--- title: AGI Has Arrived. Now Give It a Simpler Problem. type: newsletter date: 2026-09-10 source: linkedin summary: "The next test of intelligence is knowing when not to use more of it. My LinkedIn feed is full of the same three words: “AGI has arrived.” Whether we have actually crossed that threshold will be debated for years. We do not need to settle it today. Because if…" newsletter: Technology Bytes draft: false --- ### The next test of intelligence is knowing when not to use more of it. My LinkedIn feed is full of the same three words: **“AGI has arrived.”** Whether we have actually crossed that threshold will be debated for years. We do not need to settle it today. Because if AI is now intelligent enough to trigger that debate, it is intelligent enough to take on a simpler assignment: **Make AI itself more efficient.** Not just more capable. Make it cheaper to run, less compute-intensive, lower in energy, carbon and water footprint, and above all, more reusable. **The most sustainable computation is the one you did not need to perform.** That is the problem worth solving next. --- ### The Intelligence Paradox We are building systems that reason, plan, code, research, use tools and execute complex workflows. Yet these systems waste resources in one specific and observable way: they solve a problem, then solve essentially the same problem again the next time a similar task appears. Every reasoning pass processes tokens. Every tool call consumes resources. Every retry adds compute. Every additional agent adds orchestration. Every inference translates into infrastructure, energy and cost. The result is a paradox: **AI is becoming more intelligent while spending significant resources rediscovering what it has already worked out.** An intelligent system should know how to solve a problem. It should also know when the problem does not need to be solved again. --- ### Optimize the Work, Not Just the Model A great deal of engineering already targets model-level efficiency through smaller models, better hardware, faster inference, quantization, more efficient architectures and better serving infrastructure. All of it matters. But there is another layer of efficiency that receives far less attention: **the work itself.** The industry keeps asking how efficiently a model can generate the next token. The better question is: **Did that token need to be generated at all?** The same applies to agentic systems. Do we need another model invocation, another tool call, another agent, another retry or another full-context reconstruction? Or has the system already learned enough to execute the task differently? This matters more as AI moves from occasional conversations into continuous operation across business processes. **At conversational scale, waste can be difficult to see. At process scale, it becomes a line item.** --- ### A New Optimization Objective Before executing a task, an efficient system should ask: **Have I solved something like this before?** **Is there a validated answer I can reuse?** **Does this require the larger model, or can a smaller one handle it?** **Do I need the entire context?** **Do I need to reason, or has this become a known procedure?** **Can I remove unnecessary tool calls, retries or agent handoffs?** **Can I achieve the same outcome with less compute, energy and cost?** Answering these questions changes the optimization target. Instead of optimizing only for maximum capability, the system optimizes for: **Required capability at minimum resource cost.** That is a different design principle, and it produces different systems. --- ### From Repeated Intelligence to Reusable Intelligence Real-world work contains both known and unknown situations. When something is genuinely new, uncertain or exceptional, use AI reasoning. That is where intelligence creates its value. But once the system has solved the problem and the result has been validated, something important should happen: **The system should stop paying to rediscover the same intelligence.** The pattern becomes: **Unknown → Reason → Validate → Capture → Reuse → Detect Exception → Reason Again** The captured intelligence can take many forms: a rule, a workflow, a code module, a decision table, a policy, a state machine, a test or another reusable executable artifact. The format matters less than the principle: **Reason when intelligence is required. Reuse when intelligence has already been acquired.** This is **Reusable Intelligence**. The decision between the two modes is not abstract. It can be expressed through a simple operating model: ReasonReuseThe problem is newThe problem is solved and validatedThe situation is an exceptionThe situation matches a known patternUncertainty is highThe outcome is predictableDiscovery creates the valueExecution creates the valueSpend compute deliberatelyMinimize repeated compute Over time, a well-designed system should shift more of its work from the left column to the right. That shift is measurable, and its benefits compound. --- ### Experts Do Not Reason From First Principles Every Time We often equate more reasoning with more intelligence: more tokens, longer thinking, more agents, more context and more tool calls. Sometimes that is necessary. But intelligence is not demonstrated by maximizing effort. When humans become experts, we do the opposite. We remember what worked. We recognize patterns. We create procedures. We build abstractions. We reuse prior knowledge and automate what has become predictable. We reserve deep reasoning for situations that actually require it. Advanced AI should work the same way. One sign of a mature intelligent system is the ability to recognize: **I already know how to do this. I do not need to reason through it again.** --- ### Efficiency Is Both an Economic and a Sustainability Objective The economics are direct. Fewer model calls, fewer tokens and less compute mean lower operational cost. The impact extends beyond the invoice. Lower compute demand can also mean lower energy use, lower carbon impact, lower water consumption and less pressure on infrastructure. These are not competing objectives. **They are increasingly the same objective, measured in different units.** Every unnecessary inference avoided reduces both operational cost and resource consumption. That is why AI efficiency should not be treated only as an infrastructure optimization. It should become part of the intelligence architecture itself. --- ### Benchmark Efficiency, Not Just Capability For years, AI progress has been measured through capability: accuracy, reasoning, coding, mathematics, context length and speed. Those benchmarks remain important. But they need a companion category: **Outcome per dollar.** **Outcome per unit of compute.** **Outcome per unit of energy.** **Reasoning avoided through validated reuse.** And perhaps the simplest measure is: **How few resources were required to produce the desired outcome?** If two systems produce the same quality of result and one requires a fraction of the compute, the second system is not merely cheaper. **It is better designed.** --- ### Spend Intelligence Where It Creates Value The goal is not less AI. The goal is to spend intelligence where intelligence creates value. Use powerful reasoning for the unknown: new discoveries, new designs, hard engineering problems, unusual exceptions, creative exploration and situations we have never solved before. But when a problem has already been solved, validated and operationalized, invoking expensive reasoning again may not add intelligence. It may simply add computation. The future therefore needs both: **Powerful reasoning for what we do not know, and efficient reuse for what we already know.** --- ### Has AGI Arrived? What Do You Think? That is the question filling feeds right now. But there is another question worth asking alongside it: **If AI is now intelligent enough for us to debate whether AGI has arrived, is it also intelligent enough to optimize itself?** Can it learn when to use a smaller model? Can it reduce unnecessary reasoning? Can it avoid repeated tool calls and retries? Can it reuse validated intelligence? Can it achieve the same outcome with less compute, less energy and lower cost? Can it progressively become more efficient as it learns? So, has AGI arrived? **What do you think?** And if your answer is yes, perhaps we should give it a simpler problem to solve: **Can AI make itself dramatically more efficient?** That means less repeated reasoning, less unnecessary compute, lower energy use, lower operational cost and far more reuse of validated intelligence. Because one of the strongest demonstrations of advanced intelligence may not be how much intelligence a system can use. **It may be how intelligently it decides when not to use more.** That is the shift from **Artificial Intelligence to Reusable Intelligence**. --- *Technology Bytes explores the shifts in AI, technology, economics and enterprise architecture that are changing how organizations think, build and operate. Subscribe to get latest updated.*