OpenAI released ChatGPT on November 30th, and within days it became the fastest-growing consumer application in history. As someone who’s been following AI developments closely, I spent the week testing its capabilities for software development tasks. The results are both impressive and sobering.
Here’s my assessment of what ChatGPT means for our industry.
What ChatGPT Can Do
Impressive Capabilities
chatgpt_strengths:
code_generation:
- Write functions from descriptions
- Translate between languages
- Explain existing code
- Generate tests from implementations
debugging:
- Analyze error messages
- Suggest fixes
- Explain stack traces
- Identify common mistakes
learning:
- Explain concepts at any level
- Provide examples
- Answer follow-up questions
- Compare alternatives
documentation:
- Write docstrings
- Create README content
- Explain architecture
- Generate API documentation
Practical Examples
I asked ChatGPT to implement a rate limiter in Go:
// ChatGPT's response (with minor edits)
type RateLimiter struct {
tokens float64
maxTokens float64
refillRate float64
lastRefill time.Time
mu sync.Mutex
}
func NewRateLimiter(maxTokens, refillRate float64) *RateLimiter {
return &RateLimiter{
tokens: maxTokens,
maxTokens: maxTokens,
refillRate: refillRate,
lastRefill: time.Now(),
}
}
func (r *RateLimiter) Allow() bool {
r.mu.Lock()
defer r.mu.Unlock()
now := time.Now()
elapsed := now.Sub(r.lastRefill).Seconds()
r.tokens = min(r.maxTokens, r.tokens+elapsed*r.refillRate)
r.lastRefill = now
if r.tokens >= 1 {
r.tokens--
return true
}
return false
}
The code is correct, idiomatic, and production-ready. This would have taken me 10-15 minutes to write and test. ChatGPT produced it in seconds.
What ChatGPT Cannot Do
Critical Limitations
chatgpt_limitations:
no_execution:
- Can't run code
- Can't verify correctness
- No access to your environment
- No access to your codebase
knowledge_cutoff:
- Training data ends September 2021
- Doesn't know latest library versions
- Missing recent security advisories
- Outdated best practices
hallucination:
- Confidently states incorrect information
- Invents plausible but wrong APIs
- Makes up function names
- Creates fictional libraries
context_limits:
- Conversation memory limited
- Can't see your full codebase
- No understanding of your architecture
- Missing business context
Where It Fails
Asked to use a recent API, ChatGPT invented plausible but non-existent function names. Asked about a complex architectural decision, it gave generic advice that ignored crucial constraints. Asked to debug code, it identified a symptom but missed the actual root cause.
The failures are particularly dangerous because they’re presented with the same confidence as correct answers.
Implications for Developers
Short Term
immediate_impact:
productivity:
- Faster boilerplate generation
- Quick answers to common questions
- Rubber duck debugging partner
- Learning acceleration
workflow_changes:
- "Ask ChatGPT first" becomes common
- Code review becomes more important
- Verification skills become essential
- Copy-paste coding risks
job_impact:
- Junior tasks become easier
- Senior judgment more valuable
- Code review skills critical
- Architecture skills premium
Longer Term
longer_term_implications:
skill_evolution:
declining_value:
- Syntax memorization
- Boilerplate writing
- Simple debugging
- Documentation lookup
increasing_value:
- System design
- Code review
- Security analysis
- Business understanding
- Problem definition
education:
challenge: Learning through struggle matters
risk: Skipping fundamentals
opportunity: Higher-level concepts earlier
How to Use It Effectively
Best Practices
effective_usage:
verification:
- Always test generated code
- Check for security issues
- Verify API calls exist
- Review for edge cases
prompting:
- Be specific about requirements
- Provide context
- Ask for explanations
- Request alternatives
boundaries:
- Don't use for security-critical code
- Don't trust without verification
- Don't skip understanding
- Don't share proprietary code
Integration in Workflow
workflow_integration:
exploration:
- "How would I approach X?"
- "What are the trade-offs between A and B?"
- "Explain this error message"
generation:
- Boilerplate and scaffolding
- Test case generation
- Documentation drafts
- Regex patterns
review:
- "Is there a bug in this code?"
- "How could this be more efficient?"
- "What edge cases am I missing?"
learning:
- "Explain how X works"
- "What's the difference between A and B?"
- "Show me an example of Y"
The Bigger Picture
What This Means
industry_implications:
democratization:
- Lower barrier to coding
- More people can build things
- Increased software supply
quality_concerns:
- More code, not necessarily better
- Security vulnerabilities at scale
- Maintenance debt
job_market:
short_term: Minimal impact
medium_term: Role evolution
long_term: Fundamental shift
competitive_advantage:
- Speed to market
- Quality differentiation
- Human judgment
- Domain expertise
My Prediction
We’re at the beginning of a fundamental shift in software development. ChatGPT and its successors won’t replace developers, but they will dramatically change what we do and how we do it. The developers who thrive will be those who learn to leverage these tools while maintaining the judgment and expertise that AI lacks.
The next few years will be fascinating.
Key Takeaways
- ChatGPT is remarkably capable at code generation, explanation, and debugging
- Critical limitations: no execution, knowledge cutoff, hallucination, context limits
- Always verify AI-generated code—confidence doesn’t equal correctness
- Short-term: productivity boost, workflow changes
- Long-term: skill value shift toward judgment and architecture
- Security-critical code still requires human expertise
- Use for exploration, generation, and learning
- Don’t use as a crutch—understanding still matters
- This is the beginning, not the end
- Embrace the tool while maintaining critical thinking
The future just got more interesting.