Future of AI: AGI, Reasoning Models and More

It feels like every week there's a new headline about AI-smarter models, bigger breakthroughs, and even talk of machines that can "reason". Behind the scenes, there's a full-on race underway. From the pursuit of artificial general intelligence to projects like "Stargate" that demand massive computing power, the pace of innovation is just amazing. Tech giants and research labs - OpenAI, DeepMind, Anthropic, Meta, xAI, Mistral, and others are all in, investing heavily in everything from next-gen foundation models to custom AI hardware. In this piece, we’ll walk through what these major developments are, how they work, and why they matter for all of us.

AGI (Artificial General Intelligence)

Now, what is AGI? AGI basically is a machine that can think, learn, and problem-solve as well as (or even better than) humans across pretty much all cognitive tasks. It’s not just about answering trivia or generating text like today’s AI. We’re talking full-on versatility and autonomy.

This isn’t narrow AI that’s good at just one thing; it’s AI that can do anything a person can do mentally, and maybe more.

You might think AI is already doing so many things, there are so many new models that have already come in, like GPT-4 or Google's latest Gemini 2.5. But it's all "Emerging AGI". They're impressive, sure, but still fall short of matching the average Unskilled human in many areas.

AGI could literally change everything. Ofc if we get it right. Imagine scientific breakthroughs happening at lightning-fast speed, real solutions to global challenges like disease and climate change, discovering drugs and more.

Ofc there are many risks too, even leaders warn of misuse, such as cyberattacks or biothreats, this would need a heavy investment in AI safety, alignment research and careful rollout of capabilities.

Now, where are we currently in terms of the progress?

Companies around the world are racing toward AGI. OpenAI and Google DeepMind are explicit about AGI timelines and safety work. Anthropic (whose name signals a focus on human-aligned AGI) is rapidly iterating its Claude models to handle more complex reasoning. Meta this year formed a “Meta Superintelligence Lab” under former OpenAI execs, lavishly recruiting top AI talent with packages up to $300, signifying its AGI ambitions. Even Elon Musk’s xAI vows to build a “good AGI” to rival. These moves underscore that the industry sees AGI as the ultimate prize.

If AGI arrives, it will reshape society. Possible benefits include curing diseases faster, eliminating routine drudgery, and solving global challenges. All the firms and policymakers must ensure robust governance and heavy investment in AI literacy and social safety nets.

Reasoning Models and AI Agents

Reasoning models unlike LLM which predict text token by token, reasoning models actually plan, chain thoughts, and use tools internally before answering. For example, let's take GPT o3 so it can "Agentically use and combine every tool within GPT" - meaning they decide when and how to use tools like web search, code execution, or image analysis to solve complex problems. Such models can outline multi-step solutions, check their own work, and handle subtasks internally, acting more like partners than black boxes.

Here AI agents come into the picture, AI agent is when you give the model the autonomy to take action - call API, web search, run code or even control a device, it becomes an Agent. AI agents might plan a sequence of tasks, invoke external tools or APIs, and adapt based on feedback. Crucially, agents are goal-driven and tool-using (not just free-form chatbots).

How do they differ from LLM?

A regular LLM chat simply answers your prompt statically. A reasoning model works in the background to improve the response. An agent takes initiative: it might follow up if it needs more data, or execute subtasks to meet your goal. For example - you could ask an agent to “research a topic and write a report,” and it might autonomously gather sources, summarize findings, and draft an answer.

AI agents are not just hype - they're here and already making a difference. To give you context, companies now aren't wondering if they should agentic system - they're now focusing on how to use them effectively to drive value

We can already see these agents showing up. In software development, tools like GitHub or Gemini code help developers write and debug code. In everyday operations, bots are now scheduling meetings, generating reports and managing workflows - quietly taking over the tasks that used to eat up time.

Take Google's new Gemini CLI, which is positioned as an "Open source AI agent" right in the terminal, it gives developers direct access to Gemini with support for up to 1 million token context windows, and it is huge for coding, research, and other complex tasks. It also assists in planning a multi-step solution, recovering from failed paths, and recommending fixes on its own. Basically, it’s like having a tireless assistant that thinks ahead.

The rise of reasoning models and agents means AI can handle longer, more complex workflows on its own. In business, this could automate research, report writing, data analysis, and software development. In everyday life, personal assistants might proactively manage schedules, bookings, and errands. However, fully autonomous agents also raise safety questions - I mean, who oversees an AI making real-world decisions? Companies should test agents thoroughly in controlled environments and maintain human oversight loops.

Massive computing and Infrastructure (e.g. Project Stargate)

If there's one thing that's clear in today's AI race. It's Bigger compute = better AI. Progress in AI has closely followed an exponential curve in computing power. Each new generation of models that comes in is only possible because of a massive increase in data and computational resources. OpenAI even notes that " with every new order of magnitude of compute comes novel capabilities". In other words, when you throw more computing at a model, it doesn’t just get better - it starts doing entirely new things.

For example, GPT-4.5, released in February 2025, was trained on far more hardware than GPT-4, leading to big jumps in creativity, reasoning, and contextual understanding.

Project Stargate and Beyond

To support this scale, tech companies and now even governments are launching some of the largest infra projects in history. Take OpenAI's project Stargate - a $500 billion multi-year plan with $100B already funded to build AI data centres in the U.S.

Oracle alone is reportedly providing around 4.5 GW of cloud capacity to OpenAI. For context, that's enough to power roughly 4.5 million US homes, and they're not alone. The UAS is backing its massive initiative - the Global Stargate, aiming for a 5 GW AI cluster with help from Nvidia and Cisco..

Across the globe, similar projects are emerging:

• Europe’s EuroHPC is rolling out exascale supercomputers like Italy’s JUPITER, which is expected to hit over 120 petaflops and scale up to exaflop performance by 2027.

• China is building out its own national AI clusters, often within tightly controlled ecosystems.

These enormous builds now aren't just technical feats - they come with huge energy demand. Data centre's power usage is already spiking. An MIT study estimates that by 2026, global data centres can consume 1050 terawatt hours per year - it is as much electricity as the entire country of Japan.

In North America, AI-driven demand pushed data centre consumption from 2.5GW in 2022 to 5.3 GW in 2023. And it’s not just electricity, cooling these centres requires massive amounts of water and climate-sensitive siting.

Agree or not, computing is now a geopolitical priority. Countries are treating AI infrastructure the way they'd treat nuclear power - as a strategic asset.

• The U.S. Stargate project explicitly aims to “secure American leadership” in AI.

• OpenAI’s new “OpenAI for Countries” initiative is helping governments build national computing capacity.

• At the same time, the U.S. is tightening export controls on AI chips and model weights to China.

• Europe’s AI Act includes language around “digital sovereignty” to promote homegrown data infrastructure.

• The UAE is positioning its multibillion-dollar AI centres as national prestige projects, backed by sovereign wealth.

Bottom line: Scale unlocks capability - but comes with trade-offs

More computing means faster innovation, better models, and new emergent capabilities. It creates a virtuous cycle: more computing → more data → bigger models → new capabilities. But it also concentrates power on only a handful of players, as not all can afford gigawatt-scale data centres. The sad part is India is lagging in this race, even though Reliance and Adani are stepping up in the data centre game, but "Where does India stand in the AI race?" is a topic that needs a separate article.

GPT-5 and the Future of Foundation Models: What’s Next for Frontier AI?

We’re entering a new chapter in AI, and it’s being shaped by the race to build smarter, more capable foundation models. OpenAI’s current top model, GPT-4.5, already shows what scale and smarter training can unlock, from a larger context window and better creativity to stronger “EQ” and real-time web browsing. But as powerful as it is, it still doesn’t truly reason; it reacts quickly, but doesn’t “think before it speaks,” as OpenAI itself puts it.

That’s where GPT-5 comes in.

OpenAI has hinted that future models will go beyond sheer scale, integrating reasoning as a core skill. The idea is to combine the raw power of GPT-4.5 with the thoughtful, step-by-step planning found in OpenAI’s more specialised O-series models. Imagine a system that can not only generate content but also solve, plan, and adapt - like a hybrid between a creative writer and a strategic analyst.

Where things are headed: Smarter, more versatile models

We’re seeing a clear trend: every generation of foundation models is becoming more multimodal, more context-aware, and more capable of handling complex tasks. GPT-4.5 already works with both images and code. Google’s Gemini 2.5 Pro supports 1 million-token inputs — enough to process entire books or massive codebases at once.

So, when GPT-5 does arrive, we can expect it to take things further:

• Deeper reasoning and planning

• Better handling of visuals, audio, and real-time interaction

• More tool use and memory features, making agents feel more intelligent and persistent

OpenAI isn’t the only one in the game. Anthropic launched its Claude 4 suite in 2025, including models like Opus and Sonnet. These models are built for extended thinking, able to reason through thousands of steps, especially valuable in coding, decision-making, and agent workflows.

From screens to ambient AI

Startups like Humane and others are building radically different devices, like the AI Pin or Rabbit R1. These aren’t your typical phones or wearables. The AI Pin clips onto your clothing and projects a holographic display, while the Rabbit R1 relies entirely on voice and gestures, with no traditional screen at all.

The idea is to make AI an ever-present assistant you can speak to, not stare at.

Even OpenAI is getting into hardware. In 2025, it announced a partnership with famed designer Jony Ive through his studio LoveFrom. Together, they’re building AI-native consumer devices - potentially new types of wearables or smart assistants designed specifically for the generative AI era.

Everything looks so fascinating, right AI pin, Rabbit R1

But… it’s not all smooth sailing.

Building new AI-first gadgets is hard. Devices like Humane’s AI Pin received mixed reviews and were later discontinued by HP. The Rabbit R1 faced criticism for poor performance, in one case, it called a Dorito a taco. Reviewers pointed out that these devices often lacked reliability and useful everyday functionality. The hype was there, but the user experience wasn’t.

Still, the momentum hasn’t stopped. Apple is rumored to be working on AR glasses with integrated AI. And xAI might also jump into custom hardware.

BUT WHY ARE WE FOCUSING ON HARDWARE....????

Because AI-native hardware could fundamentally shift how we interact with technology. Imagine:

• Voice-first, screenless devices you talk to on the go

• Wearables that understand your environment through vision and sound

• Mixed reality assistants that overlay AI into the real world

This hands-free, always-on interaction could make AI feel more like a companion and less like a tool.

Conclusion

The future of AI isn’t some distant concept; it’s already reshaping how we work, build, and interact. From the pursuit of AGI and smarter reasoning models to mega-scale infrastructure, next-gen foundation models, AI-native devices, and shifting global dynamics, these trends are going to touch every industry.

Instead of panicking about AI taking our jobs, we should focus on how to prepare for it

For individuals, it starts with building AI fluency. Try out tools like GPTs, AI agents, and cloud-based services. See how they can simplify or enhance your workflow. Just as importantly, double down on human strengths, critical thinking, creativity, and empathy. These are the skills that AI can support, but not replace.

For organisations, the stakes are even higher. Strategic moves now will determine who adapts and who falls behind.

• Upgrade infrastructure or partner up for scalable compute.

• Retrain teams to work with AI, not against it.

• Put governance in place, think bias checks, safe deployment, and ethical data use.

• Stay ahead of regulations like the EU AI Act or principles from global bodies like the OECD.

It’s also a moment for collaboration. Open-source models, shared datasets, and collective safety testing (like red-teaming) can accelerate progress while reducing risk. The more inclusive the innovation, the more resilient the outcomes.

At the end of the day, this is a time for proactive adaptation. Adopt and include AI in design, research, customer engagement, and product development, but do it responsibly. Stay informed on the big ideas: AGI, reasoning agents, compute scale, powerful models, AI-specific devices, and the geopolitics that shape it all.

Those who understand these shifts and act with clarity, agility, and care will be the ones who lead. The pace of change will only accelerate, but with the right mindset and tools, we can not only keep up but shape what comes next.

Hope you enjoyed reading this article