2026 won’t be remembered for GPT-5.5, Claude, or Gemini topping another benchmark. It will be remembered as the year AI efficiency became an engineering discipline. Optimization is now happening at every layer of the stack, from smarter models and context engineering to tool compression, persistent memory, retrieval, orchestration, and governance. The next AI leaders won’t be the companies with the biggest GPU clusters. They’ll be the ones that make those clusters far more efficient, and Canada is well positioned to lead that shift.
This Canada Day, I found myself reflecting on how quickly AI has moved in the past six months.
Like many Canadians, I spent July 1st celebrating what this country has built and thinking about where we’re headed. But sitting on the dock at Koshlong Lake later that evening, another milestone occurred to me.
We’re at the halfway point of 2026.
And I don’t think this year will be remembered as the year of GPT-5.5, Claude Code, Gemini, or any other individual model.
I think 2026 will be remembered as the year AI efficiency became an engineering discipline.
My Wake-Up Call
Over the past few months, Claude Code has completely changed how I develop software.
It’s become my pair programmer, architect, researcher, and debugger. Entire weekends disappear as ideas turn into working software faster than I thought possible.
It also exposed a new bottleneck.
Despite generous usage limits, I kept hitting token limits on large projects. Long conversations, multiple agents, documentation, code reviews, debugging sessions. It adds up fast.
For the first time, I wasn’t asking how to get a smarter model. I was asking how to use the model I already have more efficiently.
That question sent me down a rabbit hole.
We Spent Three Years Building Bigger Models
From 2023 through 2025, nearly every AI announcement followed the same pattern. Larger context windows. More parameters. Better benchmarks. Better reasoning. Faster inference.
The assumption was simple: bigger models solve more problems.
That was true, for a while.
Today the frontier models are already remarkably capable. GPT-5.5, Claude, Gemini, and open-weight models like Llama and DeepSeek can handle most of what we throw at them.
So the conversation is starting to change.
The question is no longer whether the model can perform the task. It’s whether we can do the task with fewer tokens, less latency, lower cost, and higher reliability.
Every Layer of the AI Stack Is Being Optimized
What excites me most isn’t one breakthrough. It’s that optimization is now happening everywhere, and a whole category of tools and companies is forming around it.
Layer 1: Smarter Models
The foundation models keep improving. Better reasoning means fewer retries. Better planning means fewer wasted tool calls. Even incremental gains here ripple through the entire stack.
Layer 2: Context Engineering
We’re finally recognizing that prompts aren’t just instructions. They’re software.
Context engineering has emerged as a discipline focused on sending the right information to the model, and only the right information. Less noise, more signal, fewer wasted tokens.
Layer 3: Tool Optimization
One of my favourite discoveries this year has been RTK.
It’s a wonderfully simple idea. Instead of sending thousands of lines of terminal output back to Claude Code, RTK compresses command results while preserving the information that actually matters.
The model isn’t smarter. The conversation is.
It’s one of the clearest examples I’ve seen of engineering beating brute force.
Layer 4: Persistent Memory
Why should an AI have to relearn your project every morning?
Persistent memory is becoming one of the most important innovations in enterprise AI. Rather than resending architecture documents, coding standards, business rules, and historical decisions over and over, memory systems let AI retain and retrieve knowledge over time.
You get lower token consumption, sure. You also get better collaboration.
Layer 5: Knowledge Retrieval
Enterprise AI shouldn’t search everything. It should search the right thing.
Modern retrieval systems are getting much better at delivering only the documents, APIs, and knowledge required for the task at hand. The goal isn’t larger context windows. It’s better ones.
Layer 6: Agent Orchestration
One of the most interesting announcements this week came from Ottawa-based Backboard.
Rather than launching yet another frontier language model, the company introduced an enterprise AI platform built around orchestration, persistent memory, inference optimization, and software engineering.
That caught my attention because it reflects exactly where I believe the industry is heading. The future isn’t one super-intelligent AI. It’s multiple specialized agents working together efficiently.
Orchestration decides which model handles each task, which knowledge gets retrieved, what previous work gets remembered, how context is shared, and how costs are kept down.
This is systems engineering, not just machine learning.
Layer 7: Governance and Observability
The least glamorous layer may also be the most important.
Organizations increasingly want answers to questions like: Which teams consume the most tokens? Which prompts are inefficient? Which agents succeed? Which workflows should be redesigned? What is our cost per business outcome?
AI is becoming infrastructure. Infrastructure gets measured.
Canadian Innovation Has a Unique Opportunity
Canada helped pioneer modern AI. Researchers like Geoffrey Hinton and Yoshua Bengio laid much of the scientific foundation today’s systems rely on.
Now we have a chance to lead again. Not by building the next trillion-parameter model, but by building the systems that make every model more useful.
Backboard’s announcement is one example. Canada’s growing sovereign AI initiatives are another. Our strengths in enterprise software, cybersecurity, governance, and public sector innovation set us up well for this next chapter.
The next AI leaders may not be the companies with the biggest GPU clusters. They may be the ones that make those clusters far more efficient.
Efficiency Is the New Benchmark
I’ve written before about token efficiency because I believe we’re still underestimating it.
As AI agents become commonplace, a single business process might generate dozens of agents, hundreds of tool calls, thousands of retrieval operations, and millions of tokens. Multiply that across an enterprise and AI quickly becomes an infrastructure challenge.
The winners won’t simply have smarter models. They’ll have smarter systems. Systems that remember, retrieve, orchestrate, compress, and measure.
Looking Ahead
Standing at the midpoint of 2026, I’m more optimistic about AI than ever. Not because models are getting bigger, but because the industry is getting smarter about how we use them.
The first wave of AI was about proving what was possible. The second wave is about making it practical.
When we look back on 2026, I don’t think we’ll remember it as the year another model topped another benchmark. We’ll remember it as the year the industry stopped chasing raw intelligence alone and started chasing efficiency.
I suspect that’s where the biggest breakthroughs are still to come.
Further Reading
- The Next Frontier in AI: Token Efficiency
- Ottawa’s Backboard Signals the Next AI Race: Better Systems, Not Bigger Models
Frequently Asked Questions
What does “AI efficiency” mean?
AI efficiency is the practice of completing a task with fewer tokens, less latency, lower cost, and higher reliability, rather than reaching for a bigger or smarter model. It treats optimization as an engineering problem across the whole stack, from the prompt and the tools to memory, retrieval, orchestration, and governance. The goal is to get more useful work out of the models we already have.
What are the layers of the AI optimization stack?
There are seven layers worth watching in 2026: smarter models, context engineering, tool optimization, persistent memory, knowledge retrieval, agent orchestration, and governance and observability. Each layer delivers incremental gains on its own. Stacked together, they change the economics of running AI at scale.
Why is token efficiency such a big deal?
As AI agents become common, a single business process can spin up dozens of agents, hundreds of tool calls, thousands of retrieval operations, and millions of tokens. Multiplied across an enterprise, that turns AI into an infrastructure challenge where cost and reliability depend directly on efficiency. The organizations that extract the most value from every token gain a lasting advantage over those that simply buy more compute.
Why does Canada have an opportunity to lead in AI efficiency?
Canada helped pioneer modern AI through researchers like Geoffrey Hinton and Yoshua Bengio, and it has real strengths in enterprise software, cybersecurity, governance, and public sector innovation. Those strengths line up with the systems layer of AI rather than the race to build the biggest model. Companies like Ottawa-based Backboard and Canada’s sovereign AI initiatives show what leading through better systems, not bigger models, can look like.
Does AI efficiency mean models will stop getting better?
No. Smarter models are still the first layer of the stack, and better reasoning and planning ripple through everything above them. The shift is that raw model size is no longer the only place progress happens. The biggest breakthroughs of 2026 are increasingly about how efficiently we use models, not just how large they are.
