By Dr Chafic Chaya, Regional Manager, Public Policy and Government Affairs, Middle East, RIPE NCC

A business launching an artificial intelligence service rarely thinks about Internet routing. A company moving its applications to the cloud does not normally ask whether its country has deployed IPv6. And when consumers make a digital payment, stream content or access an online government service, they certainly do not think about where networks exchange traffic. They notice these things mainly when something fails.

For many years, discussions around digital infrastructure focused primarily on coverage and speed. Connecting more people and businesses, expanding fibre networks and increasing mobile broadband capacity were natural priorities. Those objectives remain important, but they are no longer sufficient.

As economies become increasingly dependent on cloud computing, artificial intelligence, digital financial services, connected industries and online government services, another question is becoming just as important: can the Internet infrastructure underneath these services scale securely and remain resilient when disruption occurs?

The next phase of digital competitiveness will therefore require us to look beyond bandwidth.

From connectivity to capability

The Internet is becoming the operating environment for entire economies. Factories depend on connected systems. Financial institutions depend on real-time transactions. Governments deliver essential services digitally. Businesses increasingly rely on cloud platforms located across different networks and jurisdictions. Artificial intelligence adds another layer of demand through large-scale data processing, distributed computing and machine-generated traffic.

Connectivity is moving beyond simple availability toward quality, reliability, affordability and resilience. This shift matters because digital innovation can only scale when the underlying infrastructure scales with it.

A country may have excellent broadband coverage, but businesses will still face limitations if networks cannot exchange traffic efficiently, if addressing resources constrain future growth, if routing is vulnerable to errors or attacks or if international connectivity depends on too few pathways.

This is why digital infrastructure needs to be understood as an ecosystem rather than simply as a collection of telecom networks.

Telecom operators are essential, but no network operates alone

Telecom operators remain central to this ecosystem. They make substantial investments in fibre, mobile networks, backbone infrastructure and international capacity. Continuing those investments is essential as traffic grows and businesses demand faster and more reliable services.

But the Internet is fundamentally a network of networks. Its resilience depends not only on individual operators, but also on how networks interconnect with one another and how effectively the wider technical ecosystem functions.

Internet Exchange Points allow networks to exchange traffic locally, while data centres bring content and computing resources closer to users. Submarine cables and terrestrial routes provide international connectivity. The Domain Name System enables users to find services, and Internet Protocol addresses allow billions of devices and services to communicate. Routing systems determine how information travels between networks. Weakness in any of these layers can affect the services built above them.

This is an important distinction. Building a resilient digital economy cannot be the responsibility of telecom operators alone. It requires cooperation between network operators, Internet service providers, data centres, cloud platforms, governments, regulators and the technical community.

The invisible foundations of scalability

Some of the most important investments in the Internet receive relatively little public attention.

The Internet Protocol version 6 (IPv6) is one example. IPv6 is the latest version of the Internet Protocol. As the supply of IPv4 addresses has long been exhausted at the global level, IPv6 provides the much larger pool of Internet addresses needed for the Internet to continue expanding and for new digital services and technologies to grow, while reducing dependence on increasingly complex mechanisms used to extend the life of IPv4. For businesses, governments and operators planning for millions of additional connected devices, cloud workloads and digital services, IPv6 should increasingly be considered basic infrastructure for future growth rather than an optional technical upgrade.

Another example is routing security. Every day, networks around the world exchange information about how Internet traffic should reach its destination. Mistakes or malicious announcements can redirect traffic or make services unreachable. Resource Public Key Infrastructure (RPKI) provides a mechanism that helps network operators verify whether a network is authorised to announce particular Internet address resources.

Local interconnection is equally important. When two networks operating in the same market can exchange traffic locally through efficient interconnection and Internet Exchange Points, data may no longer need to travel thousands of kilometres through another country and a different jurisdiction before returning to nearby users. The result is lower latency, greater efficiency and improved resilience.

Internet measurement completes the picture. Policymakers and operators need reliable data to understand how traffic flows, where connectivity is concentrated, where dependencies exist and how networks react during disruptions. You cannot strengthen what you cannot see.

Resilience has a cost, but so does fragility

One of the harder questions is economic. Network redundancy costs money, as do alternative international routes. Maintaining multiple upstream connections, deploying security measures, training engineers and continuously upgrading infrastructure all require investment.

In competitive markets, operators understandably need to balance these investments against commercial realities. The solution, however, cannot simply be to minimise infrastructure costs.

Digital dependency changes the calculation. When banking, healthcare, government platforms, logistics, cloud services and business operations depend on continuous connectivity, the economic impact of prolonged disruption can quickly outweigh the cost of building greater resilience.

Failures across interconnected digital systems can cascade into other sectors. For governments and businesses, resilience should therefore increasingly be treated as an investment characteristic, not simply as an emergency response.

The objective is not to eliminate every possible failure, as no network can guarantee that. Instead, the goal is to avoid unnecessary concentration, introduce diversity wherever practical, continuously improve security and ensure that systems can recover quickly.

The Middle East is moving from adoption to infrastructure maturity

Saudi Arabia and the United Arab Emirates provide a useful example. The lesson is that Progress tends to occur when policy attention, technical capacity building and implementation by network operators reinforce one another. Infrastructure transformation is rarely achieved through regulation alone, nor through technology alone. It requires sustained cooperation between policymakers and the people who actually operate networks.

The race to build AI capacity is attracting billions of dollars in investment across our region and around the world. But compute without connectivity cannot deliver value. Connectivity without resilience cannot guarantee continuity. And infrastructure without cooperation cannot scale indefinitely. The strongest digital economies will therefore not simply be those with the most infrastructure. They will be those with Internet ecosystems that are open, interconnected, secure, scalable and resilient enough to support whatever comes next

Kayvan Karim, Programme Director of MSc Software Engineering, School of Mathematical and Computer Sciences, Heriot-Watt University Dubai 

For the past few years, the AI infrastructure race has largely been measured in scale: more GPUs, larger data centres and greater power capacity. That expansion is continuing, but the economics are beginning to change. Competitive advantage may increasingly depend not only on securing additional power, but on extracting more useful AI work from the power already available. Part of the reason is a change like AI demand. We are moving from relatively simple prompt-and-response systems towards agents that can reason across multiple steps, call tools, inspect results, revise plans and continue working autonomously. Anthropic’s latest Economic Index notes that Claude usage is increasingly shifting towards long-running agentic tasks and that more computationally intensive conversations tend to be associated with higher-value outputs.

This transition could have significant implications for infrastructure demand. A chatbot might generate one answer to one prompt. An agent performing a software-development, research or business task may invoke a model dozens of times, maintain a large context, call external tools and generate many intermediate reasoning steps before producing an outcome. Demand may therefore grow in two ways: more people using AI and more inference, model calls, and tokens processed for each task. Major AI laboratories are already working on this efficiency problem. OpenAI says one of its primary inference objectives is to serve more tokens from the same hardware, using techniques including scheduling, caching, kernel optimisation and improved model implementation. It also describes GPT-5.6 as being trained to accomplish more work per token. Google DeepMind is pursuing a similar direction: its Gemini 3.6 Flash was designed for scaled agentic workloads and uses fewer output tokens than its predecessor on several evaluations. In contrast, its recent agentic video system reduced token consumption by up to 88% for that workload.

Other approaches address efficiency at the model architecture level. DeepSeek-V3, for example, uses a Mixture-of-Experts design with 671 billion total parameters but activates 37 billion per token, so only part of the network is used for each computation. Meta has similarly worked on inference efficiency through grouped-query attention and more efficient tokenisation; the Llama 3 tokeniser was reported to require up to 15% fewer tokens than Llama 2 for equivalent text. Taken together, these approaches show that model capability is increasingly being developed alongside the cost of delivering it.

Model-level efficiency, however, is unlikely to remove the infrastructure constraint on its own. Global data-centre electricity consumption was approximately 415 TWh in 2024, according to the International Energy Agency, and its base case projects this to reach around 945 TWh by 2030. AI is expected to drive most of that growth. Efficiency is therefore improving while aggregate demand continues to rise. One reason is the Jevons, or rebound, effect: efficiency improvements reduce the resources required for each unit of work, but lower costs can also encourage greater overall use. If agents become much cheaper to operate, organisations may respond by deploying more of them, running them for longer, or applying them to tasks that were previously uneconomic. Efficiency can reduce the compute required for an individual task while still increasing total demand.

That increased demand meets infrastructure that cannot expand as quickly. Models and software can improve quickly, but grids, substations, transformers and power-generation infrastructure usually have much longer development cycles. The IEA notes that while a data centre can sometimes be developed within two or three years, the broader energy infrastructure required to support it often involves longer planning and construction periods. Where grid capacity is constrained, each available megawatt becomes a more valuable production resource. The amount of power available remains important, but so does the amount of useful computation that can be produced within that power envelope.

That changes how we should understand capacity. Improvements in accelerator performance, model architecture, workload scheduling, caching, utilisation and inference software can increase computational output without increasing a site’s electrical connection. OpenAI’s recently reported Jalapeño inference hardware illustrates the direction of travel: the company says the chip can deliver more AI work per unit of power while increasing throughput and reducing latency. Efficiency can therefore act as a form of virtual capacity. If two operators each control 100 MW, but one can consistently deliver substantially more useful AI work within that power envelope, their nominal capacity may be identical while their productive capacity is not.

The same constraint applies to physical space and cooling. AI systems are concentrating more computational power into individual racks, increasing both power density and heat output. Packing more accelerators into the same building only creates useful capacity if the electrical and thermal infrastructure can support them. This is one reason liquid cooling is moving from a specialist technology towards a more central part of AI data-centre design. Microsoft, for example, has introduced a closed-loop chip-level cooling architecture that it says eliminates evaporative water consumption for cooling and could avoid more than 125 million litres of water annually per data centre. The example also shows why power, cooling, water use and rack density cannot be treated independently.

The same shift creates a measurement problem. Power Usage Effectiveness, or PUE, has been valuable for showing how much facility energy is required beyond the electricity IT equipment consumes. It does not, however, measure whether that IT equipment is producing useful work efficiently. Uptime Institute’s 2025 survey placed average PUE at around 1.54 and noted that the headline industry figure had changed little for six years. Uptime has consequently argued for productivity measures that relate computational work to energy consumption. For AI inference, tokens per kilowatt-hour might offer one operational measure. Still, even that is incomplete: an efficient model that solves a task in 1,000 tokens may be more valuable than one generating 10,000. A more useful long-term measure may therefore be useful AI work per unit of energy, water and infrastructure.

Capacity will remain essential. The AI industry will continue to build larger data centres, secure new power supplies and deploy large quantities of computing hardware. As agentic systems create more persistent inference demand and physical resources become harder to expand, however, efficiency may increasingly determine the productive value of that capacity. Operators that can support more useful computation within the same power, cooling, water, and space constraints can accommodate more workloads without waiting for equivalent growth in physical infrastructure.

For AI infrastructure, installed megawatts will remain a headline measure. The more consequential measure may increasingly be how much useful AI work those megawatts can support.

Alteryx, the agentic analytics and automation company, today announced new AI capabilities across Alteryx One that connect enterprise-grade business logic directly to the AI agents’ teams already use. By extending governed workflows and datasets to external AI tools, organizations can “build once and govern once,” eliminating the need to recreate complex business logic from scratch. This approach allows enterprises to scale AI action with confidence, helping to reduce both security risks and runaway token costs.

Key Findings

• 71 percent of IT leaders report that AI initiatives are most successful when IT and business teams collaborate closely to bridge the gap between AI agents and enterprise business logic.
• NextWave achieved a 20x reduction in LLM token consumption using an Alteryx workflow during a complex Office of the CFO reconciliation between front-office and back-office data.
• Up to 93 percent reduction in token consumption and up to 85 percent increase in speed on tasks involving raw, ungrounded data, when combining an LLM with an existing, trusted Alteryx workflow.
• Up to 83 percent reduction in token costs and up to 65 percent increase in speed on tasks involving clean, grounded data.
• 65 percent of analysts confirm that AI delivers the most value when business logic is managed at the business level.

“Generative AI is brilliant at brainstorming, but it often struggles with the precision required for enterprise execution. Organizations don’t need agents that guess at business rules and burn through tokens; they need AI that operates on the same trusted business logic and governance that underpin the rest of the business,” said Ben Canning, Chief Product Officer at Alteryx. “By connecting existing tools to a governed business logic layer, we are allowing enterprises to stop the ‘re-work’ tax of rebuilding business rules for every new agent, ensuring that every AI-driven action is as reliable as the calculations they already trust.”

New Capabilities

The latest capabilities include:

Ask Alteryx: With this release, Ask Alteryx evolves from an embedded assistant into the primary way users interact with Alteryx One, guiding new users step-by-step through their first workflow in Designer and giving everyone a natural-language front door to their data through Ask Alteryx for Live Query, with connections to Snowflake, Big Query, and Databricks for reading and writing data directly. Ask Alteryx checks existing workflows and data first, delivering a governed answer when one exists or building a new workflow when it doesn’t, with every output remaining inspectable, editable, reusable, and schedulable within Alteryx One.

Agent Studio: Enables business users to turn existing, already-governed datasets into conversational agents without rebuilding anything. Analytics teams maintain full control over which datasets and KPIs power agent responses, while finance and operations departments can instantly scope an agent to their reconciliation dataset or KPI dashboard data, so stakeholders can ask trend, root-cause, and variance questions in plain language and get governed, explainable answers back.

Alteryx Insights for OpenAI: Available through the ChatGPT Plugin Directory, this capability allows business users to generate answers based on analyst-approved data, calculations, and workflows. Employees can investigate revenue variances or resolve reconciliation issues directly, accessing trusted business logic without opening the platform or requiring an Alteryx seat. Alteryx will be expanding this surface integration strategy to bring governed logic to where teams already collaborate, including upcoming support for Claude, Gemini, Slack, and Microsoft Teams.

Alteryx MCP Server: The governed way to use Alteryx from whatever AI platform or agent you already work in. It lets AI agents interact with Alteryx as easily as a human would, finding the right data, building multi-step solutions, and turning them into governed, repeatable workflows. Agents can build, run, schedule, and discover Alteryx assets directly, with external AI requests inheriting Alteryx’s authentication, workspace context, role-based access controls, and permissions automatically, so every interaction carries the same security model and audit trail as if a person had done it. More capabilities, including connection creation and expanded scheduling, are expected to roll out later this year on the same governed connection.

Alteryx Skills: A GitHub install that teaches third-party agentic interfaces, OpenAI Codex, Microsoft Copilot, Claude Code, Gemini CLI, and more, how to build Alteryx assets the right way, closer to how Ask Alteryx already builds them. Rather than each tool guessing at Alteryx’s patterns on its own, Skills gives them Ask Alteryx’s own workflow-building know-how, so a financial calculation or reconciliation workflow gets built correctly the first time, without rebuilding logic or permissions separately for every tool your team uses.