Predictions for 2025
At Predli, we expect 2025 to open new pathways for AI to become a more natural part of everyday life and business. Agents will drive AI’s evolution beyond chat, automating specialized workflows and embedding into operating systems and tools. This shift will unlock advanced voice interfaces, smarter contextual automation, and fundamentally new ways of working and living.
This wave of innovation is also reshaping business models, as foundational breakthroughs in AI begin to challenge the dominance of traditional SaaS platforms. Organizations are increasingly turning to bespoke, AI-driven solutions that align more closely with their needs. Meanwhile, the surging energy demands of these technologies position nuclear power as a cornerstone of sustainable growth.
As these advancements gain momentum, they will also bring heightened risks, including AI-driven misinformation, emerging security threats, and legal battles over data and intellectual property. Sovereign AI clouds will emerge as another key trend, sparking debates over data ownership and governance. This article explores the transformative trends, risks, and innovations set to define the AI landscape in 2025 across its foundational technologies, the evolving business and industry landscape, and its impact on the wider society.
AI Foundations
1. The Year of the Agents: Beyond the Chat Interface
2025 marks a pivotal year for AI, as the era of "agents" moves beyond traditional chat interfaces. While ChatGPT introduced the general public to the potential of LLMs, next year brings a transition to specialized agents and agentic workflows capable of tackling specific, end-to-end tasks. Advanced LLM pipelines will automate tasks that once required extensive manual work. Agentic systems can already now generate SQL code, regex patterns for parsing and leverage domain-specific tools. While refining these pipelines remains challenging, ongoing advancements bring us closer to seamless automation for highly specialized use cases.
Specialized Workflows in Action
LLMs are being embedded into specific workflows to automate labor-intensive processes, unlocking new efficiencies. Examples include:
• Regulatory filings: Applications are emerging to handle complex filing requirements such as SEC filings, patent applications, FDA documentation, import declarations, GDPR and REACH compliance reports (EU).
• Government automation: Startups highlighted in the YC Request for Startups 2025 are those building LLM-powered tools for tasks like application reviews, form filing, and document summarization.
• Document processing: Transforming unstructured data into structured outputs is a growing opportunity
2. Bringing AI to the Core: Agents and LLMs in Operating Systems
In 2025 we will take a transformative step in integrating large language models (LLMs) directly into operating systems, both for computers and mobile devices. Apple Intelligence and Anthropic’s Model Context Protocol (MCP) have introduced early iterations of what could become a standard in personal computing. Still in its infancy, Apple Intelligence and the MCP still remain rather limited. As development accelerates, the integration of LLMs into operating systems will redefine how users interact with their devices, making tasks more intuitive, personalized, and efficient.
3. Blurring the Lines Between Agents and LLMs
We will likely see that the distinction between agents and LLMs will become increasingly blurred as frameworks and model architectures evolve to integrate multiple models into cohesive systems. Emerging approaches, including Mixture of Experts (MoE) systems, demonstrate how smaller, specialized models can work together under a unified router to behave like a single, highly capable model. This concept mirrors ensemble methods where individual components specialize in specific tasks while collectively enhancing overall performance.
Frameworks and model architectures designed for such setups are making it easier to route queries dynamically, selecting the best model for a given task based on context, much like how many of the agentic workflows are operating today. By combining multiple smaller models into a unified system, these frameworks optimize resource usage and improve task-specific accuracy. This evolution is poised to redefine how we think about agents and LLMs, blending their capabilities into seamless, modular solutions that offer enhanced efficiency and flexibility.
4. RAG beyond the Vector Database
As demand grows for agentic workflows that require a more holistic understanding of data, traditional Retrieval-Augmented Generation (RAG) workflows tied to vector databases are proving insufficient for many complex use cases. While advancements like Hybrid Search and HyDE Search improve the breadth of data collection, they often fail to capture the nuanced relationships between entities distributed across a content store.
Emerging approaches like GraphRAG and Lazy-GraphRAG, released by Microsoft earlier this year, address these limitations by enabling traversal of private datasets with structured relationships. These methods model and expose connections between data entities, offering a more comprehensive view of the information landscape. This capability is particularly valuable for workflows where understanding the intricate interplay of data relationships is critical, such as knowledge management, private research repositories, and complex enterprise systems. In the next year, we anticipate more solutions being built on GraphRAG, challenging the dominance of traditional vector databases that has prevailed in recent years. This is a topic Predli is particularly excited about, as we’ve been actively exploring and working on solutions in this area. If you’re interested in diving deeper or discussing how these approaches could benefit your workflows, we’d love to connect.
5. Giving Voice to AI
Voice models made significant strides in 2024, yet most business use cases still revolve around standard chatbot interfaces. In 2025, we expect broader adoption of voice models across industries, enabling more natural and interactive customer experiences.
Try out Predli Voice Agent here.
6. Model Migrations: Simplifying Transitions in a Multi-Model World
As the AI landscape diversifies with more commercial and open-source LLM providers, seamless model migration is becoming increasingly important. Tools like LiteLLM enable flexible integration of multiple models, making it easier to evaluate and switch between providers as the ecosystem evolves.
Model migrations are also essential for addressing end-of-life model support and adopting newer versions. Stable migration frameworks ensure smooth transitions without disrupting workflows, while updating models often necessitates refining prompts and parameters to align with expected behaviors. Solutions like Narrow AI assist in optimizing prompts to maintain consistency and prevent regressions.
The ability to efficiently manage migrations and updates ensures organizations can adapt to emerging technologies while maintaining reliable performance. These frameworks are critical for navigating the rapidly growing AI ecosystem.
7. Expanding Use Cases for Transformer Architectures Beyond Current Modalities
In the next year, we will continue to see the transformer architecture finding transformative applications across industries beyond NLP. For example, in time-series forecasting, attention models excel at identifying key temporal patterns, improving predictions in areas like stock market trends, patient health monitoring, and energy demand forecasting. Similarly, in cybersecurity, attention-based systems analyze network traffic and map threat relationships, enhancing real-time vulnerability detection and proactive defense. While models and tools for these applications already exist, they are likely to see broader adoption as the technology matures and becomes more accessible.
Business and Industry Impact
8. Advertising in AI responses
AI-generated responses are likely to follow a trajectory similar to early search engines, where clean, utility-focused outputs gradually gave way to ad-driven content. Just as Google began as a simple search tool before evolving to include ads, AI response models, especially those with web search capabilities, could soon face similar pressures.
We anticipate the rise of prompt injection/SEO-inspired techniques/jailbreaks, where entities attempt to influence model outputs for promotional or adversarial gain. As AI systems increasingly integrate with the web, these vulnerabilities may be exploited to sway responses, mirroring the evolution of search engine optimization tactics.
Additionally, partnerships between AI developers and firms like Amazon could pave the way for embedded advertising in AI responses. This could include product recommendations or sponsored content seamlessly woven into conversational outputs. While this might enhance monetization for AI platforms, it raises important questions about transparency, user trust, and the balance between utility and commercialization.
9. SaaS Business Model Challenged by Single Use Software
The traditional SaaS business model, long driven by high gross profit margins and economies of scale, is facing increasing pressure as advancements in AI and development tools shift the balance in the buy-vs-build equation. With gencode tools like lovable, cursor, and replit, the barriers to building custom software are rapidly falling. Companies are finding it faster, cheaper, and more efficient to develop solutions in-house rather than relying on costly SaaS subscriptions, which often requires integration work with their existing enterprise systems. This shift challenges the 95% gross profit margins that have been a hallmark of the SaaS industry, paving the way for bespoke, agile, and task-specific applications that prioritize efficiency and rapid development over scale.
10. The Rise of New Foundational Model Providers
The landscape of foundational AI model providers continues to expand rapidly. Companies like ElevenLabs, founded in 2022, have gained significant momentum, becoming pivotal players in the ongoing AI boom. Amazon, historically a leader in AI through AWS’s compute offerings, recently entered the foundational model space by releasing its first open-source model, and Alibaba released their QwQ model in November this year.
This dynamic market is ripe for the emergence of new foundational model providers. Recent entrants like Mistral AI, founded in April 2023, demonstrate the speed at which innovation and investment are reshaping this space. While the barriers to developing competitive models are high, we anticipate more companies will rise to challenge the established giants in 2025.
As scaling laws increasingly show diminishing returns, the need for innovative architectures is becoming clear. Recent efforts like Solar, developed by Upstage AI, and Mamba, introduced by Albert Gu and Tri Dao, have sought to move beyond Transformer-based designs but struggled to gain widespread traction among the AI community in 2024. Despite this, we anticipate that 2025 will see the emergence of new foundational models built on entirely novel architectures, as the AI field shifts its focus toward smarter, more efficient designs and smaller labs seize the opportunity to lead the way.
11. Compute Cost to Decrease as Cloud Incumbents Face Rising Competition
High compute and cloud costs have become a pressing concern for many in the AI space. While major cloud providers dominate, the market is seeing a surge in competition from emerging players offering cost-effective alternatives.
• Barriers to Switching: Despite high costs, many businesses remain locked into incumbent providers due to the complexity and risk of migration. Providers often bundle services into one-stop solutions, making it harder to explore alternatives.
• Emerging Competitors: Companies like Modal, Predibase, and TogetherAI are challenging the status quo by offering similar services at discounted rates. Advances in frameworks like TEI, TGI (for inference), and tools like Axolotl (for fine-tuning) have simplified deploying and managing models, lowering the entry barrier for smaller competitors.
• Cost-Efficient Fine-Tuning: With LoRA adaptors, hosting fine-tuned models has become significantly cheaper, as only adaptor weights need to be stored. This trend is transforming both the cost and accessibility of fine-tuning and deployment.
• Decentralized Solutions: Firms like the Akash Network are introducing decentralized compute orchestration, creating a potential spot market for compute. These solutions help address underutilization of GPUs and drive efficiency in resource allocation.
With more containerized and modular approaches to compute, the commoditization of cloud infrastructure is accelerating. Simplified frameworks, increased competition, and decentralized solutions are poised to make switching providers easier, driving costs down and creating a more competitive landscape in 2025.
12. Sovereign AI Clouds to Drive Demand for Data Centers and GPUs
The rise of sovereign AI clouds, designed to meet local data sovereignty and regulatory needs, is driving a surge in demand for data center capacity and GPUs. Nations and industries are adopting localized AI solutions to comply with privacy laws like GDPR and emerging AI-specific regulations such as the EU AI Act, which imposes strict requirements for data quality, transparency, and risk management in AI systems. These frameworks are pushing organizations to adopt infrastructure that ensures compliance while safeguarding sensitive data. This trend is leading to significant investments in localized data centers, increasing the substantial demand for high-performance GPUs over the coming years.
Society and AI
13. Proof of Personhood in a post Turing World
As AI-generated text, voice, and video become indistinguishable from human output, verifying authenticity is increasingly challenging. The need extends beyond proving a person’s identity to ensuring that media, whether text, images, or videos, is human-generated and not AI-manipulated, especially in critical areas like political elections and combating misinformation.
Potential solutions include decentralized identity systems, media provenance tracking with cryptographic signatures, and AI watermarking tools. Developing these systems will be essential to maintaining trust in a digital world dominated by sophisticated generative AI, and we anticipate that more funding will go to companies working in this space in 2025.
14. Legislation and AI: Navigating New IP Hurdles
As AI adoption accelerates, legislative challenges around LLM training data are becoming more prominent. Platforms like Reddit have already implemented monetization strategies for training data access, but broader legal frameworks are expected to emerge, imposing stricter controls on data sourcing for AI models.
Court cases like ANI vs OpenAI in India are likely to set important precedents, shaping how global AI policies evolve. Additionally, the pushback from creative professionals, exemplified by the Writers Guild of America strike in 2023, highlighted concerns over the use of AI in Hollywood, including its potential to replace or exploit human creativity.
With the release of OpenAI’s Sora model, which focuses on visual arts generation, these concerns have only intensified. The capabilities of generative AI have advanced even further than many anticipated, raising pressing questions about its role in industries like film, art, and design. These models, often trained on the works of the very creatives they now threaten to displace, have left many artists, writers, and designers grappling with the reality of being undercut by technology built on their own contributions. In 2025, we expect creative workers and IP owners whose data has been used without proper licensing to intensify legal challenges and protests against foundational model providers.
15. Risks in a More Sophisticated AI Landscape
As AI technologies advance, the sophistication of adversarial agents is increasing, creating an urgent need for new tools, methods, and legislation to address emerging threats. Scamming attempts are expected to become more intricate, leveraging AI-driven capabilities such as voice impersonation and social engineering. Scammers already map potential victims using open information sources like social media, and with AI, they can now impersonate close relatives or trusted individuals with alarming accuracy. This puts vulnerable populations, particularly the elderly, at heightened risk.
To mitigate these threats, increased public training and awareness campaigns are essential, especially targeted at those most at risk. Innovative solutions, such as AI-driven "honeypots" (e.g., an AI Granny to counter scammers), could serve as deterrents while also collecting data to improve defenses. Additionally, there is a growing need to re-evaluate how sales and financial transactions are conducted over the phone, online, and through other vulnerable channels.
Payment solution providers also play a critical role in this fight. Companies like Mastercard, which recently acquired Recorded Future to enhance threat intelligence, are likely to continue such strategic moves to stay ahead of adversarial innovations. As these threats escalate, acquisitions and investments in AI security by financial players will become increasingly necessary.
On a broader scale, the divergent regulatory approaches of the EU, US, and China pose additional risks. The EU’s more stringent regulatory stance on AI, while prioritizing safety and ethics, could create a competitive disadvantage compared to the more flexible innovation environments in the US and China. This regulatory rift may further deepen as AI technologies proliferate, potentially impacting global competitiveness and collaboration.
16. Energy Consumption in Focus
Carbon Footprint Scrutiny Meets Energy-Intensive Tech: The energy demands of AI models, as well as cloud computing, and blockchain technologies (e.g., Bitcoin) will keep drawing criticism for their environmental impact.
Carbon Intense but Lower Regulatory Risk in the U.S.: Despite growing concerns about the energy demands of AI models, the U.S. administration’s dismissive attitude toward energy-related regulation reduces the risk of regulatory oversight in this space. However, in markets like the EU, stricter policies and energy accountability standards heighten regulatory risks.
Nuclear Energy Renaissance: As tech companies seek sustainable energy solutions, nuclear power is emerging as a key player. Partnerships like Google’s collaboration with Kairos Energy and Microsoft’s deal with Constellation Energy highlight a shift toward more actively using nuclear reactors to power data centers and reduce carbon footprints. Andreessen Horowitz’s Big Ideas in Tech 2025 underscores the resurgence of nuclear energy in the U.S. market. Meanwhile, Europe is expanding its nuclear ambitions on national levels: France continues to lead in adoption, the UK is planning its largest nuclear expansion in 70 years, and countries like Hungary, the Czech Republic, and Poland are investing in new nuclear plants.
The Rundown
AI Foundations
1. The Year of the Agents: Beyond the Chat Interface
• Frameworks to watch: AutoGen, LangGraph
2. Bringing AI to the Core: LLMs in Operating Systems
• Initiatives to Watch: Apple Intelligence, Anthropic’s MCP
3. Blurring the Lines Between Agents and LLMs
• Deepdive: MoE
4. RAG Beyond the Vector Database
• Initiatives to Watch: Microsoft GraphRAG
5. Giving Voice to AI
• Hear it out for yourself: elevenlabs, vapi, Google’s TTS, OpenAI’s TTS
6. Model Migrations: Simplifying Transitions in a Multi-Model World
• Tools we use: LiteLLM, unify, Narrow AI
7. Expanding Use Cases for Transformer Architectures Beyond Current Modalities
• Companies exploring new modalities: stability.ai, nixtla, suno, udio
Business and Industry Impact
8. Advertising in AI responses
• Actors: All foundational model providers.
9. SaaS Business Model Challenged by Single Use Software
• Tools to build fast: lovable, cursor, replit
10. Compute Cost to Decrease as Cloud Incumbents Face Rising Competition
• Challengers to watch: Modal, Predibase, TogetherAI, Akash Network
• Tools streamlining AI development: TEI, TGI, Axolotl
11. Sovereign AI Clouds to Drive Demand for Data Centers and GPUs
• Initiatives to watch: EU AI Act, IndiaAI, CGK4 AI campus, Project Transcendence
12. The Rise of New Foundational Model Providers
• Companies to watch further: xAI, Alibaba, Amazon, Upstage
Society and AI
13. Proof of Personhood in a Post-Turing World
• Themes to watch:
- Decentralized Identity and Proof-of-Personhood Systems
- Digital Identity Verification
- AI Watermarking Technologies and Detection Systems of Deepfakes
- Media Provenance Tracking
14. Legislation and AI: Navigating New IP Hurdles
• Court cases to watch: ANI vs OpenAI, Canadian news publishers vs OpenAI, GEMA vs OpenAI, RIAA vs Suno and Udio
15. Risks in a More Sophisticated AI Landscape
• Companies to Watch: Payment operators,Traditional Cyber firms, Stripe
• Emerging AI players: Abnormal Security, Arkose Labs, Cybereason, ZeroFox, anch.ai
16. Energy Consumption in Focus
• Key Players and Initiatives to Watch: Microsoft, Google, AWS, Meta, Constellation Energy, Kairos Power, Energy Northwest, Oklo, NuScale Power, co2ai.com
