Big Business Ideas for 2025
a16z highlighted what to expect as key ideas for 2025, with tech innovation spanning nuclear energy for AI demands, healthcare breakthroughs like CAR-T therapy, and AI copilots for white-collar roles. Decentralized cryptosystems, interactive video storytelling, and real-time AI reshape industries.
On-device AI, asset tokenization, and Google search challengers drive change while infrastructure and governance evolve for AI dominance.
Let’s see some of them.
American Dynamism Nuclear Resurgence:Rising demand driven by AI data centers’ insatiable energy needs.Bipartisan support and grassroots enthusiasm spur investments in reactors like Three Mile Island (slated for 2028).Nuclear energy becomes critical for securing leadership in global AI and resilient energy grids.Hardware-Software Chasm:Resurgence of engineering roles like electrical, mechatronics, and industrial engineers to address autonomy, reshoring, and aging workforce challenges.Hardware-focused roles may outpace software engineering growth in the coming decade.Defense Decentralization:Autonomous systems and battlefield AI enable real-time decision-making at the edge.Mobile command centers process vast amounts of data locally, supported by scalable compute and reliable power in hostile environments. Bio + Health Big is Back:Renewed focus on common diseases like diabetes, obesity, and autoimmune disorders.CAR-T cell therapy breakthroughs could revolutionize autoimmune disease treatments.Biotech startups embrace large, impactful targets, inspired by GLP-1 drug success.Health Democratization:Wearables, AI biomarkers, and early detection tools empower individuals to monitor health proactively.AI refocuses healthcare from reactive “sick care” to prevention and early intervention.Super Staffing in Healthcare:AI platforms automate administrative and repetitive clinical tasks, addressing staffing shortages and unlocking efficiency. Consumer Tech AI Companions with Inner Worlds:Next-gen AI companions will have virtual lives, motivations, and emotional intelligence for richer, give-and-take interactions.AI Video Specialization:Generative video tools become tailored for use cases like marketing, long-form film, and 3D avatars.AI video evolves into an art form with increased depth, character consistency, and storytelling capabilities.AI “Brains” for Personal Use:AI tools will act as personal “digital brains,” organizing unstructured personal data to assist decision-making, self-improvement, and interactions. Crypto AI Wallets and Decentralized Chatbots:AI agents gain autonomous capabilities, including crypto wallets for transacting and decentralized chatbot operations for managing assets.Proof of Personhood:Privacy-preserving digital identity systems combat impersonation and deepfake risks, ensuring unique IDs for people, not AIs.Stablecoin Adoption:Small businesses begin adopting stablecoins for cost-effective transactions, challenging traditional credit card systems. Enterprise + Fintech AI-Native Systems of Record:Enterprises replace legacy systems with AI-powered systems that combine engagement and data storage to optimize workflows.AI Copilots in the Workplace:AI agents handle tedious tasks like data entry and initial outreach, freeing employees for strategic work.Regulation as Code:AI-based tools streamline compliance by interpreting regulations and providing actionable guidance, reducing the burden on businesses and consumers. Games Interactive Video Games:AI-native storytelling blends movies and games, offering infinite, player-driven gameplay generated in real-time by neural networks.Game Tech Beyond Entertainment:Game engines and AI models revolutionize industries like real estate, defense, and autonomous vehicle testing. Infrastructure Hypercenters for AI:Nations race to develop AI infrastructure, with AI Hypercenters requiring 3–6 GW capacity to stay competitive in frontier AI.On-Device AI:Smaller, efficient AI models dominate in volume, enabling real-time interactions on phones, IoT devices, and appliances.Generative AI Everywhere:AI becomes integrated into everyday applications and devices, from photo editing to real-time language translation. Growth-Stage Tech Decline of “Google It”:Alternatives like ChatGPT, Claude, and Perplexity challenge Google’s search dominance.Users favor deeper, context-driven AI queries over traditional ad-heavy search results.Golden Era of Sales:AI-powered tools streamline administrative tasks, enabling sales teams to focus on high-touch client interactions, driving growth in hiring.AI App Differentiation:Winning AI apps combine multimodal models with customer-specific data for tailored solutions, moving beyond generic AI “wrappers.”Key AI Trends and Their Implications for 2025 AI Agents Redefine Work and Automation Trend: AI-powered agents with advanced reasoning capabilities will handle complex workflows in supply chains, HR, and customer service. Tools like Microsoft Copilot Studio democratize agent creation for businesses and individuals.Implications: Businesses will achieve unprecedented efficiency by automating mundane tasks, freeing human workers for strategic and creative roles. AI orchestrators will enable seamless integration across systems, ensuring scalability.Future Outlook: Companies will deploy “constellations of agents” working autonomously with human oversight, fundamentally altering how organizations operate. Generative AI Becomes Mainstream Trend: Generative AI will evolve from prototypes to revenue-driving applications, expected to contribute $1.3 trillion by 2032. Advances in models like OpenAI o1 and Nvidia’s platforms enable new use cases in entertainment, healthcare, and business.Implications: Industries will leverage generative AI for product design, interactive storytelling, and marketing, increasing creative possibilities. Enterprises will build AI factories to process and transform raw data into actionable insights.Future Outlook: Generative AI will converge with real-time systems, making personalized applications and interactive media ubiquitous across consumer and enterprise domains. AI-Driven Sustainability Solutions Trend: AI will support sustainability through resource-efficient datacenters, renewable energy integration, and tools for environmental optimization in agriculture and urban planning.Implications: Companies like Microsoft and Nvidia are building energy-efficient infrastructure, employing techniques like liquid cooling and using clean energy sources such as wind and nuclear power. AI tools in agriculture will reduce emissions and enhance supply chains.Future Outlook: AI advancements in infrastructure will reduce energy footprints, align with climate goals, and potentially catalyze breakthroughs in clean energy and resource management. Rise of Personalized AI Experiences Trend: AI companions and tools will increasingly integrate emotional intelligence, providing tailored support in daily life. AI query engines and personalized “digital brains” will help individuals manage unstructured data and make informed decisions.Implications: Personalized AI experiences will improve productivity and well-being, with applications in decision-making, health management, and task prioritization. Enhanced realism in interactions will drive adoption across home and professional settings.Future Outlook: AI companions will become integral to life, offering emotionally intelligent interactions that adapt to user needs and preferences. AI in Specialized Industries Trend: AI will revolutionize agriculture, engineering, and medicine, optimizing processes like crop management, structural design, and disease treatment. Tools like physics-informed neural networks and CAR-T therapy will advance healthcare and engineering.Implications: Industries will experience efficiency gains and cost reductions, with AI applications addressing critical challenges like food security and healthcare accessibility.Future Outlook: AI adoption in specialized fields will create tailored solutions for global challenges, driving innovation and economic growth. Emergence of Humanoid Robotics and On-Device AI Trend: Multimodal robot models integrating vision, language, and actions will enable humanoid robotics to handle diverse tasks. Small, efficient AI models will power edge devices and wearables.Implications: Robotics and on-device AI will promote automation in homes, factories, and remote locations, while advancing mobility and real-time intelligence in consumer devices.Future Outlook: Humanoid robots will gradually enter everyday settings, supported by on-device AI for personalized and context-aware applications. Quantum Computing Meets AI Trend: Quantum computing will integrate with AI to address complex challenges, focusing on error correction and drug discovery. Nvidia and others are exploring colocated quantum systems in supercomputers.Implications: Combining quantum and AI capabilities will accelerate breakthroughs in science, materials development, and logistics optimization.Future Outlook: Quantum-AI convergence will unlock transformative applications, particularly in sectors requiring intensive computations. Decentralized and Autonomous AI Ecosystems Trend: AI agents will leverage decentralized systems, such as crypto wallets and blockchain, for autonomous decision-making and secure transactions.Implications: Decentralization will drive innovation in autonomous systems, reducing reliance on central control and enabling new business models in finance, supply chains, and digital identity.Future Outlook: Autonomous, decentralized AI systems will expand capabilities in governance, infrastructure, and economic activities.Are you ready?
Ciao!
With 
Gennaro, FourWeekMBA
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30+ Rising AI Startups to Keep An Eye On In 2025
What’s a good mental model to understand where we’re going next in AI ecosystem development?
The AI Convergence explains which areas will be critical to look at in the next 10-30 years:
The AI ConvergenceMany people mistakenly view the current AI paradigm as “another new industry.”
Read full storyIn the three layers of AI, I’ve explained a good mental model to leverage to understand what’s going on and where to keep an eye on when it comes to the nascent AI ecosystem.
In addition, and that’s a very tricky one, how can we judge whether a company has some moats or is building any sustainable ones in the AI industry?
I’ve put together this whole piece to explain how AI companies might be able to build a long-term moat on top of a very competitive industry that is just developing.
A caveat here:
The research below is not intended to find out who will be the next unicorn, the AI landscape is still quite fluid. Instead, it’s more to emphasize in which areas of development of the ecosystem we might expect new major companies to emerge, as these verticals are the ones where there might be more traction, both from enterprise and consumer standpoint.
With that in mind, these are some of the patterns we found out as the research was undertaken:
Convergence of Hardware and Software: Integration of cutting-edge hardware and advanced software to enable scalable and efficient AI solutions.Agentic and Autonomous Systems: Development of AI agents capable of independent reasoning, task execution, and decision-making.Domain-Specific Innovations: Tailored AI applications addressing high-value problems in healthcare, legal, transportation, and other verticals.Democratization and Accessibility: Open-source platforms and user-friendly tools driving broader adoption of advanced AI capabilities.Cross-Industry Applications: Versatile AI solutions seamlessly integrating across diverse sectors like climate, space, and enterprise data management.AI Safety and Ethical Alignment: Focus on reliability, transparency, and regulatory compliance to ensure ethical AI operations.Generative AI Beyond Text: Expansion of generative AI into creative domains like music, design, and visual applications.Real-Time and Context-Aware AI: AI systems delivering dynamic, contextually relevant insights and actions in real time.Rapid Infrastructure Growth: Scaling AI cloud and computing infrastructure to meet growing demand for processing power.Collaborative Ecosystems: Shared frameworks and platforms fostering innovation within the AI developer and user communities.Infrastructure (Hardware and Chips)1 – CoreWeave
Year founded: 2017
Location: Roseland, New Jersey, USA
What they do
CoreWeave provides cloud-based GPU infrastructure designed for the intense demands of AI and machine learning.
Initially a cryptocurrency mining venture, the company pivoted in 2019 to focus on delivering powerful, scalable computing solutions.
Today, CoreWeave supports cutting-edge AI applications across industries, and, in May 2024, secured $7.5 billion in debt financing to expand its infrastructure and meet growing demand.
2 – Cerebras Systems
Year founded: 2015
Location: Sunnyvale, California, USA
What they do
Cerebras Systems is revolutionizing AI computing with the world’s largest processor, the Wafer-Scale Engine (WSE-2).
Designed for deep learning and high-performance AI, their CS-2 system accelerates workloads that would take traditional processors months to complete.
This technological advancement has positioned Cerebras as a leader in the AI hardware sector. In November 2021, the company raised $250 million in a Series F funding round to become valued at over $4 billion.
In late 2024, the company announced plans to hold an IPO which some analysts believe could value it at around $8 billion.
AI Models (Software)3 – OpenAI
Year founded: 2015
Location: San Francisco, California, USA
What they do
OpenAI is a leading AI research organization dedicated to developing and promoting AI technologies that benefit humanity.
The company is responsible for groundbreaking models like GPT-4 and DALL-E, which have significantly advanced natural language processing and image generation.
The Sam Altman-led company’s innovations are widely adopted across various industries and enhance applications from customer service to creative design.
In October 2024, OpenAI raised $6.6 billion in funding to be worth around $157 billion. The company noted that the raise would allow it to “double down on our leadership in frontier AI research, increase compute capacity, and continue building tools that help people solve hard problems.”
4 – Anthropic
Year founded: 2021
Location: San Francisco, California, USA
What they do
Anthropic is an AI safety and research company dedicated to developing reliable and interpretable AI systems.
Their flagship product, Claude, is a conversational AI assistant designed to perform a wide range of tasks while adhering to relevant safety protocols. Anthropic emphasizes AI alignment and transparency, which sets the company apart from others in the industry.
In November 2024, Anthropic secured an additional $4 billion investment from Amazon. This brought Amazon’s total investment to $8 billion and strengthened their strategic partnership in the process.
5 – Mistral AI
Year founded: 2023
Location: Paris, France
What they do
Mistral AI is redefining AI accessibility by creating open-weight large language models like Mistral 7B and Mixtral 8x7B.
These models offer powerful AI capabilities without the constraints of proprietary systems, which makes these advanced tools more accessible to businesses.
The company’s competitive pricing and open-source approach to LLMs have quickly positioned it as a standout in the competitive AI landscape. In June 2024, Mistral secured €600 million in Series B funding, boosting the then-one-year-old company’s valuation to an impressive €5.8 billion.
6 – Hugging Face
Year founded: 2016
Location: New York, USA
What they do
Hugging Face is a leading open-source platform that enables developers and organizations to build, train, and deploy machine learning models collaboratively.
Their extensive repository includes over a million models and datasets that work to foster innovation across the AI community.
Like Mistral, Hugging Face has become a central hub for AI development by democratizing access to advanced AI tools. It has also earned the attention of major tech companies such as Salesforce, Google, and Nvidia.
In August 2023, the company raised $235 million in Series D funding to become valued at $4.5 billion.
Applications (Enterprise)Agentic AI7 – /dev/agentsYear founded: 2024
Location: San Francisco, California, USA
What they do
/dev/agents is the developer of an operating system tailored for AI agents that aims to simplify the creation of digital assistants capable of autonomous task execution and decision-making.
Founded by former Google executives, CEO David Singleton claimed the company was seeking to deliver an “Android-like moment for AI” by providing a unified platform that enables seamless interaction between AI agents and users across various devices.
In November 2024, /dev/agents raised $56 million in seed funding to advance their innovative platform. Notable participants included Index Ventures and CapitalG—Alphabet’s independent growth fund.
8 – SierraYear founded: 2023
Location: San Francisco, California, USA
What they do
Sierra is redefining customer service with advanced conversational AI platforms that allow businesses to create personalized, responsive AI agents.
Led by industry heavyweights Bret Taylor (ex-Salesforce) and Clay Bavor (ex-Google), the company’s solutions are already used by top brands like ADT and Sonos to deliver seamless customer experiences.
Sierra’s innovative approach to AI-driven customer engagement earned it $175 million in funding in October 2024. The company—which recently crossed $20 million in annualized revenue—is now worth an estimated $4.5 billion.
9 – Imbue AI
Year founded: 2021
Location: San Francisco, California, USA
What they do
Imbue (once known as Generally Intelligent) is pushing the boundaries of AI by building LLMs that can think, reason, and make decisions in real-world environments.
Unlike typical AI tools, Imbue’s agents aim to truly understand user goals and solve complex problems autonomously.
This bold vision for practical, goal-oriented AI has set them apart in a crowded field. In September 2023, Imbue reached unicorn status after it secured $200 million in Series B capital.
10 – Adept AI
Year founded: 2022
Location: San Francisco, California, USA
What they do
Adept AI is transforming human-computer interaction by developing AI models that execute complex tasks across various software applications and APIs using natural language commands.
Their flagship ACT-1 model serves as an AI teammate that streamlines workflows and enhances productivity. This innovative approach has attracted significant attention and saw the company raise $350 million as part of a Series B funding round in March 2023.
CEO David Luan noted that the cash would be spent on productization, model training, and recruitment.
11 – LangChain
Year founded: 2022
Location: San Francisco, California, USA
What they do
LangChain has quickly become a developer favorite for building AI-powered applications thanks to its versatile framework that integrates with LLMs and diverse data sources.
Whether it’s powering chatbots or advanced analytics tools, LangChain’s open-source platform is now used by over 50,000 organizations around the world (including notable tech players).
In a Series A round led by Sequoia Capital, LangChain raised $25 million in February 2024. Accompanying the announcement was the launch of LangSmith—the company’s first paid LLMOps product with general availability.
12 – Cognition Labs
Year founded: 2023
Location: San Francisco, California, USA
What they do
The flagship product of Cognition Labs is Devin, an AI engineer that can independently code, debug, and deploy applications. Devin streamlines development processes and reduces reliance on human intervention, which increases speed and accuracy.
The potential to automate entire projects has captured industry attention and cemented Cognition Labs as one to watch.
In April 2024, after just six months in operation, the startup secured $175 million and became worth around $2 billion.
Data and Analytics13 – Databricks
Year founded: 2013
Location: San Francisco, California, USA
What they do
Databricks offers a unified data and AI platform that simplifies data engineering, collaborative data science, and machine learning across various industries.
Their open lakehouse architecture combines the best features of data lakes and data warehouses, enabling organizations to process and analyze vast amounts of data efficiently and cost-effectively.
Databricks counts many industry behemoths among its clients, including AT&T, Warner Bros. Discovery, and Rivian Automotive.
The company announced in late 2024 that it hoped to raise as much as $8 billion—an amount that if successful, would represent the largest venture capital funding round ever.
14 – Arthur AI
Year founded: 2018
Location: New York, USA
What they do
Arthur AI specializes in monitoring and optimizing machine learning models, ensuring they perform accurately and fairly.
Their platform provides real-time insights into model behavior to help businesses detect issues like data drift and bias. This capability will become crucial as companies increasingly rely on AI for decision-making.
In September 2022, Arthur AI raised $42 million in a round led by Acrew Capital. The company is extremely well placed to meet future demand for its services. Indeed, Gartner predicts the AI software market will be worth $124 billion by 2025 thanks to organizations having thousands of AI models deployed.
“Some of the largest and most important companies in the world rely on Arthur to improve the performance and fairness of their critical AI models”, explained Acrew Capital partner Theresia Gouw.
Workplace Productivity15 – Glean
Year founded: 2019
Location: Palo Alto, California, USA
What they do
Glean’s AI-powered search platform for enterprise productivity enables employees to swiftly locate information across various applications and databases.
By integrating data from multiple sources, Glean provides a unified search experience and substantially reduces time spent searching for information.
Glean raised over $260 million in a Series E funding round in September 2024 and doubled its valuation to $4.6 billion. The startup more than tripled its business over the previous 12 months with a product that addresses a shortfall of AI-powered enterprise productivity solutions.
16 – Moveworks
Year founded: 2016
Location: Mountain View, California, USA
What they do
Moveworks is transforming how companies handle employee requests with its cutting-edge AI platform “Copilot”.
Utilizing natural language understanding (NLU), the platform automates tasks like IT support, HR inquiries, and onboarding. It also seamlessly integrates with existing enterprise tools, which means employees spend less time waiting and more time being productive.
Trusted by over 300 companies and 5 million employees, Moveworks has solidified its place in AI-driven workplace solutions. The company was also recognized by Gartner in its 2024 Gartner Magic Quadrant for Artificial Intelligence Applications in IT Service Management.
Human Resources17 – Eightfold
Year founded: 2016
Location: Santa Clara, California, USA
What they do
Eightfold AI is reshaping how companies attract and retain talent with its Talent Intelligence Platform.
Using advanced deep learning, it analyzes global workforce data to match candidates with roles that best suit their skills and potential. The platform also helps businesses improve diversity and streamline hiring.
With a growing global client base that includes the likes of Siemens, Vodafone, Coca-Cola, and Ernst & Young, Eightfold AI has become a key player in HR technology.
Legal and Compliance18 – EvisortYear founded: 2016
Location: San Mateo, California, USA
Evisort is a key player in legal tech with its end-to-end contract lifecycle management platform. Using AI, the startup automates time-consuming tasks like drafting, reviewing, and tracking contracts, freeing up teams to focus on more strategic priorities.
From legal to sales, businesses across industries rely on Evisort to streamline operations and reduce risk. The company’s $100 million Series C funding round in October 2023 was especially notable when one considers that venture backing (and indeed innovation) in the legal space are uncommon.
19 – Harvey
Year founded: 2022
Location: San Francisco, California, USA
What they do
Harvey is also reshaping the legal industry with its AI-powered personal assistant designed to assist lawyers in drafting contracts, analyzing documents, and conducting research.
By automating time-intensive tasks, Harvey helps law firms improve efficiency and focus on more important work. It is also well suited to professional service providers and the Fortune 500.
The company secured $100 million in a Series C funding round in July 2024. The round—which involved heavyweights like OpenAI, Google Ventures, and Sequoia Capital—took the startup’s valuation to $1.5 billion.
Applications (Consumer)Search and Information Retrieval20 – Perplexity AI
Year founded: 2022
Location: San Francisco, California, USA
What they do
Perplexity AI integrates large language models, chatbots, and real-time data retrieval to provide direct answers to user queries.
Their AI-powered search engine delivers concise, accurate responses complete with source citations, which promotes much-needed trust and efficiency in information discovery.
Perplexity has experienced substantial growth in its user base in recent times, with the startup now boasting around 10 million active monthly users. In November 2024, the company was in the process of finalizing additional capital that would see it valued at around $9 billion.
Creative Tools21 – DeepL
Year founded: 2017
Location: Cologne, Germany
What they do
DeepL delivers some of the most accurate AI-powered translations on the market. Its platform leverages advanced neural network technology to translate complex texts with precision, and it can also be used to enhance writing or communicate across languages in real-time.
Known to outperform competitors in linguistic nuance, DeepL has become a favorite for users worldwide. It also has a customer network of more than 100,000 businesses, governments, and organisations such as Zendesk, Coursera, and Deutsche Bahn.
22 – UizardYear founded: 2017
Location: Copenhagen, Denmark
What they do
Uizard democratizes design by enabling users to transform simple sketches into interactive app prototypes without requiring design expertise.
The startup offers a platform that utilizes advanced machine learning models trained to convert textual descriptions into functional UI designs. This, in turn, makes design accessible to non-designers and fosters innovation across various industries. Demand for such services is likely to increase as more companies recognize the importance of product design to revenue and require an AI tool to power their creativity.
In August 2021, Uizard raised $15 million in Series A funding led by Insight Partners. Some of Uizard’s major clients include Samsung, IBM, Meta, Uber, Accenture, Logitech, and Adidas.
Development Tools23 – CodeiumYear founded: 2021
Location: Mountain View, California, USA
What they do
Codeium enhances coding productivity with advanced machine learning models that provide intelligent code suggestions, real-time search, and conversational support.
By analyzing patterns in over 70 programming languages, Codeium’s tools anticipate developer needs, automate repetitive tasks, and help coders focus on creativity and problem-solving.
In August 2024, the company raised $150 million in Series C funding and reached a valuation of $1.25 billion.
The company’s growth (and indeed potential) is the result of identifying a key gap in the market. Specifically, that coders were still struggling with tedious tasks despite the influx of generative AI tools for other purposes.
Music and Art24 – Suno
Year founded: 2022
Location: Cambridge, Massachusetts, USA
What they do
Suno democratizes music creation by enabling users to generate original songs through simple text prompts. The startup’s platform utilizes advanced machine learning models trained on diverse musical data to compose unique tracks across various genres and styles.
Suno’s was founded by a team whose members previously worked at Meta, TikTok, and Kensho and came out of stealth in December 2023. It was also around this time that Suno was incorporated into Microsoft’s AI software platform Copilot.
The startup has attracted a significant amount of funding in the months since, with a $125 million Series B round in June 2024 the highest.
Adjacent IndustriesAutonomous Transportation25 – Waymo
Year founded: 2009
Location: Mountain View, California, USA
What they do
Waymo—which started life as the Google Self-Driving Car Project—develops autonomous driving technology aimed at enhancing transportation safety and efficiency.
The startup’s Waymo Driver system combines sensors and machine learning to enable vehicles to navigate real-world environments without human intervention.
After years of setbacks, Waymo is poised for massive growth. It secured $5.6 billion in funding to be worth over $45 billion in November 2024, with the capital used to expand the Waymo-One robotaxi service and an existing partnership with Uber.
26 – Aurora InnovationYear founded: 2017
Location: Pittsburgh, Pennsylvania, USA
What they do
Aurora Innovation is the developer of Aurora Driver—a self-driving system designed to operate multiple vehicle types, from freight-hauling trucks to ride-hailing passenger vehicles.
With a suite of self-driving hardware, software, and data services, Aurora aims to make transportation safer and more efficient.
In July 2023, the startup raised approximately $820 million through a public offering and private placement, bolstering its financial position to support the commercial launch of its autonomous trucks.
Biology and Healthcare27 – FreenomeYear founded: 2014
Location: South San Francisco, California, USA
What they do
Freenome is at the forefront of early cancer detection, developing blood tests that identify cancer in its initial stages.
Freenome’s multi-faceted approach employs machine learning to analyze data to detect cancer-related patterns. This approach aims to make routine screenings more accessible and less invasive.
The startup has also developed a blood test for early detection of colorectal cancer—the world’s second deadliest cancer that claims over 50,000 lives each year in the United States alone.
In February 2024, Freenome raised $254 million in a round led by pharmaceutical giant Roche.
28 – AbridgeYear founded: 2018
Location: Pittsburgh, Pennsylvania, USA
What they do
Abridge offers a generative AI solution for clinical conversations.
Their platform records and transcribes medical conversations, organizes the information into electronic health records (EHRs) and reduces administrative burdens for clinicians.
This innovation addresses physician burnout while also improving patient care and health outcomes.
Abridge is poised for growth in an industry known for inefficiencies in communication, services, and various clinical processes. In October 2024, it was reported that the company was seeking to raise $250 million from Elad Gill, VC firm Institutional Venture Partners, and Alphabet’s CapitalG growth fund.
This would see the healthcare AI startup valued at $2.5 billion—up from just $200 million in 2023.
29 – Memora HealthYear founded: 2017
Location: San Francisco, California, USA
What they do
Memora Health is transforming healthcare delivery by digitizing and automating complex care workflows.
Memora’s platform enables clinicians to focus on patient care by streamlining administrative tasks and providing patients with proactive, two-way communication. It can be used across various contexts such as cancer care, surgical care, chronic care management, and transitions of care.
Like Abridge, Memora strives to increase the likelihood of favorable patient outcomes while also reducing clinician burnout.
In April 2023, the startup announced a $30 million investment led by General Catalyst, with participation from Northwell Holdings and several major health systems.
Space Technology30 – Planet LabsYear founded: 2010
Location: San Francisco, California, USA
What they do
Planet Labs operates a fleet of Earth-imaging satellites that capture daily, high-resolution images of the planet. AI is then used to analyze the imagery and track changes that may reflect deforestation, urban development, and climate change, among other applications.
This enables industries like agriculture, forestry, and disaster response to make informed decisions based on ever-changing environmental conditions—including in real time.
Planet Labs’ ability to provide near-instant insight into global changes has positioned them as a key player in climate tech and geospatial intelligence.
After going public via a merger with dMY Technology Group in December 2021, Planet Labs reached a valuation of $2.8 billion. Future growth will be fuelled by the increased need for actionable, real-time climate and related data.
31 – SpaceX
Year founded: 2002
Location: El Segundo, California, USA
What they do
SpaceX is redefining space exploration with reusable rockets and autonomous spacecraft. AI plays a crucial role in its operations, powering the autopilot systems that guide Falcon 9 rockets during navigation and landing.
These AI-driven systems process real-time data to execute precision landings on drone ships and ground pads. AI also supports spacecraft management, analyzing satellite data to enhance mission planning and efficiency.
While SpaceX does not disclose its financial data, industry analysts believe the company is worth around $350 billion—an impressive increase over an initial estimate of $210 billion in early 2024.
When one considers that the space economy is predicted to grow to $1.8 trillion by 2035, there is potential the Elon Musk-led company will reach a higher valuation in the near future.
To concludeThese 31 rising AI startups showcase the future of artificial intelligence across hardware, software, and industry-specific applications.
From breakthroughs in AI chip technology to advancements in healthcare, transportation, and creativity, the impact of these forward-thinking companies is set to reshape industries, streamline workflows, and redefine the role of AI in everyday life.
Recap: In This Issue!Convergence of Hardware and Software:Startups like CoreWeave, Cerebras, and Nvidia demonstrate how AI hardware innovation is pivotal for enabling more efficient and scalable AI applications.Simultaneously, software-focused companies like OpenAI, Anthropic, and Hugging Face are leveraging this infrastructure to push the boundaries of AI capabilities, especially in reasoning, creativity, and real-time applications.Agentic and Autonomous Capabilities:Companies such as Imbue, Adept AI, and /dev/agents are focusing on AI agents that can independently perform complex reasoning and task execution, moving beyond simple automation to proactive problem-solving and decision-making.This trend is a step toward realizing fully autonomous systems in various domains like customer service (Sierra) and enterprise workflows (Moveworks, Cognition Labs).Domain-Specific Innovations:Many startups are targeting specific verticals to solve niche problems, such as:Healthcare: Freenome and Abridge address early diagnosis and administrative burdens.Legal: Harvey and Evisort streamline contract management and legal research.Transportation: Waymo and Aurora focus on autonomous driving solutions.This highlights a trend toward tailored AI solutions for high-value, complex industries.Democratization and Accessibility:Companies like Mistral AI, Hugging Face, and Uizard aim to make advanced AI tools and models more accessible through open-source platforms, affordable pricing, and user-friendly interfaces. This fosters broader adoption across industries and skill levels.Cross-Industry Applications:AI startups are increasingly integrating their technologies across multiple industries:Planet Labs and SpaceX AI bring AI to climate monitoring and space exploration.Databricks and Arthur AI apply advanced analytics to enterprise data management, emphasizing the cross-sector relevance of AI.Focus on AI Safety and Alignment:Startups like Anthropic and Arthur AI emphasize ensuring that AI models operate reliably, ethically, and transparently. This focus is critical as regulatory scrutiny of AI systems intensifies.Expansion of Generative AI Beyond Text:Startups like Suno (music generation) and DeepL (translation and writing enhancement) demonstrate how generative AI is moving beyond text into domains like music, design, and visual creativity.Rapid Infrastructure Growth and Scaling:Companies like CoreWeave and Vultr highlight the growing demand for AI cloud infrastructure, which is essential for scaling AI applications. This is further evidenced by massive investments in GPU clusters and data centers.Real-Time and Contextual AI:Startups like Perplexity AI and Cognition Labs are advancing AI systems that provide real-time, context-aware insights and solutions, catering to dynamic environments like enterprise workflows and consumer search.Collaborative and Ecosystem-Oriented Models:Platforms like Hugging Face and LangChain promote collaboration by enabling developers to build on shared tools and frameworks, accelerating innovation within the AI community.With massive Gennaro Cuofano, The Business Engineer
The post 30+ Rising AI Startups to Keep An Eye On In 2025 appeared first on FourWeekMBA.
10+ Predictions For 2025
What are some major counter-intuitive trends that might play out in 2025?
I’ve already explained in detail how this AI paradigm’s next development cycle might play out (as a single-directional macro-trend) in the next 10-30 years.
The AI ConvergenceGENNARO CUOFANO AND FOURWEEKMBA
·
OCTOBER 31, 2024
Many people mistakenly view the current AI paradigm as “another new industry.”
Now, look at 2025 and some counterintuitive trends coming up.
Some of these comprise:
Cognitive Automation.Reverse Adoption of AI.Energy Innovation.Verticalization.Convergence of AI and AR.Outcome-based Business Modeling.AI Coaching.Infrustructure Paradigm.Reasoning Race.And why a tech with a lot of buzz might be a flop (for now).1×
0:00
-16:40
AI Agents Redefine AutomationIn 2025, AI-powered agents are set to be tested across many industries, potentially going from simple workflows to completely autonomous agents.
Image Credit: Anthropic AIThese intelligent agents will operate as interconnected systems, or “constellations,” working collaboratively under human supervision to enhance efficiency and productivity.
Deploying such agents is expected to fundamentally transform organizational structures and operational models, significantly improving service delivery and operational efficiency.
As we’ll see ahead, this might also push for creating new business models.
This era we’re going through might become similar to the Industrial Revolution, where we saw, throughout the 18th and 19th centuries, the emergence of a whole new system based on automating the assembly line.
With a core difference, this will be an “Intelligence Revolution,” where AI, as the “computing layer on top,” will enable a sort of “Cognitive Automation,” which will be imbued initially into a few core industries at lower value-add first, like customer support, and move its way up from there!
Read this for that:
The Era of Agentic AI!·
NOVEMBER 19, 2024
Generative AI Becomes Mainstream (For Enterprise)Generative AI is poised to transition from experimental applications to core components of business operations across various industries.
Image Credit: firstpagesage.comAn interesting take is while in the tech industry, we’re all quite aware of many alternatives to ChatGPT, as we’ve been playing with the APIs of many foundational AI providers; at a consumer level, ChatGPT leads the pack by a significant margin.
By 2025, as competition will intensify in the Generative AI space, these AI systems will be integral in creating personalized content, driving interactive storytelling, and enhancing user experiences in real time.
The integration will also move toward an enterprise level.
The counterintuitive take here is that, contrary to many other industries, this AI paradigm started with a massive consumer push (with the exposition of AI Chatbots), and only later did we get Enterprise running after it to understand how to adopt the technology.
Usually, a “gradual paradigm” moves from enterprise to consumer in a more linear cycle. Generative AI has come all the way around! Leaving many enterprise businesses unable, confused, and lost during this transition.
This is not a “gradual” but a “breakthrough” paradigm.
If you want to understand how we got here, I’ve explained in detail what things might look like under this new paradigm, starting from the company that was supposed to lead us in the AI era and that instead got (almost) crashed by it!
The Post-Google World·
DECEMBER 15, 2024
AI Infrastructure Will Lead To An Energy Innovation BreakthroughWhile you’ll hear many articles from established media outlets on how AI is wasting energy (in the short-term, it is), those are very good on the clickbaity side, but they miss the point.
The amount of resources, both in R&D and infrastructure development, the AI Race is spurring and will generate in the coming decade might, on the side of it, propel us toward some technological breakthrough when it comes to energy generation, storage, and distribution.
In short, the “wasted” energy AI is sucking right now might turn into a breakthrough in the coming decade!
We don’t know yet what this will be. Still, in The Rise of AI Data Centers, I’ve explained how major AI companies (hyperscalers) are parallelly investing in key areas, such as Nuclear energy for stable power and Liquid cooling for efficient data centers.
Read also:
The New AI Hardware Paradigm·
NOVEMBER 28, 2024
EdTech Is Dead, EdAITech Is On!AI has posed a significant threat to the entire edtech ecosystem.
Global edtech investment fell to $3 billion in 2024, the lowest in a decade, as generative AI tools like ChatGPT and LearnLM disrupted the sector. While companies like Duolingo thrived, others like Chegg and Coursera struggled.
Image Credit: The Financial TimesThe counterintuitive take here is that while AI has killed for promising the “edtech” industry, it was defined a decade back (the paradigm was “get the expensive education you get from a higher level institution, into a packaged cheap version of it”), there will be the chance for edtech to embrace it, to become something else.
As generative AI exploded, edtech (as it has been defined in the last decade) has turned out to be a transitional sector.
Yet, integrating AI into existing edtech business models will play out, depending on the paradigm each company is running with. For instance, players like Stack Overflow and Chegg have been hit the most by it.
Other players who embrace AI will be able to redefine their core with it, thus experiencing a massive resurrection.
For one thing, AI might make it possible to offer personalized education to billions of people worldwide.
Wasn’t this what the payoff of edtech was supposed to be in the first place?
The Humanoid Cycle Will Be Enterprise-LedAs I’ve explained above, Generative AI, contrary to many other industries, has evolved out of a mass consumer application (AI Chatbots) and is now working backward to enterprise.
Well, humanoid robotics will move in the opposite direction.
As I said, “a breakthrough paradigm” moves from consumer backward to enterprise. While a “gradual paradigm” moves from enterprise to consumer.
Here, “breakthrough” or “gradual” isn’t about the societal impact of the technology (indeed, humanoids might have a much more significant impact on it) but rather the technological progression of that technology.
Thus, many might envision the next humanoid in the home. The progression there will go from enterprise, with assembly line humanoids, working its way back to the consumer.
The Rise of Embodied AI·
DECEMBER 3, 2024
Verticalization of AIAs soon as ChatGPT came out, it was clear to me that a lot of the value in applying AI to everything was in its “verticalization” or capabilities to have AIs that could go from generalists (like ChatGPT is) to specialized generalists (like an AI Accountant, HR, CS, Support rep and so forth).
Well, that’s where we’re going. The counterintuitive take here is that while foundational companies can offer a horizontal platform to build anything on top of it, it will be quite valuable. My take is among the most valuable companies; there will be the verticalized players.
Or those who can tackle an entire vertical with their AI.
In AI Moats, I explain why this matters:
AI Moats·
OCTOBER 10, 2024
AI Reasoning WarsThe most interesting aspect of the coming wave of “Reasoning AI” is that while we might have figured out a new scaling paradigm, for now, it is also coming at impressive computational costs.
Indeed, OpenAI’s o3 model, while it demonstrates major advancements in AI scaling with a record 88% on the ARC-AGI test (designed to test more human-like intelligence), also came at extremely high compute costs (the high-scoring o3 comes at $10,000 per computation), making it impractical for everyday use, as of now.
However, advancements in cost-efficient AI inference chips (e.g., Groq, MatX) could help make test-time scaling more economical, improving accessibility for institutions and high-value applications before this would get ready for mainstream adoption.
The competitive landscape of AI development is expected to intensify in 2025, with leading AI developers striving to enhance their models’ reasoning capabilities.
Yet, a fascinating, counter-intuitive take is the “AI reasoning” layer, which is becoming embedded in large-scale applications. When it comes to the most advanced level, it might be progressing from enterprise to consumer, as only a few major enterprises will have the financial resources to enable very complex reasoning to solve very complex, valuable tasks or find the answer to very complex quandaries (e.g., you’re a Wall Street firm trying to outsmart everyone else, you might spend millions only to test AI reasoning on a single, complex question! Why not try your shot there? It might be worth a billion if you make it right.)
To understand the AI Reasoning Race, read this:
Beyond Search: The AI Reasoning Race!·
DECEMBER 21, 2024
Rise of AI Data Centers with Specialized GPUsAs we move toward 2025, a critical take is that the GPU is no longer relevant as a single component and architecture.
Why? While the first wave of AI has been built on top of the current GPU architecture, it enabled this first phase of scale to train and serve these AI models well.
Yet, now, not only is a specialized GPU critical (AI GPU), but a whole new architecture for data centers is as important – if not more important – to enable AI models to run on mass consumption/enterprise workloads.
Building a robust and competitive AI supercomputer with NVIDIA’s Blackwell architecture might require 40,000 GPUs, plus all the hardware pieces needed to make them into an AI Supercomputer.
Thus, CapeX needs $3 billion even to enter the hyperschaler space.
Outside that, a key take here is that we’ve passed well over the GPU; right now, not only the AI GPU is critical, but the core, the whole architecture on top, is the real hardware paradigm!
That is why, when NVIDIA launched Blackwell, it wasn’t launching a single GPU but an entire AI Supercomputer!
That is also why when Google is working on its AI Chip, Trillium, the company isn’t focusing on the single chip but on the integration of these into an AI Hypercomputer, which is instrumental to training Gemini 2.0 and used in Google Cloud to support both the integration of Gemini across the Google’s suite of products and to enable other companies to leverage these capabilities.
That represents the core of Google’s AI strategy.
Read Also:
The Rise of AI Data Centers·
NOVEMBER 21, 2024
Convergence of AR and AI HardwareIt took us a decade (and more) to finally get into the moment when AR might be crossing the chasm of mass adoption. And that is all thanks to the current AI paradigm.
Again, I’ve touched this repeatedly, but AR is one of these industries that, thanks to AI, might finally become commercially viable for the first time after some major failed attempts in the last decade (do you remember Google Glasses?).
Indeed, as we enter 2025, Meta plans to add displays to its Ray-Ban smart glasses by 2025, accelerating its AR strategy.
The updated glasses will show notifications and AI responses, complementing Meta’s Orion AR prototype.
Despite challenges like costs and scalability, Meta aligns its AI and AR efforts to dominate next-gen computing platforms.
AR, as a platform, will be a core part of Meta’s AI Strategy.
And Meta isn’t alone there.
Indeed, the fact that Google might be getting ready with a smart glasses release in 2025 also seems clear from the hiring ramp-up the company has been pushing this year, as it hired more than 100 staffers at AR firm Magic Leap.
The arrangement with Magic Leap will help Google work in parallel on underlying AI assistant, operating system, and hardware device for the next AR race, especially vs. Meta, which is trying to establish itself as the top player there.
If it’s unclear, Project Astra will be the underlying AI assistant powering up future models of Google’s smart glasses.
Of course, Apple is also the giant there if it successfully transitions from the iPhone to a successful AR device.
Let’s keep both our eyes on this.
Read Also why AR will matter in Google’s AI Strategy:
Google’s AI Triad·
DECEMBER 12, 2024
Rise of Outcome-Based Business ModelsAs AI systems become integral to enterprise operations, there will be a shift towards outcome-based business models.
Companies will move from traditional consumption-based pricing to models that charge based on achieved results, such as tasks completed or processes optimized by AI agents.
This transition will redefine value measurement in business transactions, aligning costs with tangible outcomes and performance metrics.
The counter-intuitive take here is it won’t be as simple as it seems. Outcome-based business models will require a whole consumer ecosystem to develop. That is why enterprise might be a great setting stage for this to happen in the coming years. It will enable the testing of different sets of outcome-based business models depending on very custom commercial use cases. Those who will scale from there to enter the B2B world might be good candidates for reaching consumer-based outcome business models.
Read Also:
Emerging AI Trends·
DECEMBER 8, 2024
Quantum Computing Will Be A Flop (for now)I’m closing the piece with a pessimistic prediction. With all the excitement around Quantum Computing, as Google unveiled Willow (I was also excited about it), this was presented as a breakthrough:
https://www.youtube-nocookie.com/embed/W7ppd_RY-UE?rel=0&autoplay=0&showinfo=0&enablejsapi=0Yet, I believe this might be a big flop as we go into the next 2-3 years, as the time it might take for us to get something quite interesting, at scale, from Quantum computing will be in the coming decade or two.
Indeed, in the AI Convergence, I’ve explained how, in the next 10-20 years, we might see something super interesting from the encounter of AI and new technologies that never managed to cross the chasm, and Quantum Computing is there, too.
But not for now, not in this decade.
Recap: Some of The Key Takes In This Issue!Automation Moves Beyond Physical to Cognitive TasksAI systems transition from automating assembly lines to automating complex cognitive and organizational workflows.Reverse Adoption Paths
Technologies like generative AI and humanoid robotics exhibit contrasting innovation paths: generative AI shifts from consumer to enterprise, while robotics starts in enterprise and moves toward consumer applications.Energy Innovation as a Byproduct of AI Growth
The intense energy demands of AI spur breakthroughs in energy generation, storage, and efficiency, positioning AI as a catalyst for sustainability innovation.Industry-Specific AI Verticalization
Specialized AI systems tailored for specific industries (e.g., healthcare, finance, supply chain) gain prominence over general-purpose platforms.Convergence of Emerging Technologies
AR, AI, and specialized hardware intersect, enabling transformative consumer and enterprise devices that blend immersive experiences with intelligent interfaces.Cost-Driven Technological Balance
Rising computational costs of advanced AI models drive innovation in efficient hardware and software, emphasizing scalability and affordability.Outcome-Oriented Business Models
Enterprises pivot from usage-based pricing to outcome-based models, redefining value measurement in AI-driven workflows and enterprise ecosystems.The Evolution of Education through AI
Traditional edtech fades as personalized, AI-driven education platforms emerge, promising scalable, tailored learning solutions.New AI Infrastructure Paradigms
AI data centers become critical hubs with specialized GPUs and architectural innovations, forming the backbone of scalable, next-gen AI applications.Competitive AI Race in Reasoning and Specialization
The race to enhance AI reasoning capabilities intensifies, with foundational players investing in specialized reasoning models for enterprise and high-value applications.
With massive Gennaro Cuofano, The Business Engineer
The post 10+ Predictions For 2025 appeared first on FourWeekMBA.
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