10 Predictions for 2025

1. Large Language Models (LLMs) as a Standard Tool

LLMs will not replace software engineers, but they’ll become a standard, “must-have” tool in every engineer’s toolbox. The reasoning behind that is that while LLMs possess knowledge akin to a junior developer, they still struggle with large, complex codebases—current tools like Cursor or Devin serve more as PoCs than fully operational enterprise solutions. The implications are that junior engineering roles may shrink as LLMs handle simpler tasks, but engineers will continue to be vital for designing architectures, ensuring domain-specific logic, and maintaining complex systems. We will also see more tools that provide integrations with multiple LLMs and APIs such as LiteLLM, LangChain, IntelliNode and Gateway.

2. Rise of Humanoid Robotics in Industry

Humanoid robotics will begin to penetrate the industrial market in a more practical way, delivering a tangible return on investment rather than just viral demonstrations of dancing or backflips. The reasoning behind that is that industries need reliable, cost-effective automation solutions that can integrate safely alongside humans in manufacturing or logistics. The implications are that companies will increasingly adopt specialized humanoid robots for tasks that are repetitive or hazardous, thereby reducing labor costs and improving operational efficiency. Industrial digital transformation 5.0 will start to see a rise in 2025 as more IoT is embedded in industrial facilities, making humanoids and UAVs inside factories (especially in big sectors like automotive and warehousing) more feasible and cost-effective to collaborate with humans. I believe also the regulations in America will become more open to them.

3. VCs Shift Toward AI-Proven Startups and Hardware

Venture capitalists will increasingly fund startups that focus on hardware or “AI-proof” technologies, especially where AI has yet to fully penetrate. The reasoning behind that is as the software side of AI reaches a saturation point, investors seek the next frontier—solutions that require specialized hardware, robotics, or embedded systems. The implications are that early-stage companies tackling sectors like customized chip design, robotics, and new forms of edge computing will capture more attention and capital.

4. Infrastructure (DevOps, MLOps) Takes Center Stage

Infrastructure-related roles—like DevOps and MLOps—will become even more critical as tech giants like NVIDIA, Amazon, and Google pour resources into supporting AI workloads. The reasoning behind that is training, deploying, and maintaining large models requires robust compute, storage, and networking capabilities, which in turn demands specialized infrastructure to handle these demands. The implications are that skilled professionals in infrastructure and operations will be essential for designing scalable, cost-effective architectures that enable cutting-edge AI innovations. Also we could see a rise in on-premise data centers as more companies tend to want their data absolutely in their hands as the data is starting to become a commodity more clearly.

5. Continued Buzz Around AI “Agents” and AI tools

AI-driven “agents” will remain a hot topic, evolving into specialized tools that tackle very specific tasks. The reasoning behind that is the success of general-purpose agent-like solutions—such as GitHub Copilot—has demonstrated how much productivity can be gained with focused, automated assistance. The implications are that we’ll see agents for everything from Database manipulation and monitoring (PeepDB, NetData), CI/CD pipelines (ResolveAI) to finance and insurance underwriting, with niche markets flourishing as they address highly targeted industry problems.

6. Sophistication in Database Tools and SaaS

Database tools and Software-as-a-Service (SaaS) platforms will evolve rapidly to meet the growing demands of AI, focusing on automation, speed, and more intelligent data handling (cool niche examples are ClickHouseDb and TileDB). The reasoning behind that is companies are collecting and processing unprecedented volumes of data, creating a need for sophisticated storage, retrieval, and analytics solutions capable of integrating machine learning directly. The implications are that vendors will compete on features like automatic data cleaning, real-time analytics, and tighter security, driving innovation in the data management and SaaS sectors. Security will become a bigger priority, and we’ll likely see new tools specifically designed to safeguard data and ensure it’s safely available for AI.

7. More Layoffs in Big Tech

The wave of layoffs in Big Tech is not over…… it will persist as companies lean into AI for cost savings and efficiency gains. The reasoning behind that is the global economic environment remains uncertain, and firms are eager to cut costs while maintaining or even increasing output via AI-enhanced workflows. The implications are that hiring practices will shift toward roles that directly benefit from or bolster AI initiatives, potentially leaving some roles vulnerable while sparking demand in areas like advanced ML, DevOps, and robotics.

8. Growing Demand for Embedded Software Engineering

Embedded software engineering will experience a boom as more hardware-centric solutions and IoT devices become integral to AI-driven systems. The reasoning behind that is that edge computing, robotics, and consumer electronics all require specialized firmware that marries power efficiency, real-time performance, and data processing, which makes it a more AI-Proof sector. The implications are that we’ll see a surge in demand for engineers with low-level programming expertise(for languages like: Rust, C++, LUA) real-time operating system skills, and hardware-software integration know-how.

9. A Renewed Crypto Boom

Cryptocurrency will experience another surge, spurred on by ongoing geopolitical uncertainty and a U.S. administration that may be more receptive to digital assets. The reasoning behind that is that in times of financial instability, many investors turn to crypto as a high-risk, high-reward opportunity, while Bitcoin continues to gain traction as a global hedge. The implications are that regulatory environments may shift, traditional financial institutions could expand crypto offerings, and blockchain technology might see renewed development interest. I believe also we will see more adoption with CBDCs.

10. Defense Sector Boom

Defense and military sectors will continue their boom, driven by heightened global tensions and technological advancements in unmanned systems. The reasoning behind that is that nations are actively modernizing their arsenals with drones, autonomous submarines, and surface vehicles, reflecting a shift toward high-tech warfare capabilities(check this book for more info: The Kill Chain: Defending America in the Future of High-Tech Warfare by Christian Brose. The implications are that defense R&D will intersect with AI, robotics, and embedded systems, leading to further innovations and significant procurement contracts, particularly in countries following the U.S. playbook for advanced defense strategies.

Disclaimer:

I did not receive any money or incentives for mentioning the sites and tools referenced in this article. These are purely personal thoughts and suggestions. (with exclusion of PeepDB, which I am Core Contributor)