From the East Agile AI and Software Development team

It's been an extraordinary week in AI development. As a consultancy deeply embedded in building AI-powered solutions, we're tracking several developments that matter for practical software development work.

Claude Sonnet 4.5: The New Coding Champion

Anthropic quietly released Claude Sonnet 4.5, and the early consensus is striking: it's outperforming GPT-5 and other recent releases for coding tasks. Just last week, everyone was talking about how impressive GPT Codex was. This week, those same voices are saying Sonnet 4.5 is better.

What makes this particularly interesting for development teams:

• Better, faster, and cheaper - The trifecta that actually matters for production use
• Automatic upgrades - Claude Code (Anthropic's CLI tool) is automatically using Sonnet 4.5 now
• Real cost impact - For teams using Claude's SDK in their agents and tools, this represents immediate savings

The timing feels intentional. Anthropic waited for OpenAI's move, then dropped something objectively superior on multiple dimensions.

The Rapid Model Evolution Problem

Google updated Gemini 2.5 Flash this week as well, creating what we're calling "model fatigue." For development teams, this creates a real challenge: which model do you standardize on when the landscape shifts weekly?

Our take: pick one that works well across your use cases and stick with it unless there's a compelling reason to switch. The constant churn is exhausting, and the differences at the top tier are narrowing. For now, Claude seems to have the edge for coding-heavy workflows.

AI Surpasses Human Performance in Algorithm Competitions

Several benchmark results caught our attention this week:

College-Level Algorithm Competition: ChatGPT achieved 12/12 on complex algorithm problems that typically stump students. The most interesting detail: for the hardest problems, they used two agents working together—one to investigate solutions, another to execute. This agentic approach beat single-model performance.

Economic Impact Assessment: Testing across 40 high-GDP-impact tasks with 1,300+ evaluations, AI models are reaching human expert levels:

• GPT-4.1: 52% (one percentage point below best human)
• GPT-5: 48%
• Best human expert: ~53%

The gap is closing fast.

The Skill Gap: Using AI Effectively

Here's what matters more than the benchmarks: the ability to use these tools effectively is becoming the differentiator.

We're seeing a real divide in our consulting work:

1. Skilled users can guide AI to produce excellent, production-ready output
2. Unskilled users produce plausible-looking work that may be fundamentally flawed—what we're calling "AI slop"

The problem with AI slop isn't that it's obviously bad. It's that it looks good enough to pass initial review but lacks the depth, accuracy, or appropriateness for the actual use case. Users without domain expertise or critical evaluation skills can't distinguish between quality output and sophisticated-looking garbage.

This creates a new kind of risk: instead of submitting nothing or obviously poor work, people submit confidently-presented work that may be completely wrong—and they don't know enough to tell the difference.

OpenAI's Strategic Moves: Pulse, Shopping, and the Conflict of Interest Problem

OpenAI announced three major products this week, and the strategic direction is clear: they want to be Facebook.

Pulse is a proactive social feed that surfaces cards based on your conversations and context. Combined with their new agentic shopping integration (pulling in Shopify merchants and Stripe payments), you can see where this is going:

1. AI analyzes everything you've said and done
2. Proactive feed suggests things you might want
3. Ads appear in that feed
4. Agentic shopping completes purchases on your behalf

The technical implementation is impressive. The conflict of interest is blatant. Would you trust an AI shopping agent that gets kickbacks from certain vendors?

For paid users, maybe ads won't appear. For free users, this becomes an extremely sophisticated advertising and commerce platform that knows everything about you.

Practical Implications for Development Teams

From our consulting perspective, here's what matters:

1. Claude Sonnet 4.5 is worth evaluating if you're doing significant coding work with AI assistance
2. Multi-agent patterns are effective for complex problem-solving—don't just throw everything at a single model
3. Skill development is critical - Your team's ability to effectively use these tools matters more than which model you choose
4. Domain expertise remains essential - AI augments but doesn't replace the need for humans who can evaluate quality
5. Build with flexibility - Model capabilities and pricing change weekly; architecture should accommodate easy model swapping

The Bottom Line

AI coding tools are genuinely approaching expert human performance on many tasks. But the value isn't in replacing developers—it's in augmenting them. The consultants and developers who master these tools become significantly more productive. Those who use them poorly create more problems than they solve.

The race isn't to replace humans with AI. It's to build teams that combine human judgment with AI capabilities effectively. That's the work we're focused on at East Agile, and it's where the real competitive advantage lies.