AGI, Project Astra and Where AI Is Actually Going

What is AGI, and why does the definition matter?

Artificial general intelligence (AGI) is an AI system that can do most of the intellectual work a capable adult can do, across most fields, without being rebuilt for each task. That is the everyday meaning. The trouble is that the companies closest to building it each define it differently, and the definition decides whether the claim "AGI is near" is exciting, alarming or just accounting.

OpenAI's charter defines AGI as highly autonomous systems that outperform humans at most economically valuable work. Google DeepMind published a levels framework in late 2023 that grades systems from "emerging" through "competent", "expert" and "virtuoso" to "superhuman", and separately asks how broad the system is and how much it is allowed to do on its own. By that framework, the frontier chatbots of 2024 were already "emerging AGI": general in scope, but no better than an unskilled human at most of it.

Why this matters to a business owner: the useful question is never "has AGI arrived". The useful question is "which specific tasks in my company can a machine now do at a competent-employee level, and what happens to my data and my risk when I let it". That question has a practical answer today, and it changes every few months.

What is Project Astra?

Project Astra is Google DeepMind's research programme for a universal AI assistant: one that watches through a camera, listens, reads the screen, remembers what it has seen and answers in real time. It was shown at Google I/O in May 2024 as a phone and prototype-glasses demo that could identify objects, read code off a monitor and recall where the user had left their glasses a minute earlier.

By 2025, Astra stopped being a demo. Its camera and screen-sharing abilities were folded into Gemini Live on Android and iOS, so a user can point a phone at a broken router, a contract or a spreadsheet and talk about it. Google also showed Astra-style features heading into Search and into Android XR glasses built with eyewear partners. The through-line is that the assistant is no longer a text box you type into. It is a participant that perceives the same environment you do.

Astra is worth naming, rather than just "Gemini", because it represents the direction every serious lab is taking: multimodal perception, persistent memory and action in the world, packaged as an always-available agent. Microsoft is doing the same with Copilot, OpenAI with its agent products and voice models, Anthropic with computer-use agents and the Model Context Protocol that lets an assistant plug into business systems. The brand names differ. The shape of the thing is the same.

How close is AGI in 2026?

Nobody can say with confidence, and anyone who gives you a date is selling something, so the honest answer is a range of informed opinions with names attached. Sam Altman wrote in January 2025 that OpenAI was confident it knew how to build AGI as traditionally understood. Dario Amodei of Anthropic argued in late 2024 that "powerful AI" could arrive as early as 2026 and described what a decade of it might do for health and the economy. Demis Hassabis of Google DeepMind has repeatedly put AGI five to ten years out, and has said the systems still lack consistency, real reasoning and the ability to learn continuously.

What is beyond dispute is the trend that sits under those opinions. Each generation of frontier models handles longer tasks with less supervision: in 2023 an assistant could draft an email, in 2024 it could write and debug a working program, and by 2025 agents could complete multi-step jobs in a browser or a code repository over hours, with a human checking the result rather than each step. Independent measurements of how long a task a model can finish unaided have roughly doubled every several months. If that continues, the systems of 2027 will handle work that takes a person a full day.

So the realistic planning assumption for a South African business is not "AGI arrives on a date". It is "each year, a wider band of white-collar tasks becomes something software can do competently, cheaply and around the clock". Plan for the band widening, and the exact moment someone declares AGI becomes a headline rather than a shock.

What changes when assistants become agents?

The change is from advice to action: an assistant answers a question, an agent is given a goal, an account and permission, and then does the job. That single shift rewrites most of the risk model a business already has for AI.

• Data exposure becomes structural. A chatbot only sees what someone pastes into it. An agent connected to your mailbox, file shares and CRM sees everything those permissions reach. The question stops being "what did staff paste" and becomes "what can the agent read", which is a question about identity and access control, not AI.
• Mistakes execute. A wrong answer in a chat is caught when a person reads it. An agent that misreads an instruction can send the email, change the invoice or delete the folder before anyone looks. The controls that matter are the same ones you would put on a new junior employee: limited permissions, approval steps for anything irreversible, and logs.
• Perception adds a new surface. When an assistant sees through a camera and hears the room, whatever is on the whiteboard, the screen behind you or the client's paperwork on the desk is in scope. POPIA does not care whether a person or a model processed the personal information.
• Vendor terms decide ownership. Business tiers of Copilot, Gemini for Workspace, ChatGPT Enterprise and Claude for Work commit in writing not to train on your data. Consumer tiers generally do not. As agents gain access to more, that line in the contract is worth more than any feature.

What does this mean for a South African SME?

For a company of ten to three hundred people, the arrival of agentic AI is mostly good news with three local complications. The good news is that the productivity gains that used to need an enterprise budget now come bundled into Microsoft 365 and Google Workspace licences priced in rand per user per month.

The complications are these. First, POPIA puts the responsibility for personal information squarely on the business, whatever tool touched it, so an agent reading customer records needs the same lawful basis and safeguards as a staff member would. Second, data residency is now a real choice: Microsoft, Amazon and Google all run cloud regions in Johannesburg or Cape Town, and for regulated sectors it is worth choosing services that keep processing in-country where the option exists. Third, exchange-rate exposure: most AI tiers are dollar-priced, so a per-user add-on that looks cheap in a US blog post costs a South African company meaningfully more, and should be justified by a measured saving rather than enthusiasm.

There is also a skills point that cuts the other way. The same tools that threaten routine administrative work make a small, well-run South African firm able to compete with far larger ones on analysis, documentation and customer response. The businesses that benefit are the ones whose data is organised enough for an agent to use, which is usually the ones that already have their identity, files and backups in order.

What should a business do in the next 90 days?

The next 90 days should go on foundations rather than on picking a model, because every model will change twice before the year is out and the foundations will not. In order:

1. Fix identity first. Enforce multi-factor authentication for every account and turn on conditional access. Agents act with the permissions of the account they run under, so an unprotected account is now an unprotected robot.
2. Classify and label data. Decide what is public, internal, confidential and personal, and apply labels in Microsoft Purview or Google's data protection tools. Copilot and Gemini respect those labels; without them, an agent treats the board pack and the canteen menu the same way.
3. Write a one-page AI policy. Which tools are approved, on which tier, what may never be entered, who approves an agent being connected to a system, and how incidents are reported. A policy that fits on one page gets read.
4. Move staff to business tiers. Retire consumer accounts. The no-training commitment and the admin controls are the whole point.
5. Pilot one agent on one measurable job. Pick a task with a clear before-and-after, such as first-draft replies to a shared inbox or summarising weekly reports, give the agent read-only access at first, and measure hours saved and errors made for a month before widening it.
6. Log and review. Keep audit logs on, and have someone read a sample of what the agent did each week. This is how you catch the confident mistake before a customer does.
7. Train people on the new failure modes. Staff already know not to click phishing links. They also need to know that an assistant can be fed a malicious instruction hidden in a document or a web page, and that "the AI said so" is not a verification step.

What should a business not do?

Three mistakes come up repeatedly, and all three are avoidable. Do not ban AI outright, because staff will use it on their phones with none of your controls. Do not connect an agent to production systems with a global admin account "just for the pilot", because pilots have a habit of becoming permanent. And do not buy a licence for everyone on day one; buy for the team with the clearest use case, prove the saving in rand, then expand.

Where is AI going next?

The direction over the next few years is assistants that see, hear, remember and act, embedded in the tools a business already pays for, becoming steadily more capable at longer tasks. Whether the result is called AGI is a matter of definition. Its effect on a company is a matter of preparation.

The preparation is unglamorous: identity, data hygiene, a policy, a measured pilot and people who know what the new tools can and cannot be trusted with. Those are the same foundations that make a business secure and compliant today, which is why AMEA treats AI adoption as an extension of security and policy work rather than a separate project. Get the foundations right and every model release becomes an upgrade instead of a scramble.