Megaport spent a decade as a company you used to connect to other people’s clouds. On Wednesday it announced a plan to become one. The Australian networking firm secured four new AI infrastructure contracts worth a combined A$458.9M (about $329M) and launched a fully underwritten entitlement offer to raise A$827.3M (about $594M), according to its filing. The money funds a pivot from plumbing to compute.

The contracts come first. All four are with US-based technology providers running AI applications, are expected to start in the first half of 2027, and require nearly A$369.5M in capital expenditure, mostly for high-performance Nvidia GPUs alongside network and storage. That is a meaningful commitment for a company of Megaport’s size, and it explains why the raise is so large relative to the business.

What the capital is really buying is the strategy behind the contracts. Megaport says it will build a globally distributed AI inference cloud, anchored by an on-demand GPU pool backed by about A$350M in investment and offered to enterprise customers on both contracted and consumption-based pricing.

The pool is to be deployed across the company’s existing footprint of more than 1,100 connected data centres in 31 countries, with rollout over the next six to nine months.

The bet is geographic. Most GPU capacity today sits in a handful of enormous data centres optimised for training the largest models. Megaport is targeting the other half of the AI workload: inference, the act of running a trained model to answer a query, which benefits from being close to the user.

Its pitch is that a distributed network of smaller GPU pools, spread across the data centres it already connects, fits inference better than centralised mega-campuses, and slots into the gap between hyperscaler clouds and single-location GPU specialists.

It is a credible reading of where AI infrastructure is heading. As models move from research demos into products embedded in real applications, the economics shift from training to serving, and serving rewards proximity and distribution.

Megaport already owns the network that links the locations where that compute would live, which is a genuine structural advantage if the thesis holds.

The numbers around the raise were briefly muddled across early coverage, which is worth untangling. The four contracts are worth A$458.9M in total contract value; the capital raise is A$827.3M; the GPU pool commitment is about A$350M.

Several headlines collapsed these into a single figure. They are distinct: contract wins, the money to fund them, and the specific compute investment inside that money.

Megaport also tightened its 2026 revenue guidance to A$307M–A$315M and projected combined group pro forma annual recurring revenue of A$662.9M once the compute division is folded in. The shares were halted while the raise was arranged, a standard mechanism for a deal of this scale on the ASX.

The risk is the obvious one for any company spending heavily on Nvidia GPUs on the strength of contracts that begin in 2027: that AI infrastructure demand, and pricing, may look different by the time the hardware is installed and earning.

Megaport is committing capital now against revenue that lands later, in a market moving fast enough that 18 months is a long time. The contracts give it a floor. The inference-cloud ambition is the part that has to compound, and that is the part the A$827M is really betting on.

TL;DR

Perplexity AI announced a platform at Computex that dynamically routes AI inference between PCs and cloud servers in real time, acting as an “air-traffic controller” for AI tasks. The chip-agnostic system targets the cost crisis of centralised inference as Perplexity’s revenue hits $500 million.

Perplexity AI has developed a platform that dynamically splits AI workloads between personal computers and cloud servers, deciding in real time which tasks can run locally on a PC’s processor and which need the power of data centre hardware. CEO Aravind Srinivas announced the system at Computex in Taipei on Tuesday, describing it as an “air-traffic controller for AI tasks” designed to reduce the cost of inference, the process of running trained AI models to generate responses.

“You don’t want all your compute centralised in servers and everything running through the largest models,” Srinivas said in a Bloomberg Television interview. “You’re already reading reports of how people are freaking out about their cost. Some people are spending half a billion dollars per month. What you actually want is efficient value per watt per user.”

How it works

The system evaluates each AI task and routes it to the most efficient compute layer. Simple operations that modern PC processors can handle, such as summarisation, formatting, or lightweight classification, run locally without touching the cloud. More complex tasks that require large model inference, such as multi-step reasoning or retrieval-augmented generation across large datasets, get routed to cloud servers. The routing decision happens in real time, invisible to the user.

The practical effect is that Perplexity can serve more users at lower cost by offloading a portion of inference work to the billions of PCs already in circulation. As AI inference demand strains data centre capacity and drives utilities to plan $1.4 trillion in grid upgrades, distributing compute to the edge is both an economic and infrastructure necessity.

Srinivas made the announcement alongside Intel CEO Lip-Bu Tan, whose company leads the market for PC processors and has a commercial interest in making PCs a meaningful AI compute layer. However, Srinivas said the platform is “chip agnostic” and works with Nvidia processors as well. Nvidia highlighted the same edge-inference trend at Computex with its new RTX Spark platform for AI-powered laptops and desktops.

The cost problem

Srinivas’s reference to companies “spending half a billion dollars per month” on AI compute is not hyperbole. OpenAI’s infrastructure costs have been widely reported at that scale, and Anthropic’s projected $10.9 billion in Q2 revenue comes with substantial compute expenses that compress margins. The energy and cost burden of centralised AI inference is one of the defining constraints of the current AI boom.

Perplexity’s approach inverts the assumption that AI inference must happen in the cloud. By treating the PC as a first-class compute node rather than a thin client, the company can reduce its own server costs while potentially delivering faster responses for tasks that run locally. The tradeoff is complexity: the routing system must accurately assess task difficulty in milliseconds, and the quality of local inference depends on the user’s hardware capabilities.

Revenue efficiency

Perplexity’s financial trajectory underscores why cost efficiency matters. Srinivas posted on X in April that the company’s revenue grew fivefold, from $100 million to $500 million, while headcount increased just 34%. That ratio, roughly 15x revenue growth per employee added, reflects both the leverage of AI-native business models and Perplexity’s position as an aggregator that routes queries across multiple AI providers rather than training its own frontier models.

“Every time any of the AI gets better, our unified system also gets better because we route across all of them,” Srinivas said. The AI-native growth rates that are drawing capital away from traditional SaaS companies are partly enabled by this kind of architectural efficiency, where the product improves as its underlying providers improve, without proportional cost increases.

The hybrid compute platform extends that logic to hardware. If Perplexity can use the compute already sitting on users’ desks to handle a meaningful share of inference work, it reduces marginal cost per query and improves response latency for lightweight tasks. As AI moves deeper into enterprise workflows, the economics of who pays for the compute, the cloud provider, the AI company, or the user’s own hardware, will become a critical competitive variable.