Ben Lavender

Visionary Product Leader | Board Advisor | Creator of BBC iPlayer, LOVEFiLM acquired by Amazon to be the foundation of Prime Video and DAZN

[Ben Lavender @benlavender]

When AI Agents Go Rogue: A Cautionary Tale from the Trenches 🤖⚠️

This week delivered a stark reminder that AI, while transformative, can be a double-edged sword when given too much autonomy.

I had a perfectly functioning API service filtering brand data. After letting an AI coding assistant run in agent mode to "optimize" some updates, it systematically corrupted the entire filtering logic. What started as returning 11 random results with poor matching degraded to returning nothing at all—despite 472 out of 484 records containing the exact filter criteria.

The recent METR study 'Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity' (https://t.co/oMd2tWJQVc) reinforces this experience with hard data: AI tools actually SLOWED DOWN experienced developers by 19%, despite both developers and experts predicting 20-40% speedups.

Key takeaways from both research and experience:

🔍 AI excels as a copilot, not an autopilot
It's brilliant for suggestions and code completion. But give it free rein to refactor your codebase? That's when things go sideways.

📊 The expertise paradox
The study found AI helped least where developers had deep familiarity with their repositories. My experience confirms this - AI couldn't grasp the nuanced business logic that makes a filtering system actually useful.

🧠 The ML comprehension gap
Perhaps most concerning: the LLMs struggled to understand and correctly modify complex machine learning code. They could write boilerplate, but when it came to understanding feature engineering, model predictions, and data pipelines? They introduced subtle bugs that broke everything downstream.

🎓 Fundamentals matter more than ever
Andrew Ng's (@AndrewYNg) Machine Learning Specialization (https://t.co/fgthWGaULe) on Coursera proved invaluable for understanding what's happening under the hood. Without that foundation, I'd be helplessly watching AI tools make decisions I couldn't evaluate or correct.

Critical papers like Entity Embeddings of Categorical Variables (https://t.co/lIJ9iMb82T) showed how turning categories into vector embeddings lets neural networks understand hidden relationships and make far better predictions. This foundational knowledge is what separates informed AI collaboration from blind dependency.

I'm planning to dive deeper with Andrew NG’s (@AndrewYNg) Deep Learning Specialization (https://t.co/scRR9019BH) next—if we're going to work alongside AI, we need to understand both its capabilities and limitations at a fundamental level.

The reality check:

The METR researchers found developers spent 9% of their time just reviewing and cleaning AI outputs. In complex ML systems, that overhead can completely negate any productivity gains.

The bottom line: AI is a powerful amplifier of human capability, but it's not a replacement for human judgment - especially in machine learning systems where small changes can cascade into major failures.

As we rush to integrate AI into every workflow, let's remember: understanding the fundamentals and maintaining human oversight isn't optional - it's essential.

Have you experienced similar AI "help" that went wrong? What guardrails have you put in place?

Thanks to Juan Perna (https://t.co/Fe1BrlUHjI) for sharing the METR paper.

#AI #MachineLearning #DeepLearning #SoftwareDevelopment #TechLeadership #CodingBestPractices

[Ben Lavender @benlavender]

@LloydsBank @Charlot85653970 The Lloyds bank mobile app is not allowing you to login either

[Ben Lavender @benlavender]

@elonmusk

[Ben Lavender @benlavender]

When AI Agents Go Rogue: A Cautionary Tale from the Trenches 🤖⚠️

This week delivered a stark reminder that AI, while transformative, can be a double-edged sword when given too much autonomy.

I had a perfectly functioning API service filtering brand data. After letting an AI coding assistant run in agent mode to "optimize" some updates, it systematically corrupted the entire filtering logic. What started as returning 11 random results with poor matching degraded to returning nothing at all—despite 472 out of 484 records containing the exact filter criteria.

The recent METR study 'Measuring the Impact of Early-2025 AI on Experienced Open-Source Developer Productivity' (https://t.co/oMd2tWJQVc) reinforces this experience with hard data: AI tools actually SLOWED DOWN experienced developers by 19%, despite both developers and experts predicting 20-40% speedups.

Key takeaways from both research and experience:

🔍 AI excels as a copilot, not an autopilot
It's brilliant for suggestions and code completion. But give it free rein to refactor your codebase? That's when things go sideways.

📊 The expertise paradox
The study found AI helped least where developers had deep familiarity with their repositories. My experience confirms this - AI couldn't grasp the nuanced business logic that makes a filtering system actually useful.

🧠 The ML comprehension gap
Perhaps most concerning: the LLMs struggled to understand and correctly modify complex machine learning code. They could write boilerplate, but when it came to understanding feature engineering, model predictions, and data pipelines? They introduced subtle bugs that broke everything downstream.

🎓 Fundamentals matter more than ever
Andrew Ng's (@AndrewYNg) Machine Learning Specialization (https://t.co/fgthWGaULe) on Coursera proved invaluable for understanding what's happening under the hood. Without that foundation, I'd be helplessly watching AI tools make decisions I couldn't evaluate or correct.

Critical papers like Entity Embeddings of Categorical Variables (https://t.co/lIJ9iMb82T) showed how turning categories into vector embeddings lets neural networks understand hidden relationships and make far better predictions. This foundational knowledge is what separates informed AI collaboration from blind dependency.

I'm planning to dive deeper with Andrew NG’s (@AndrewYNg) Deep Learning Specialization (https://t.co/scRR9019BH) next—if we're going to work alongside AI, we need to understand both its capabilities and limitations at a fundamental level.

The reality check:

The METR researchers found developers spent 9% of their time just reviewing and cleaning AI outputs. In complex ML systems, that overhead can completely negate any productivity gains.

The bottom line: AI is a powerful amplifier of human capability, but it's not a replacement for human judgment - especially in machine learning systems where small changes can cascade into major failures.

As we rush to integrate AI into every workflow, let's remember: understanding the fundamentals and maintaining human oversight isn't optional - it's essential.

Have you experienced similar AI "help" that went wrong? What guardrails have you put in place?

Thanks to Juan Perna (https://t.co/Fe1BrlUHjI) for sharing the METR paper.

#AI #MachineLearning #DeepLearning #SoftwareDevelopment #TechLeadership #CodingBestPractices

[Ben Lavender @benlavender]

@elonmusk

[Ben Lavender @benlavender]

The LLM Orchestra: How AI Models Collaborate Better Than They Work Alone 🎼🤖

After experiencing the limitations of individual AI models, I discovered something remarkable: LLMs perform dramatically better when they learn from each other's responses.

Here's what happened when I turned AI debugging into a collaborative symphony.

The Experiment Setup

I presented the same complex machine learning pipeline problem to four leading models:

- Claude Opus 4.1 (Anthropic)
- ChatGPT-4.1 (OpenAI)
- Gemini Pro (Google)
- Grok (https://t.co/Xkl2Q7qIhY)

Initially, their responses were wildly divergent—different architectures, conflicting best practices, even disagreements about which tools existed.

The Collaborative Breakthrough

But then I tried something different: I shared each model's response with all the others.

The transformation was immediate and fascinating:

Round 1: Isolated Responses
- Claude: Focused on PostgreSQL optimization and materialized views
- ChatGPT: Emphasized pandas vectorization and memory management
- Gemini: Suggested complete architectural redesign with distributed computing
- Grok: Recommended switching to different ML frameworks entirely

Round 2: Cross-Pollination
When I shared Claude's PostgreSQL insights with ChatGPT:

- ChatGPT immediately recognized the superiority of the database-level filtering approach
- It refined Claude's SQL queries with better indexing strategies
- Added pandas optimizations that complemented the database approach

When I shared this refined solution back to Gemini:

- Gemini identified edge cases in the demographic filtering logic
- Provided mathematical corrections to the audience size calculations
- Suggested performance monitoring approaches

Round 3: Convergence
By the third round, something remarkable happened: all models converged on virtually identical solutions, but each contributed unique optimizations:

Individual Model Strengths Revealed

Claude Opus 4.1: The Database Whisperer ⭐⭐⭐⭐⭐
Best for: Complex SQL, system architecture, production-ready code

Strengths discovered through collaboration:
- SQL mastery: Generated sophisticated PostgreSQL queries with proper indexing
- Systems thinking: Understood the entire data pipeline context
- Code quality: Produced production-ready, well-documented solutions
- Edge case handling: Identified subtle business logic requirements

Cost caveat: 10x more expensive than alternatives, but delivered the most comprehensive initial analysis

ChatGPT-4.1: The Performance Optimizer ⭐⭐⭐⭐
Best for: Code optimization, pandas operations, memory management

Collaboration superpowers:
- Refinement specialist: Took Claude's architectural insights and optimized them beautifully
- Memory efficiency: Added crucial pandas optimizations that reduced processing time by 60%
- Integration expertise: Seamlessly combined database and in-memory processing approaches
- Practical focus: Balanced theoretical correctness with real-world performance needs

Gemini Pro: The Mathematical Validator ⭐⭐⭐⭐
Best for: Mathematical accuracy, algorithmic validation, edge case analysis

Unexpected collaborative strengths:

- Formula verification: Caught mathematical errors in demographic calculations that others missed
- Edge case detection: Identified scenarios where the filtering logic would break
- Scalability insights: Provided crucial guidance on handling large datasets
- Academic rigor: Brought theoretical ML knowledge that improved algorithm design

Grok: The Integration Catalyst ⭐⭐⭐
Best for: Synthesis, workflow integration, practical implementation

Collaborative value:

- Synthesis specialist: Excellent at combining insights from multiple models
- Workflow integration: Best at connecting the solution to existing development processes
- Practical questions: Asked the right clarifying questions that refined the overall approach
- Implementation focus: Kept discussions grounded in actionable next steps

The Collaborative Methodology That Worked

Phase 1: Isolated Analysis (15 minutes)
Present the problem to each model independently, capture their initial approaches

Phase 2: Cross-Pollination (30 minutes)
Share the best insights from each model with all others, let them build on each other's work

Phase 3: Convergence & Validation (20 minutes)
Synthesize the refined approaches, identify the optimal hybrid solution

Phase 4: Implementation Planning (10 minutes)
Use the collaborative solution to create actionable implementation steps

Key Insights for Developers

1. Individual Model Limitations Are Real
- Each model has blind spots and biases
- Single-model solutions often miss critical optimizations
- Cost doesn't always correlate with quality for specific use cases

2. Collaborative AI Amplifies Strengths
- Models build remarkably well on each other's insights
- Cross-validation catches errors that individual models miss
- Different models excel at different aspects of complex problems

3. The METR Study Makes More Sense
The METR finding that AI slowed experienced developers by 19% likely reflects single-model usage. Collaborative AI approaches could flip this equation entirely. (https://t.co/oMd2tWJQVc)

4. Practical Implementation Strategy
For complex ML debugging:

1. Start with Claude Opus for architectural insights (if budget allows)
2. Refine with ChatGPT for performance optimization
3. Validate with Gemini for mathematical accuracy
4. Synthesize with Grok for implementation planning

The Future of AI-Assisted Development
This experience suggests we're approaching AI collaboration wrong. Instead of seeking the "one true model," we should be building collaborative AI workflows that leverage each model's unique strengths.

The total cost of using four models collaboratively was still less than what we'd spend on junior developer time, but delivered senior-level insights across multiple domains.

The orchestra metaphor is apt: individual musicians are talented, but the symphony emerges from their collaboration.

Have you experimented with multi-model approaches? What collaborative AI workflows have worked in your domain?

Maybe the future isn't about finding the perfect AI assistant—it's about conducting the perfect AI ensemble.

#AI #MachineLearning #CollaborativeAI #LLMOrchestration #SoftwareDevelopment #AIWorkflow #TechInnovation

[Ben Lavender @benlavender]

The Trust Paradox: Why Developers Are Using AI More But Trusting It Less 🤖📉

We've hit peak paradox in the AI coding revolution: Stack Overflow's 2025 survey (https://t.co/R971sDpcjG) of 49,000+ developers reveals that while 84% now use AI tools (up from 76% in 2024), trust has plummeted to just 33%—down from 40% last year.

Having spent months wrestling with this exact contradiction in my own development work, I now understand why developers everywhere are experiencing the same cognitive dissonance: we're simultaneously dependent on and distrustful of our AI coding partners.

The "Almost Right" Problem

The data validates what I experienced firsthand when my API filtering logic went from returning 11 random results to returning nothing at all. Stack Overflow found that 66% of developers cite "AI solutions that are almost right, but not quite" as their biggest frustration.

This isn't just an annoyance—it's creating a new category of technical debt. According to a Harness survey of 500 engineering leaders:

- 67% spend MORE time debugging AI-generated code than code they would have written themselves
- 68% report increased time resolving AI-related security vulnerabilities
- 92% say AI is increasing the "blast radius" of bad code needing to be debugged

As one developer put it in the survey comments: "Debugging code you didn't write is already hard. Debugging code that an AI wrote, which looks correct but has subtle logic errors? That's a special kind of hell."

The Experience Gap: Why Seniors Trust Less

The most counterintuitive finding? Experience breeds scepticism. Stack Overflow's data shows:

- Experienced developers (10+ years): Only 2.6% "highly trust" AI output, with 20% "highly distrusting" it

- Early career developers: Show 61% favourable sentiment toward AI tools

Learning to code: 53% favourable sentiment

This aligns with multiple studies I reviewed:

- Microsoft/MIT/Princeton research (https://t.co/BvGm8qGiUS) found junior developers saw 27-39% productivity gains from AI tools

Senior developers achieved only 7-16% improvements

The METR study (https://t.co/oMd2tWJQVc) showed AI tools actually slowed experienced developers by 19% on complex, familiar codebases

Apple's recent "Illusion of Thinking" research (https://t.co/GGlb6HVcuX) provides the smoking gun: Large Reasoning Models (LRMs) face "complete accuracy collapse beyond certain complexity thresholds." Using controlled puzzle environments, researchers found that these supposedly "thinking" models actually:

Overthink simple problems, finding correct answers then second-guessing into wrong ones

Show advantages only at medium complexity

Completely fail at high complexity regardless of compute budget

Why does this matter? As I learned through Andrew Ng (@AndrewYNg) courses (https://t.co/fgthWGaULe) on machine learning fundamentals, understanding what's happening under the hood is crucial. Senior developers spot the subtle errors that juniors might miss—or worse, deploy to production. They recognise what Apple proved: these models aren't reasoning, they're sophisticated pattern matchers hitting hard limits.

The Productivity Illusion

The numbers tell a fascinating story of collective self-deception:

What developers THINK:

- Before using AI: "This will speed me up by 24%"
- After using AI: "I'm 20% more productive"

What actually happens:

- METR study: 19% SLOWER on real-world tasks
- Google internal study: ~20% improvement, but only on greenfield projects (https://t.co/ty4SECBG9d)
- Stack Overflow: 45% say debugging AI code takes longer than writing it themselves

The reality? Context is everything. AI excels at:

- Boilerplate generation (82% use it for writing code)
- Simple CRUD operations
- Well-documented patterns

But it fails catastrophically at:

- Complex business logic (only 4.4% say AI handles complex tasks "very well")
- Legacy codebases with intricate dependencies
- Domain-specific requirements (exactly what killed my filtering system)

Apple's research confirms this isn't a bug—it's a fundamental limitation. Their controlled experiments showed that frontier models exhibit a "counterintuitive scaling limit": reasoning effort increases with complexity up to a point, then declines despite having adequate tokens. The models essentially give up when problems get too hard, confirming they're not actually reasoning but pattern-matching within learned boundaries.

The Money Problem Nobody Talks About

Here's what the surveys don't headline but the data reveals: AI coding tools are expensive at scale, and the ROI is questionable.

Microsoft's study across 4,867 developers showed gains primarily in "code velocity metrics"—more commits, more compilations. But as any experienced developer knows, more code ≠ better code.

If 250 developers each waste 30% of their time on AI-related debugging (per Harness data), that's £8 million annually in lost productivity for a mid-sized tech company. Meanwhile, enterprise AI coding tool licenses run £15-30 per developer per month. The maths doesn't always work out.

The Human Factor Remains Supreme
Despite the hype about autonomous coding and "AI agents," the data shows we're nowhere close to replacing human judgment:

- 72% of developers don't engage in "vibe coding" (generating entire apps from prompts)
- 76% refuse to use AI for deployment and monitoring
- 69% won't use it for project planning
- 75% would still ask a human when they don't trust AI's answers

Most tellingly, 35% of Stack Overflow visits now result from developers trying to fix AI-generated code. We've created a circular dependency: AI generates code → code breaks → humans search for fixes → AI scrapes those fixes → repeat.

The Path Forward: Collaborative Intelligence

After analysing 20+ studies and surveys, the pattern is clear: AI coding tools work best as sophisticated autocomplete, not autonomous agents.

The most successful implementations I've seen (including at ChargeLab, mentioned in developer forums) share common traits:

- Developer choice in tools (not mandates)
- Focus on assistance, not replacement
- Clear human oversight for critical systems
- Investment in developer education about AI limitations

As I discovered in my "LLM Orchestra" experiment, the future isn't about finding the perfect AI tool—it's about orchestrating multiple tools with human expertise at the centre.

The Bottom Line

The trust crisis in AI coding tools isn't a bug—it's a feature. Healthy scepticism is what's keeping our codebases from complete chaos.

Yes, I use AI tools daily. My platforms leverage FastAPI endpoints that AI helped scaffold. But every line of business logic, every security check, every performance optimisation? That's human-verified, human-understood, and human-accountable.

The data is unequivocal: developers who blindly trust AI tools are setting themselves up for failure. Those who treat AI as a junior developer who occasionally has brilliant ideas but needs constant supervision? They're the ones seeing genuine productivity gains.

Apple's research crystallises what we're all experiencing: these aren't thinking machines, they're sophisticated pattern matchers that create an "illusion of thinking." Once we accept this reality, we can use them effectively within their limitations.

As we rush toward an AI-augmented future, remember: the 66% of us struggling with "almost right" solutions aren't failing—we're the quality control that keeps production systems running.

Have you found your balance between AI assistance and human oversight? What's your trust threshold for AI-generated code?

Thanks again to Juan Perna for the METR paper that started this journey.

#AI #SoftwareDevelopment #CodingReality #DeveloperProductivity #TechLeadership #AITrust #StackOverflow2025

[Ben Lavender @benlavender]

The AI Identity Crisis: When LLMs Don't Even Know Themselves 🤖🔍

Following up on my post about AI agents going rogue: AI models don't understand their own capabilities or even know which version they are.

I asked the same ML debugging question to Claude Opus 4.1, Grok, ChatGPT, and Gemini. The results were eye-opening.

The Identity Problem

Claude Sonnet 4 told me Claude Opus 4.1 "doesn't exist"—while I was looking at Opus 4.1 in my interface dropdown. When corrected, it apologized, but the damage was done.

Each model gave wildly different answers about:
•Which models currently exist
•Their own version numbers/capabilities
•Current ML debugging best practices
•Basic facts about their training/release dates

The Correction Loop Failure

They keep making the same mistakes even after being corrected. This suggests fundamental issues with:

•Context retention during corrections
•Self-awareness of capabilities
•Confidence calibration

Real-World Command Following Failures

AI models repeatedly fail to execute simple editing commands. After multiple instructions to add or remove content, they acknowledge but fail to implement changes. Some admit to bugs in their artifact updating after numerous failed attempts. This isn't complex reasoning—it's basic instruction following.

The irony peaked when drafting this post. After asking Claude Opus 4.1 to remove a section, it ignored the instruction. When pointed out, Claude responded: "You're absolutely right! I'm demonstrating the exact problem you're describing - I failed to remove that section even though you explicitly asked for it."

This meta-failure encapsulates the problem: AI models recognize their mistakes, see the irony, but can't execute basic tasks consistently.

The METR Study Context

METR study (https://t.co/oMd2tWJQVc) findings: experienced developers spent 9% of their time reviewing/cleaning AI outputs. In complex ML systems, this overhead negates productivity gains.

AI tools slowed experienced developers by 19%. We're not just debugging code anymore—we're debugging the AI's understanding of itself.

Recent Claude Performance Concerns

Multiple users report Claude's capabilities degrading. Whether this is:

•Model drift/fine-tuning issues - Safety adjustments impacting technical capabilities
•Infrastructure scaling challenges - Operational issues affecting performance
•Training data staleness - Knowledge gaps and outdated information
•Economic pressures - Cost optimization affecting quality

The pattern: inconsistent performance when we need reliability most.

The Bottom Line

We're in a strange era where AI claims it can write sophisticated ML algorithms but produces broken, inefficient code while confidently asserting correctness. These tools are powerful amplifiers with identity crises.

Until AI models develop better self-awareness and consistency, "AI pair programming" remains unfulfilled. Approach them as talented but confused interns—capable of insights, requiring constant verification.

Have you noticed similar inconsistencies? How are you handling the verification overhead?

The goal isn't to bash AI—it's to use these tools effectively by understanding their limitations.

#AI #MachineLearning #TechReality #AIDebugging #SoftwareDevelopment #ProductivityParadox

[Ben Lavender @benlavender]

Check out my latest article: When AI Agents Go Rogue: A Cautionary Tale from the Trenches 🤖⚠️ https://t.co/BCmYUGSIkg via @LinkedIn

[Ben Lavender @benlavender]

Barry McGinlay’s Tai Chi Life School is a world-class destination for anyone serious about mastering Tai Chi, whether for health, martial application, or personal growth. His credentials speak for themselves—Barry is a two-time Tai Chi Push Hands world champion, a world Tai Chi champion, and a European gold medalist. Beyond his personal victories, he has also coached international, world, European, and national Tai Chi champions, proving that his mastery extends beyond competition and into the realm of elite coaching.

In The Art of Learning, Josh Waitzkin—the chess prodigy turned martial artist—details how he overcame brutal fouls, bias, and adversity to claim a Tai Chi Push Hands world championship title through strategy, resilience, and mastery. That alone is an incredible achievement. Barry McGinlay didn’t just do it once—he did it twice. His victories are a testament to not only his technical excellence but also his mental fortitude, adaptability, and deep understanding of Tai Chi’s internal power.

Barry’s teaching at Tai Chi Life School reflects these qualities. His approach is both inspiring and rigorous, seamlessly blending traditional Tai Chi principles with real-world application. Whether you’re looking to train for competition, deepen your understanding of martial arts, or explore Tai Chi for health and well-being, Barry’s precision, insight, and encouragement create an environment where every student can thrive.

For those looking to train with an instructor who has not only competed and won at the highest level but has also overcome adversity and guided others to success, Barry McGinlay is the real deal. His Tai Chi Life School offers an unparalleled opportunity to learn from a master who embodies the very essence of strategy, resilience, and mastery.

https://t.co/FT9gljrruY

[Ben Lavender @benlavender]

@BarbaraOneillAU M

[Ben Lavender @benlavender]

@DonaldTNews @SpeakerPelosi YES

[Ben Lavender @benlavender]

@ElonMuskAOC Yes

[Ben Lavender @benlavender]

@RestIsPolitics #TRIPlive after the Canadian government froze the bank accounts of striking truckers. Could you imagine this coming to the UK?

[Ben Lavender @benlavender]

@campbellclaret, your unique insights would enrich Threads! Help us elevate discussions. #Threads

[Ben Lavender @benlavender]

@RoryStewartUK, join Threads! Share your unique perspective. Help us foster in-depth, enriching dialogues. #Threads #EngageInDebate

[Ben Lavender @benlavender]

@RestIsPolitics @campbellclaret @RoryStewartUK please get on Threads

[Ben Lavender @benlavender]

@RestIsPolitics @campbellclaret @RoryStewartUK please join Threads to save yourselves from Musk

[Ben Lavender @benlavender]

@Keir_Starmer

Labour could win increased majority by turning against Brexit, new poll finds

https://t.co/tnyrb2lJSs

[Ben Lavender @benlavender]

@GaryLineker huge respect