AI Coding Tools: The Future of Software Dev

We recently had a SaasRise community call about AI Coding Tools.

We discussed tools like Cursor, Co-pilot, Replit, ChatGPT, Claude, Lovable, v0, and Codeium. These tools are already revolutionizing software development speed — and they are improving every month.

You can watch a recording of the discussion here and read the deep dive guide we wrote based on the learnings from the community conversation. Feel free to forward this guide to your CTO/Head of Engineering.

My sense is that nearly all fast growing tech companies will be actively using AI as part of their coding workflow by 2026. Here’s the video and the deep dive article.

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AI Coding Tools Deep-Dive

By Ryan Allis, CEO & Co-Founder of SaasRise

Over the last year, AI coding tools have exploded, promising faster development, leaner engineering teams, and the potential to transform the way we build software.  

But how far along are these tools, really? Are they just handy code-completion engines, or can they seriously reshape how we scope projects, hire developers, and manage QA? Will there use make developers obsolete, or perhaps make human developers so efficient that companies can afford to hire more?

To find answers, we convened a community discussion with over 70 software CEOs, founders, and CTOs from around the world.  

We opened the floor for folks to compare notes on what’s working, what’s not, and what might happen in the next 24 months.

Below is an in-depth summary of that entire conversation and jam-packed with specific tips, real-world experiences, and cautionary notes.

Let’s dive in!

The Rising Tide of AI Coding Tools

As soon as we asked attendees to share what tools they are currently using, our Zoom chat lit up immediately with mentions of tools like:

• GitHub Copilot – The AI coding assistant from Microsoft.
• Cursor – An “agentic” coding environment that goes beyond basic code-completion.
• ChatGPT & Claude – General-purpose LLMs that can also generate code, debug, and do QA.
• Lovable – Popular for UI prototyping.
• v0 – Often used to whip up frontend from design or image prompts.
• Replit – An online coding environment with AI assistance baked in.
• Codeium – Another up-and-coming competitor in the code-assistant space.

If you’re new to this domain, the big shift is that these tools don’t just “autofill” the next line; they can generate entire code snippets, propose architectural changes, convert data structures, or create UI prototypes from a screenshot or natural language written description. And we’re only at the tip of the iceberg.

Who’s Using These AI Coding Tools and How

During the call, folks chimed in that they’re using AI for:

• Snippets and code completion (like in Visual Studio Code or GitHub Copilot).
• SQL generation and optimization (ChatGPT with specialized plug-ins).
• Prototyping (spinning up new React or Laravel apps in a fraction of the usual time).
• Testing (auto-generating unit tests, Cypress scripts, or entire QA workflows).
• Debugging (pasting in broken code to get quick suggestions).
• Documentation (letting the AI read and interpret developer docs so humans don’t have to).

Our member firms reported the speed of their development improving by an average of 20-40%, with one firm reporting a 10x improvement in development speed.

In short, it’s not just a neat novelty. Some teams see AI as a strategic advantage that doubles or even tenfolds productivity—especially when their devs are comfortable guiding the AI with well-crafted prompts.

AI as Coding Assistant: From “Nice-to-Have” to “Essential”

A big chunk of the call focused on how AI is used today to support existing developers, rather than replace them. Several members gave concrete examples:

• Ron Laughton, CEO at ReviewInc uses Microsoft Visual Studio’s AI typeahead to guess lines before he types. He’s coded in Visual Studio for 25 years and found it surprisingly intuitive. He uses ChatGPT to convert large JSON objects into C# classes, which used to be tedious and error-prone. Ron said, “Suddenly, the next three lines appear, and often it’s spot on. That saves me a bunch of time.”
• Dan Perez’s team at Aquent leans on GitHub Copilot for day-to-day code completion, but Dan personally relies on ChatGPT for complicated SQL tasks, letting it churn through large database schemas to propose queries and highlight possible slow joins or missing indexes.

Efficiency Gains in Test Generation & Translation Between Programming Languages

Multiple participants said they use ChatGPT or Claude to generate entire test suites. One member mentioned a simple prompt, “Here’s my API or function, now generate relevant tests.” Another member described how they feed in a complicated bit of code or architecture, and the AI suggests a variety of test cases they might’ve overlooked.

HD Vo, CEO of Inmapz said: “We had no official test suite at one point. With AI, we can quickly spin up all the test scaffolding. It’s a great timesaver.”

HD also praised the AI’s ability to convert code from one language to another: “If you have a dev who’s better at JavaScript than Python, let the AI handle the translation so they can keep trucking in a language they’re comfortable with.”

It all boils down to one major lesson: devs remain in control, but the mundane chores—like writing repetitive boilerplate, debugging small bits of code, or generating test coverage—are faster when you can rely on an AI co-pilot.

Building Entire MVPs, Front to Back

A second group of members described more ambitious use cases: generating whole new features, or even greenfield products, with minimal developer input.

Going from Zero to React App

Nikhil Nathar, CEO/CTO of AvanSaber recounted how he used AI to build a React app, even though he’d never coded in React.

1. He started with Lovable for UI/UX.
2. Then switched to Cursor when complexity rose.
3. Then iterated repeatedly—sometimes hitting weird extra code or conflicting database schema, but eventually reaching a functional, ~95%-complete solution.

This highlights a pattern we heard over and over: you can create an MVP very quickly, but you should expect some friction—like random code bloat or context mix-ups. A good developer still needs to remove the clutter and ensure it all runs smoothly.

“One-Month MVP” (Instead of Six)

Philippe Dallaire, the CTO of Consuly described going “all-in” on advanced AI models (Claude 3.7, “thinking mode,” and others) to build an entire MVP in under a month. Previously, that kind of product would have taken 6–12 months with a team of 10–20 developers. Now, a handful of devs plus AI can do it in 4 weeks.

He observed a 10x speed improvement. He’s hiring fewer pure coders, focusing on more well-rounded devs who understand “the bigger picture” of product requirements and architecture, because the AI does so much of the basic coding.

As Philippe put it: “I still need a senior engineer to clarify the desired output and set the architecture, but we then iterate 10 times faster than we did a year ago.”

Lovable and Cursor for the Frontend

Several participants, including Chaithanya Kumar (CEO of Incepteo and StratPilot) and Arjan Arjan Herskamp (CEO of MyDataFactory) love to use Lovable for UI generation. They feed in high-level specs—like “I want a login page with a whimsical feel, plus a two-column dashboard”—and get back working React or Tailwind code. That code is usually about 70-80% ready to go, with devs polishing the remaining edge cases or styling details.

Hiring Implications: Does AI Replace Dev Teams?

One burning question: Will AI coding tools replace junior developers or maybe entire dev departments? Our group was split on the timeline, but nearly everyone agreed on three key points:

1. You will need fewer junior devs. Simple tasks—like bug fixing, trivial improvements, or small front-end changes—can now be done by AI. HD said that in 24 months, a new CS grad might be less valuable because the AI can effectively do that level of coding.
2. You still need senior minds. People who understand architecture, requirements, security, performance, and can interpret business logic will remain indispensable. In fact, they might become more valuable because they can leverage AI to multiply their output.
3. Some are even hiring more developers. Strange as it might sound, because each dev’s productivity is boosted, the ROI on each new dev is higher, making it tempting to double down on an already efficient team.

Christian Frunze at GetKen.ai actually grew his dev team:

“They ship features twice as fast with AI. So the ROI is amazing. Why wouldn’t I scale up a group that can deliver that quickly?”

On the flip side, Jayakrishnan Melethil, CEO of CodeLynks said his company reduced R&D spending by 30% while still speeding up release cycles. So it’s a balancing act: some companies will trim staff and rely on AI for “entry-level” tasks; others will add more devs to become unstoppable shipping machines.

Security, IP Risks, and the Trust Factor

Many members on the call expressed caution about uploading full codebases to a public AI model. One member said:“I won’t feed my entire proprietary code to ChatGPT. That’s a huge IP risk. We’d want a fully local or private version for that.”

Christian Frunze of GetKen.ai shared they tried hosting their own large language model (LLM) for cost and security reasons, but found it “high maintenance” and not as advanced as the big public LLMs. Some have turned to specialized “enterprise ChatGPT” solutions or plan to run open-source models like Llama-3 on their private servers.

Key Tip: Check your vendor’s data retention and privacy policies. If you’re in fintech, healthtech, or an enterprise with strict compliance, you’ll want contractual guarantees or a self-hosted approach. Otherwise, you risk your code or user data being used to further train someone else’s AI.

The QA and Testing Frontier

Automated QA Generation

We had a lively side-discussion on using AI to auto-generate entire test suites or even create advanced E2E tests with frameworks like Cypress, Selenium, or Playwright. Chris asked if anyone’s used an “AI agent” to crawl an app with detailed instructions and produce a test suite.

Most people responded that, yes, the idea is there, but it’s early days:

• Tools like TestRigor, Mabl, Functionize, CodiumAI claim to generate or maintain tests automatically.
• For simpler tasks, feeding code to ChatGPT or Claude might suffice.
• However, these solutions still break down on complex apps, or time out, or require manual supervision to ensure the tests are relevant and robust.

HD Vo, CEO of InMapz, pointed out:

“I can generate half my test scripts quickly, but as soon as the AI hits complex user permissions or advanced logic, it gets confused. Then I have to do more manual cleanup.”

Review Bots in the CI/CD Pipeline

Jayakrishnan Melethil from Codelynks talked about a “virtual engineer” approach: a specialized AI script that checks each pull request and offers inline comments. This helps catch minor issues before a human lead reviews them:

“We see huge time savings. The AI flags 2–3 potential bugs or un-optimized code issues so the lead dev can focus on more important stuff.”

The “Agentic” Future

A recurring theme was the idea of “agentic AI”—LLMs that do more than code snippets. They understand high-level goals, maintain context, and autonomously make changes across a codebase, committing to GitHub, running tests, and iterating.

Joseph Khorshed, CEO of Cequens said they’re building an AI that rewrote their entire website from scratch, pushing changes to GitHub automatically. He rated AI dev tools at 8/10 for replacing a full-time developer. That’s in contrast to a lot of folks who scored them more like 2–6/10.

Why the difference? Probably because Joseph is going all-in on an advanced approach that integrates the AI deeply with the code pipeline. He acknowledges there’s still bug-fixing and oversight required, but in some narrower contexts, it can function like a mid-level developer.

Practical Tips for Maximizing AI’s Value

Throughout the conversation, participants dropped plenty of “lessons learned” or “best practices.” Here’s a deeper dive:

1. Focus on Micro-Problems

• Let’s say you want a Chrome extension that does X, Y, Z. If you feed that as a small, discrete prompt to ChatGPT or Claude, you can get 80–90% of the code in minutes. Then you do a little debugging.
• If you ask it to build your entire monolithic enterprise app at once, you’ll get jumbled code or partial solutions.

2. Prompt Engineering is King

• For advanced use, you want to provide the AI with the relevant function signatures, constraints (e.g., “No external dependencies”), and examples of coding style so it can match your codebase.
• “What is your environment?” or “Which library versions are you using?” are critical details the AI needs to tailor solutions.

3. Don’t Skip the Debug Phase

• Many developers said, “AI-generated code is rarely 100% functional out of the gate.” You might see large chunks that almost work, but expect to refine them.
• If you treat the AI as a junior dev producing a first draft, you’ll likely have better success than blindly trusting its output.

4. Integrate QA Early

• Tools like ChatGPT can propose tests. Combine that with your CI/CD pipeline, so you detect breakage quickly.
• If the AI updates code but not the docs or tests, you may see creeping inconsistencies.

5. Maintain Oversight

• Senior devs or architects need to watch out for architecture drift, too many dependencies, or performance issues.
• AI is great at producing code, but can’t always judge the intangible “fit” for your product’s style or user base.

6. Enforce Security and Data-Sensitivity Safeguards

• If your code is sensitive, explore private or enterprise-grade solutions.
• For proprietary code, consider local LLMs or partial code snippets, rather than the entire repo.

7. Keep an Eye on Unit Test Generation

• AI loves to produce test stubs—but ensure they actually check real logic, not just the “happy path.”
• If something changes in your code, re-run the AI to update the tests or be prepared to do it manually.

Possible Pitfalls and Concerns With AI Coding

Code Quality Over Time

1. Some worry that AI code might degrade maintainability. You get a “sugar rush” of quick output, but 3–6 months later, you discover messy abstractions that hamper future changes.
2. Context Window Limitations

• Current versions of tools like ChatGPT or Claude can only handle a certain chunk of code or conversation at once. If you have a huge codebase, you might have to feed it in smaller sections, losing the holistic view.
• Solutions like “retrieval-augmented generation” or local searching within your codebase might help, but it’s still evolving.

3. Talent Development

• New or junior devs might not learn the fundamentals if they rely on AI too heavily. That could create skill gaps long-term.

4. Security Vulnerabilities

• AI might propose code that’s suboptimal or insecure. Attackers could query the same models to find known vulnerabilities in popular code patterns.

5. Plateaus in Model Improvement

• Some participants quipped that the AI might “run out of StackOverflow code.” Whether that’s likely or not, the pace of improvement is expected to be extremely fast for another year or two, but no one knows if we’ll hit a plateau eventually. We doubt the we are many many years away from a plateau in improvements, as context windows and training sets improve and AI tools become more powerful and aware.

Where We’ll Be in 24 Months with AI Coding

The group tossed around predictions:

• Fewer entry-level dev hires: AI can handle straightforward tasks.
• More specialized or “business-minded” engineers: People who can prompt the AI, validate outputs, and shape the product’s direction.
• Further integration with design tools: Already, people like Arian are using v0 to generate UI from Figma. Soon, you might “speak” your design changes, and the AI modifies your app in real time.
• Potential massive rethinking of R&D: If a single developer can do the job of five, do you restructure your entire dev org? Where do QA and product management fit?

And, as HD Vo from InMapz suggested, “maybe we hire more architects instead of new grads,” shifting the entire focus of a dev team.

Balancing the Risks vs. the Rewards

Yes, there are real concerns:

• IP security
• Dependency on third-party or closed-source LLMs
• Excess code complexity
• Possible over-reliance on AI suggestions

But the payoff can be huge:

• 10x speed for building MVPs
• 30%+ cost reduction or ability to ship twice as many features
• Engineers with superpowers who can tackle front-end, back-end, or testing in one day

The coding piece might become a smaller fraction of a developer’s job. More important will be domain expertise, user empathy, architecture, and communication.

Tips for Getting Started (or Going Deeper) with AI Coding

1. Pick a low-risk project or feature where you can test the waters.
2. Give your team some “prompt-engineering 101” training —the more context they provide, the better the AI’s output.
3. Incorporate AI-based code reviews into your pipeline, but keep a senior dev in the loop to finalize merges.
4. Monitor velocity and track actual ROI—see whether you can reduce cycle times or reallocate dev resources.
5. Stay informed on new AI developments: large context windows, local models, or agentic frameworks could drastically improve your workflow.

Final Takeaways: Embrace the AI Coding Evolution

Reflecting on our call, I came away with a few overarching insights:

• AI is no longer a novelty: plenty of SaaS founders are using it daily to code, test, document, and even create UIs.
• It’s not a perfect dev replacement—most folks rated it around 3–6/10 for “replacing humans,” although a handful said 8/10 if you fully integrate agentic AI.
• Production-ready code still needs a human eye, at least for the foreseeable future.
• Companies are reacting differently: some cut engineering spend by 30%, others hire more devs to capitalize on the huge speed gains.

In short, we’re at a crossroads. If you adopt AI coding tools wisely, you might build new products in half the time (or less). Your QA could be more thorough, your junior devs more empowered, and your senior devs free to focus on architecture. But you have to manage the risks of security, oversight, and code sustainability.

My personal take: In 12–24 months, these AI tools will be much better. By late 2025 or early 2026, they could be writing entire complex features with minimal human input. It’s not too soon to experiment now and figure out how to integrate them into your workflow. The payoff is real—and so is the sense that we’re approaching some “singularity moment” in software development productivity. While these tools were not around when I was building iContact, all fast growing tech companies will be actively using AI as part of their coding workflow by 2026.

As always, stay curious, keep building, and share your wins (and hiccups) with the community. I’m excited to see how you all harness these AI superpowers to move even faster in our SaaS-driven world.

Special thanks to everyone who joined the call, shared insights, posted polls, and provided real-world experiences with AI coding tools. Let’s keep exploring, iterating, and building the future—together.

I hope this guide was helpful. Let me know your thoughts, and I’ll see you next time with more great content on building, scaling, and exiting SaaS companies.

Ryan Allis, CEO
SaasRise
The Community for SaaS CEOs & Founders
www.saasrise.com

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About The Author

Ryan Allis is the founder of SaasRise, the mastermind community for growth-focused SaaS CEOs with $1M-$100M in ARR. He is a three time INC 500 CEO. He was previously CEO of iContact and grew the firm as founder/CEO to 70,000 customers, 1 million users, 300 employees, $50M per year in sales, and an exit for $169M to Vocus (NASDAQ:VOCS). ‍

‍Since the sale of iContact, Ryan has been the CEO coach to high-growth SaaS firms including Instantly, Tatango, Seamless.ai, Pipeline, Datalyse, Green Packet, Revenue Accelerator, Galleon, Clearstream, YouCanBookMe, Retreaver, and EventMobi. Ryan has been part of the EO and Summit Series communities.

He holds an MBA from Harvard Business School, where he was Co-President of the Social Enterprise Club and a member of the Harvard Graduate School Leadership Institute. He’s passionate about helping recurring revenue software companies grow and exit.

We’ll see you next time with more great SaaS growth and scaling content!

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