Ricardo Silva’s Post

Is the era of "all-you-can-eat" AI coding officially coming to an end? GitHub has announced a fundamental shift for Copilot, moving from its long-standing flat-rate subscription to a token-based consumption model starting June 2026. While the monthly fees remain nominally the same, they will now function as a pre-paid credit balance. This change is driven by the rise of "agentic" workflows—complex, multi-step autonomous tasks that consume significantly more compute power than simple autocomplete suggestions. For developers and enterprise leaders, this marks a transition from predictable seat-based expenses to variable operational costs. While basic code completions remain exempt, high-intensity tasks like architectural planning and deep-dive debugging will now require careful credit management. This shift reflects a broader market trend where AI is maturing from an experimental add-on into a metered utility, much like electricity or water. How will this change your team's approach to AI integration? Will "prompt optimization" become a financial necessity rather than just a technical skill? #GitHubCopilot #GenerativeAI #SoftwareDevelopment #TechTrends #CloudComputing Read more: https://lnkd.in/gbhtiATq

GitHub Copilot is moving to usage-based billing by June 2026. Base plan prices stay, but now you'll get monthly AI Credits. Seems like those powerful "agentic" coding sessions are what's driving the change – makes sense for compute costs. Good thing basic code completions are still included! Businesses will appreciate the pooled credits and new admin budget controls. Smart move to offer a preview bill too. 💻💰 #GitHubCopilot #AI

Agentic AI is breaking traditional compute economics — and GitHub is feeling the pressure GitHub has paused new sign-ups for Copilot Pro, Pro+, and Student plans — not because of lack of demand, but because demand has fundamentally changed. We’ve moved from simple autocomplete to agentic workflows. And that changes everything. What’s happening under the hood? Traditional AI coding: → Single prompt → Single response → Predictable compute cost Agentic AI: → Multi-step reasoning → Self-correction loops → Parallel sub-agents working across the codebase This creates exponential compute growth, not linear. Every iteration expands context. Every step increases token consumption. A single “agent session” can now cost more than a user’s monthly subscription. GitHub’s response:: To prevent system-wide degradation, GitHub is enforcing: • Session limits (real-time circuit breakers) • Weekly token caps (total usage control) • Model access restrictions (removing high-cost models like Opus) Even more interesting: You can have unused requests but still get blocked due to token limits. What this means for developers AI is no longer “infinite.” You now have to think like a systems engineer, not just a user. You’ll need to: • Optimize prompts • Avoid unnecessary iterations • Choose models based on cost vs performance • Control parallel agent execution Your IDE is becoming a resource management interface, not just a coding tool. Bigger picture:: This is the same problem we’ve seen in distributed systems: The “noisy neighbor” problem — where one workload consumes disproportionate resources and impacts everyone else. Agentic AI just brought that problem to developer tools. My take:: We’re entering a new phase where: AI usage = infrastructure cost awareness The winners won’t be those who use AI the most… but those who use it efficiently. If you're building with AI agents today — how are you managing token usage and cost? #AI #Copilot #SoftwareEngineering #CloudComputing #DevTools #LLMs #Startup

The AI Honeymoon is OVER! GitHub Copilot and the rapid acceleration of AI "Enshittification". We all knew the golden era of highly subsidized, flat-fee AI access couldn't last. The compute costs driving today's LLMs are simply too immense. But the transition to a rigid, pay-for-what-you-use reality is accelerating at breakneck speed. Case in point: GitHub Copilot is gutting the value of their subscription plans. Here is the reality check for the software engineering world: The Death of the "Premium Request" Historically, for $10/month, users got 300 "premium requests" regardless of token weight. Starting June 1st, that's gone. GitHub is shifting entirely to a token-based credit system. The flat-fee safety net is vanishing. The Illusion of the Subscription Plan Retaining a Copilot plan now just acts as prepaid credits. The math is staggering: Massive Jumps: Top-tier models like Claude Opus are jumping from a 3x multiplier to an astonishing 27x. That is a 900% increase in cost per prompt! Zero Prepaid Benefit: Copilot's unit price for tokens perfectly mirrors direct API costs. Paywalls: Previously free models are removed, and features like code review using GitHub actions are now metered. The Hard Reality for AI and Engineering Stepping into this space as an AI Specialist, it is clear we are moving from an era of subsidized experimentation into a phase of rigorous ROI justification. Teams building internal tooling using direct API access will have a massive cost advantage over those relying on SaaS wrappers. The bottom line? Cut out the middleman. Take control of your own token usage and build workflows directly. The era of cheap AI is closing. Start budgeting your tokens accordingly. GitHubMicrosoft #AI #SoftwareEngineering #GitHubCopilot #LLMs #TechTrends #DeveloperTools #TechNews #Coding #Technology #ArtificialIntelligence #SoftwareEngineering #GitHubCopilot #TechTrends

Stop telling your friends to “just get GitHub Copilot.” GitHub has effectively admitted the model is under pressure. They’ve paused new Copilot Individual sign-ups, tightened usage limits, and reduced model access. Their reason is clear: agentic workflows now consume far more compute than the original plan structure was built to support. The AI gold rush is now meeting operational reality. #GitHub #GitHubCopilot #AI #GenerativeAI #SoftwareEngineering #DeveloperTools #DevOps #LLM #CodingAssistants

GitHub just paused new signups for Copilot Pro and Pro+ — and the reason is more interesting than it sounds. 🤖 Agentic AI broke the pricing model. When GitHub first designed Copilot's usage limits, the assumption was that developers would use it for autocomplete and chat. Occasional, bounded interactions. That world doesn't exist anymore. Today, agents run long-horizon tasks — spinning up parallel sessions, executing multi-step workflows, reviewing entire codebases. Some single requests now cost more than the entire monthly plan price. So GitHub hit the brakes. New sign-ups are paused. Usage caps have tightened. Session limits now sit alongside weekly token limits. And Opus models have been quietly removed from Pro plans — they're still in Pro+, which now offers more than 5x the limits of Pro. This isn't a failure — it's a reckoning the whole industry is heading toward. The economics of agentic AI are fundamentally different from conversational AI. When a tool stops waiting for human prompts and starts doing real work autonomously, consumption patterns change completely. Pricing models built for chat don't survive contact with agents. If you're building AI strategy for your organisation — or advising on AI tooling — this is worth watching closely. Vendors are still figuring out how to price agentic workloads. That means pricing changes are coming across the board, not just at GitHub. The question isn't whether your team's AI usage will grow. It's whether your budget and vendor agreements are ready for what that growth actually looks like. How are you thinking about AI cost governance as agentic tools become standard in your teams? Read the full announcement here: https://lnkd.in/g7TfhXHx #AI #AgenticAI #GitHubCopilot #AIStrategy #EnterpriseAI #Leadership #TechLeadership #AIGovernance #DataPlatform #CostManagement

GitHub Copilot is moving to usage-based pricing, and developers are raising concerns about predictability, value and rising costs for token-heavy workflows. With billing shifting from request-based units to AI credits tied to token consumption, users say the change could make usage harder to estimate and reduce the value of existing plans. See how developers are reacting to the change: https://lnkd.in/d8bi3uTj #AI #Copilot #SoftwareDevelopment #DevTools #GitHub

GitHub Copilot is evolving Starting June 1, 2026, Copilot will shift from premium requests to a usage‑based billing model powered by GitHub AI Credits. 💡 What this means for developers & teams: - Plan prices stay the same, but credits are consumed based on token usage. Code completions and Next Edit suggestions remain free. - Credits can be pooled, tracked, and topped up — giving enterprises more control. - Heavy users will need to monitor usage closely, while light users benefit from fairer pricing. - This change reflects Copilot’s growth from an in‑editor assistant to a full AI coding agent capable of multi‑step reasoning across repositories. 👉 The future of coding assistance is not just about features — it’s about sustainable, scalable AI access. #GitHub #Copilot #AI #UsageBasedBilling #DeveloperTools #SoftwareEngineering #TechNews #CodingAI #EnterpriseIT #Productivity

GitHub's upcoming policy shift on Copilot data—using interaction data to train models by default starting April 2026—raises an important question for our industry: who owns the intelligence generated during development? This isn't just a privacy issue. It's about the feedback loop that makes AI coding tools better. Every autocomplete, every rejection, every edit is training signal. GitHub is essentially saying: "Your coding patterns belong to us, unless you opt out." For teams building with AI agents, this matters deeply. If you're using Copilot while developing agentic systems, your architectural decisions, error patterns, and problem-solving approaches are being absorbed into the next generation of models. That's powerful for the ecosystem—but it also means you're contributing to the competitive landscape without explicit choice. The opt-out mechanism is important, but opt-out policies historically have low adoption rates. Most developers won't know this changed, let alone how to disable it. We think developers deserve clarity here: understand what data you're contributing, what it trains, and whether that aligns with your company's IP strategy. For enterprises building proprietary agents, this is a conversation worth having with your legal and security teams now—before April 2026. The broader lesson? As AI tools become infrastructure, the terms of engagement matter. The models that power our work are shaped by collective data. That's a feature, not a bug. But it should be intentional. What's your take—does this change how you think about using AI coding assistants? #AI #Developers #AgenticEngineering #GitHub

GitHub has become the main place where AI actually gets built. Not announced, not hyped - built. Over 4 million AI-related repositories now exist on the platform. That number from GitHub's Octoverse report genuinely surprised me. A 178% jump in LLM-focused projects in a single year is not slow growth. It's a wave. As someone working in QA, what I find interesting is how much of that activity is infrastructure. Agent frameworks, evaluation tools, RAG pipelines, model servers. The ecosystem is filling in fast. And a lot of it is open source, which means teams like ours can actually look at how these things work, not just consume the end product. I keep an eye on trending AI repos partly out of curiosity and partly because understanding what developers are building tells you a lot about what you'll be testing in 12 months. The patterns emerging now in agentic systems and tool use will be someone's production feature soon enough. #AI #OpenSource #SoftwareTesting