I Read The BOND Report...So You Don't Have To

You publish a piece thinking you've nailed the scope—then a 340-slide deck lands, and you realize you were still thinking too small.

Last week, the investment firm BOND released their latest report: "Trends – Artificial Intelligence" – a massive report diving deep into the current state of AI. The timing was perfect: it dropped just as I was putting the final touches on my workplace AI article, so I spent the day (well, my Claude agent did) going through every chart, every data point, and every insight they've compiled.

You don't want to spend your day parsing through a 7,000 line, 340 slide deck written by Mary Meeker et al. I have no fear. I've done it for you and extracted the things you most should know so you don't have to do that work.

A quick note: This is a bit off-topic from my usual focus on agentic programming tools (you can find those deep dives at HyperDev), but this report was so packed with fascinating insights about the broader AI landscape that I felt compelled to share it here on LinkedIn. Think of this as a follow-up to my recent AI workplace opportunity article – a lot of what this report reveals reinforces that thesis, but there are some genuinely surprising findings worth highlighting.

If you want the full picture, the complete BOND report is available here. But here are the things that caught my eye that may be new to you.

The Developer Reality Check

The most striking finding buried in the data: We're watching the rise of everyday AI-powered developers—'the AI dev next door.' NVIDIA's ecosystem went from 2.5 million to 6 million developers in just four years. Google's processing 50x more tokens year-over-year. Microsoft Azure's token processing jumped 5x.

But here's the kicker – it's not just about volume. The report shows that 63% of developers now use AI in their development process, up from 44% just one year ago. We're past the tipping point where AI coding assistance is optional.

What struck me most was the productivity data. Internal studies from major software teams report developers completing tasks 14% faster with AI assistance. That might not sound revolutionary, but compound that across millions of developers globally, and you're looking at a fundamental shift in how software gets built. Based on my own research, it's more likely that a small percentage of developers are seeing massive gains, whilst the majority are still dipping their toes in the water.

The Great Model Convergence

Something fascinating is happening with AI model performance. The report shows that by 2024, AI systems achieved 73% accuracy in passing as human in Turing tests – up dramatically from earlier versions. More critically for devs, the performance gap between different models is shrinking fast.

DeepSeek's R1 model scored 93% on advanced math tests compared to OpenAI's o3-mini at 95%. We're talking about a 2% difference for models that cost dramatically different amounts to run. This convergence is huge for developers – it means you can get near-frontier performance without frontier pricing.

The implications are clear: Model quality is no longer the moat. Execution, distribution, and specialization are.

China's AI Speed Run

Here's something that'll make you think twice about AI timelines: China's response time to AI innovations is dramatically faster than their internet adoption curve.

The report breaks down how China went from AI follower to legitimate competitor in just 2-3 years. DeepSeek, launched in January 2025, is already capturing significant global market share. Alibaba's Qwen 2.5-Max claims to outperform both DeepSeek and ChatGPT on key benchmarks.

But what's really remarkable is the efficiency angle. Chinese companies are achieving comparable AI performance with significantly lower training costs. They're not just catching up—they're building smarter. This pace puts pressure on everyone building in global AI markets.

The Infrastructure Reality

The capital expenditure numbers are staggering. The "Big Six" US tech companies are spending $212 billion annually on infrastructure – up 63% year-over-year. That's not a typo. We're talking about the largest infrastructure buildout in human history, happening right now.

Here's the part that made me pause: data centers are being built faster than houses. xAI's Colossus facility went from empty building to fully operational AI data center in 122 days. The average US house takes 234 days to build. If data centers beat homes in build speed, what does that say about the pace of AI deployment?

The Workplace Transformation Numbers

This section hit closest to home given my recent article on AI workplace adoption. The data here is both validating and sobering.

Shopify's CEO now considers "reflexive AI usage" a baseline expectation for all employees. Duolingo announced they're going "AI-first" and will only approve headcount if teams can't automate more of their work first. These aren't AI companies – these are businesses realizing AI adoption is existential.

The job posting data tells the story: AI-related job postings are up 448% over seven years, while non-AI IT jobs are down 9%. We're not just seeing new AI roles – we're seeing traditional tech roles evolve or disappear.

The Physical World Acceleration

One area the report covers that I don't touch on much: AI's expansion into the physical world is happening faster than anyone expected.

Tesla's fully self-driven miles jumped 100x over 33 months. Waymo now captures 27% of San Francisco rideshare gross bookings. These aren't lab experiments anymore – they're revenue-generating deployments at scale.

But what caught my attention was the industrial robot data. China has more industrial robots installed than the rest of the world combined. When AI starts controlling physical infrastructure at scale, the geopolitical implications become very real very quickly.

The Next 2.6 Billion Users

Here's a mind-bending insight: 2.6 billion people (32% of the world's population) still aren't online. But when they come online – thanks largely to satellite internet – they'll start with AI functionality from day one.

These users won't experience the traditional internet progression of browsers → search → apps. They'll start with conversational AI in their native language. Imagine skipping the entire application layer and going straight to agent-driven interfaces.

This could fundamentally reshape how we think about digital platform dominance. The winners might not be those who own the apps, but those who own the interface layer.

The Open Source Wild Card

The report highlights something I've been tracking: open source AI models are closing the performance gap faster than expected. Meta's Llama downloads jumped 3.4x to 1.2 billion in just eight months. Hugging Face now hosts 1.16 million AI models, up 33x from early 2022.

This matters because it's breaking the traditional big tech moats. When a startup can access near-frontier AI capabilities for free, the competitive dynamics shift dramatically. We're potentially looking at the democratization of AI in real time.

The Inference

The report confirms what many of us suspected but provides the scale: we're in the middle of the fastest technology adoption curve in human history. AI user growth is outpacing internet adoption. Infrastructure investment is unprecedented. Performance improvements are exponential.

But what's different this time is the simultaneity. Unlike previous tech waves that started in one geography and spread, AI is global from day one. China and the US are building parallel AI ecosystems. Open source and closed models are advancing in tandem.

For those of us building in the agentic coding space, this report is both validating and sobering. The opportunity is massive, but the pace of change means standing still is moving backward. The companies that figure out how to ride this wave – rather than get swept up by it – will define the next decade of technology.

The full BOND report is worth a read if you're tracking the macro shifts. But the takeaway is simple: AI isn't coming. It's here—and it's global, simultaneous, and compounding.

For those interested in how this translates specifically to development workflows and agentic programming tools, check out more of my analysis at HyperDev.

What patterns are you seeing in your own work that align with these trends? And more importantly, how are you adapting to stay ahead of this curve?