Tagged: AI Coding
AI Agents Can Write Code, Here’s How We Win as Developers
This is a thought exercise and a biased prediction. I have no real facts except what I see happening in the news and observed through my experiences. I don’t have any proof to back up some of my predictions about the future. So, feel free to disagree. Challenge my position, especially when I try to blow up the rockets in the end.
The Game has Changed
We don’t need to write C#, Python, or Java to build software anymore. Just like we no longer need to code in assembly or binary, today’s high-level languages are now being pushed down a level. We can code by talking to an AI agent in plain English. This isn’t science fiction. AI agents are here, and they’re disrupting traditional software development. The value isn’t in writing code, it’s in delivering value and desired business outcomes.
Soon, every app can be basically copied by an agent. Features don’t matter, value does. This means the future isn’t about who can write the best code or build the best feature set. Any product developer with an agent worth its silicon will be able to write an app. For product developers, it will soon be about who can use AI agents in a way that actually delivers business value. Those developers that have Agentic: Ops, Design, Development, Infrastructure, and Marketing will beat those without. Those with agents and experienced agent operators that deliver value rapidly will beat the developers that still take 3 months to deliver an MVP.
AI is No Longer Just a Tool, It’s the New Coder
AI assistants won’t just be assisting developers, as I once thought, they will become the developers, the designers, the marketers, the project managers. The shift isn’t about writing code faster. It’s about not writing code at all and letting AI generate, deploy, and optimize entire systems. How do we manage AI agent employees? An AI HR agent? The implications are far wider than just the replacement of humans in developer roles. Markets are going to shift, industries will be disrupted regularly, the world is going to enter a new age faster than any other shift in civilization that we’ve had in the past. I may be wrong, but it looks clear to me.
What does that mean for us?
- The focus moves from software development to AI agent development and integration.
- Companies that figure out how to deliver value with agents effectively will dominate product development.
- The winners will have an early advantage building a proven system, with tested agents, and experienced agent operators that customers will trust to continuously deliver desired value.
Product Features are Dead, Value Delivery is Everything
If an AI agent can copy any feature, what really matters in product development? Value delivery, that’s what. Value has always been king and queen in product development. I believe it’s more important now than ever. AI-native product developers will outperform traditional ones not only because they don’t waste time or money on manually coding features, but they focus on outcomes and delivering value that deliver those outcomes.
Hell, I’m seeing people that can’t code build apps that use to take weeks to build. They can build an app in 30 minutes, and we are still on v1 baby agents. What happens when the agents grow up in a couple years. In the future time won’t matter because we can deliver apps and features in day. Costs become less of a concern because agents cost less than hiring new employees. Understanding and delivering value will be the great divider between product development teams. Those who can wield agents to understand and deliver value will do better in the market.
China and the team that built DeepSeek proved that they can beat the likes of multi-billion dollar US aligned companies with less than $10 million to train a frontier model. What will someone with a team of agents delivering value in days do against an old school team of human developers delivering the same value in months.
Think about it 🤔
Businesses don’t care if the back end is in Python or Rust. They care if revenue goes up and costs go down.
Customers don’t care if their data is in PostgreSQL or SQL Server. They care if their system is performant and costs are feasible.
Users don’t care if the UI is React or Blazor. They care if the experience is seamless and solves their problems.
No one asks whether an AI agent or human wrote the code, they just want a solution that fills their needs.
A product development team’s value is not in their technology choices but in the value they can deliver and maintain.
The AI-Native Product Development Playbook
If AI replaces traditional software product development, how do we compete? We learn to not focus on coding features, we build AI-driven systems that can deliver value.
Here’s A Playbook 🚀
1. Find the pain points where AI delivers real value. Optimize workflows, automate decisions, eliminate inefficiencies, increase customer attraction, acquisition, engagement, retention, and satisfaction.
2. Use rapid prototyping to test and iterate at breakneck speed. Don’t waste weeks and months building, when we can ship, test, and refine in days.
3. Orchestrate AI agents. Until AI surpasses AGI (artificial general intelligence) and reaches super intelligence, initial success won’t come from using a single agent. It will come from coordinating multiple agents to work together efficiently.
4. Measure and optimize continuously. The job isn’t done when a system is deployed. AI needs constant tuning, monitoring, and retraining.
People Still Want Human Connection
There’s one thing AI agents can’t replace, human relationships. People will always crave trust, emotional intelligence, and real connection with other humans. Businesses that blend AI automation with authentic human experiences will win.
The Future of Software Product Development is AI-First, Human-Led
This isn’t about whether AI will replace traditional software product development or developers. That ship is sailing as we speak, it is underway. The real question is who will successfully integrate and optimize AI in businesses? Who can help build AI-native businesses that out compete their competitors? I hope the answer is you. The future is AI-first. Those who embrace it will lead. Those who resist will be left behind because we are the Borg, resistance is futile.
Now, my last question is, are you ready? Do you know how to transform now? Evolution is too slow. You must blow up some rockets to rapidly figure out what works and doesn’t work. But doing so is easier said than done when jobs and investments are on the line. For now, we may be OK staying stuck in our ways and relying on old thought processes. I’d say we have 5-10 years (into my retirement years) to enjoy the status quo. However, that time horizon seems to shrink every month and every day not focused on transformation is a day lost to the competition.
Need help in your transformation? Let’s talk about the rockets you want to blow up.
2025: The Year AI Transforms Work and Industry at Scale
Happy New Year! Here are my bet’s for 2025.
The rapid evolution of artificial intelligence is reshaping industries and redefining how we work. In 2025, I predict that several transformative trends will reshape the landscape of software development, industry specialization, and workforce dynamics. Below, we explore these predictions, provide additional insights, and examine the opportunities and risks that come with these changes.
1. Accelerating Improvements in LLMs
Large Language Models (LLMs) will continue to push the boundaries of what AI can do. With advancements in transformer, SSM, and other architectures, fine-tuning, and multimodal learning, LLMs will deliver faster, more accurate, and contextually rich results. We may even see an new architecture that pushes LLMs ahead even faster.
Opportunities
- Enhanced productivity and creativity in tasks like content creation, research, and customer interaction.
- Multimodal capabilities enabling seamless integration of text, images, and even video into workflows.
Challenges
- Ethical concerns around misuse, bias, and misinformation.
- Increased compute requirements may exacerbate energy consumption concerns.
2. Smaller Models, Big Potential
Compact LLMs leveraging distillation, pruning, and quantization will achieve near-parity with today’s largest models. This shift will democratize AI, making it accessible for edge devices, IoT applications, and industries with limited compute resources.
Opportunities
- Cost-effective AI solutions for SMBs.
- Expanding AI’s footprint into rural and underserved areas via low-power devices.
Challenges
- Balancing efficiency with accuracy in critical applications like healthcare diagnostics or autonomous vehicles.
3. Industry-Specific AI Takes Center Stage
The rise of verticalized AI solutions tailored to specific industries, such as marketing, healthcare, finance, and legal, will dominate the market. These solutions will provide unparalleled domain expertise, driving faster ROI for businesses.
Opportunities
- Precision and relevance in solving domain-specific challenges.
- Increased trust in AI adoption as models demonstrate real-world impact.
Challenges
- Heavy reliance on proprietary data could lead to monopolistic behavior or widen the gap between industry leaders and smaller players.
4. AI-Driven Development as the Norm
AI coding assistants like GitHub Copilot, Cursor, and Aider are already changing the way developers work. In 2025, AI will be fully integrated into development ecosystems, assisting with ideation, debugging, and even deployment.
Opportunities
- Streamlined development cycles, reducing time to market.
- Better accessibility for non-traditional developers, diversifying the talent pool.
Challenges
- Over-reliance on AI could erode foundational coding skills.
- Potential loss of creativity in problem-solving as AI-driven patterns become standardized.
5. Coding Becomes a Commodity
As AI handles the technical details of coding, the value of knowing how to code will diminish. Instead, the ability to guide AI assistants, define clear objectives, and solve high-level problems with code will become paramount. The experience to know when code is good or bad becomes more important than the just the ability to code.
Opportunities
- Broader inclusion of non-technical professionals in tech projects.
- Emergence of new roles focused on prompt engineering, strategy, and oversight.
Challenges
- A potential skills gap for current developers who fail to adapt.
- Risk of job displacement without sufficient upskilling initiatives.
6. Shippable AI-Driven Products with Minimal Oversight
In 2025, performant coding agents capable of turning product requirements into deployable solutions will emerge. These agents will handle well-defined scopes but may still require human oversight for complex or high-stakes applications.
Opportunities
- Faster MVP development for startups.
- Automated maintenance of legacy systems, freeing up human resources for innovation.
Challenges
- Ensuring quality control and accountability in AI-generated products.
- Difficulty in generalizing complex, nuanced requirements.
7. OpenAI Dominates but Faces Competition
OpenAI will maintain a stronghold on the market, but competition from Google, Meta, Anthropic, Cohere, and open-source ecosystems will heat up.
Opportunities
- Diverse options for businesses to choose from, fostering innovation and reducing costs.
- Strengthening open-source movements that promote transparency and collaboration.
Challenges
- Risk of market fragmentation, making it harder for businesses to standardize solutions.
- Proprietary dominance could limit interoperability.
8. AI Agencies Replace Traditional Agencies
Small AI agencies will rise to prominence, offering specialized services in automation, data modeling, and AI-driven marketing and development. These agencies will cater to SMBs, replacing traditional creative and technical firms.
Opportunities
- Affordable, tailored AI solutions for local markets.
- Innovation in personalized customer experiences and hyper-local strategies.
Challenges
- Ethical dilemmas in hyper-targeted advertising.
- Limited oversight in emerging markets where regulation lags behind.
9. Data Becomes the New Gold for Real This Time
Businesses will finally fully realize the value of their proprietary data, using it to train domain-specific models. Data-rich companies will dominate their industries by leveraging AI in unique and powerful ways.
Opportunities
- Competitive differentiation through unique datasets.
- Greater investment in data quality, security, and governance.
Challenges
- Risk of data monopolies exacerbating inequality.
- Increased cybersecurity threats targeting proprietary datasets.
Navigating Risks and Building for the Future
While the predictions for 2025 are exciting, they also come with challenges that require proactive measures:
- Upskilling the Workforce: Governments, businesses, and educational institutions must collaborate to prepare the workforce for AI-driven roles.
- Regulating Ethically: Establishing global standards for AI use will be crucial to avoid misuse and ensure equitable benefits.
- Driving Sustainability: Advancements in AI must prioritize energy efficiency and sustainable practices.
As we move into 2025, businesses that embrace these changes while navigating risks will unlock unprecedented opportunities. The future of work and industry is brighter, more efficient, and deeply collaborative—driven by AI.
Are you ready to harness the power of AI for your business? Let’s talk about it and get your business ready for our AI future.