Satya Nardella Thinks Agentic AI Will Replace SaaS

 

Agentic AI Could Change User Interface (Again)

The annual letter penned by Satya Nadella, Microsoft CEO, points out the hoped-for value of artificial intelligence agents which “can take action on our behalf.” 

That should have all sorts of implications. Today, users typically issue commands or input data, and software executes tasks. With agentic AI, software would do things on a user’s behalf without some amount of explicit work on the user’s behalf. 

When arranging a meeting about a subject, the agent might query attendee calendars, send out invites and prepare an agenda, instead of the many steps a human might otherwise undertake. 

That might change the way some types of software are created, allowing non-technical people to create apps. A user might tell an agent to “build a basic web app for a recipe database,” without coding knowledge. 

Lots of other manual tasks might also be automated. Think of photo tags. Instead of manual tag creation, an agent could automatically tag photos and create collections. 

Agents might draft routine reports or monitor and adjust system performance, without active human intervention. Where today software “waits” for a directive, agents would work in the background, anticipating what needs to be done, and often doing that. 

Agents could also enhance levels of personalization already based on user behavior and preferences that might not always be explicitly stated. 

There are several key changes in user interaction with computers and software. First, a shift in user interface: “a new natural user interface that is multimodal,” he says. 

Think back to the user interfaces of the past, and the progression. We started with command line interfaces requiring typing on a keyboard in a structured way. No audio, no video, no speech, no gestures, no mouse or pointing. 

Over time, we got graphical, “what you see is what you get” mouse-oriented interactions, which were a huge improvement over command line interfaces. Graphical interfaces meant people could use and control computers without the former technical knowledge. 

Era	Time Period	Interface Type	Key Features	Impact on Usability
Batch Processing	1940s–1950s	Punch Cards	Input via physical cards with holes representing data and commands.	Required specialized knowledge; interaction was slow and indirect.
Command-Line Interfaces (CLI)	1960s–1980s	Text-Based Commands	Typing commands into a terminal to execute programs or tasks.	Greater flexibility for users but required memorization and technical expertise.
Graphical User Interfaces (GUI)	1980s–1990s	Visual Desktop Interface	WYSIWYG (What You See Is What You Get) design; icons, windows, and mouse control.	Made computers accessible to non-technical users; revolutionized personal computing.
Web-Based Interfaces	1990s–2000s	Internet Browsers	Interfacing through websites using hyperlinks and forms.	Simplified information access and expanded computer use to online interactions.
Touchscreen Interfaces	2007–present	Multi-Touch Gestures	Direct manipulation of elements on-screen using fingers.	Intuitive for all age groups; foundational for smartphones and tablets.
Voice Interfaces	2010s–present	Natural Language Commands	Voice assistants like Siri, Alexa, and Google Assistant.	Enabled hands-free operation but often struggles with context and nuance.

Beyond that, AI should bring multimodal and multimedia input and output” speech, images, sound and video. Not just natural language interaction, but multimedia input and output as well.

Beyond that, software will become more anticipatory and more able to “do things” on a user’s behalf. 

Nadella places that within the broader sweep of computing. “Can computers understand us instead of us having to understand computers?”

“Can computers help us reason, plan, and act more effectively” as we digitize more of the world’s information?

The way people interact with software also could change. Instead of “using apps” we will more often “ask questions and get answers.”

Nvidia Jetson Gives Generative AI It's Own Platform

 

Now here’s a switch: Nvidia hobbyist AI computers, supporting generative artificial intelligence apps (chatbots, for example) created using the Nvidia Jetson platform. 

The new machines are designed for hobbyists, commercial AI developers, and students to create AI applications, including chatbots and robotics, for example. 

Jetson modules are optimized for running AI models directly on embedded systems, reducing the need for sending data back and forth to the cloud, crucial for real-time decision-making in applications such as robotics, drones, autonomous vehicles, smart cameras, and industrial Internet of Things, for example. 

The developer kit features an NVIDIA Ampere architecture GPU and a 6-core Arm CPU, supporting multiple edge AI applications and high-resolution camera inputs and sells for about $250. 

 The Jetson modules are designed to enable edge AI applications that require real-time, high-performance processing at the edge rather than in a centralized cloud environment. Use cases might include autonomous vehicle real-time image and sensor data processing to enable navigation and decision-making. 

Robotics use cases include object recognition, motion planning, and human-robot interaction. The modules also can support smart cameras for security applications that require object detection, face recognition, and anomaly detection. 

Industrial Internet of Things use cases include the monitoring of machinery and systems used for real-time analysis. Unmanned aerial vehicles use cases include visual navigation, obstacle avoidance, and image-based inspection. 

At least in one respect, the Jetson is a sort of upside-down case of computer development. Personal computers started out as hobbyist machines entirely for edge computing (though we did not use the term at the time). 

Connected computing at remote locations (cloud computing) developed later. For AI, sophisticated remote processing in the cloud or enterprise-style data centers happened first, and now we get development of platforms aimed strictly at edge, “autonomous” computing without the requirement for connection to remote processing.

Will AI Actually Boost Productivity and Consumer Demand? Maybe Not

A recent report by PwC suggests artificial intelligence will generate $15.7 trillion in economic impact to 2030. Most of us, reading, seeing or hearing that estimate, will reflexively assume that means an incremental boost in global economic growth of that amount. 

Actually, even PwC says it cannot be sure of the net AI impact, taking into account all other growth-affecting events and trends. AI could be a positive, but then counteracted by other negative trends. 

Roughly 55 percent of the gains are estimated from “productivity” advances, including automation of routine tasks, augmenting employees’ capabilities and freeing them up to focus on more stimulating and higher value adding work, says PwC. 

As much as we might believe those are among the benefits, most of us would also agree we would find them hard to quantify too closely. 

About 68 percent will come from a boost in consumer demand: “higher quality and more

personalized products and service” as well as “better use of their time,” PwC says. Again, that seems logical enough, if likewise hard to quantify. 

source: PwC 

Just as important, be aware of the caveats PwC also offers. “Our results show the economic impact of AI only: our results may not show up directly into future economic growth figures,” the report states.

In other words, lots of other forces will be at work. Shifts in global trade policy, financial booms and busts, major commodity price changes and geopolitical shocks are some cited examples. 

The other issue is the degree to which AI replaces waning growth impact from older, maturing technologies and growth drivers, and how much it could be additive.

“It’s very difficult to separate out how far AI will just help economies to achieve long-term average growth rates (implying the contribution from existing technologies phase out over time) or simply be additional to historical average growth rates (given that these will have factored in major technological advances of earlier periods),” PwC consultants say. 

In other words, AI might not have a lot of net additional positive impact if it also has to be counterbalanced by declining impact from legacy technologies and other growth drivers. 

Thank PwC consultants for reminding us how important assumptions are when making forecasts about artificial intelligence or anything else. 

AI Agents are to AI as the Web and Broadband Were to the Internet

In the early days of the internet, people could mostly share text on bulletin boards. Web browsers allowed us to use video, audio and text. We moved from learning things or sharing things to being able to "do things." We could shop, for example, transfer money and consume all sorts of video and aural content (podcasts, streaming entertainment video). 

Our use of artificial intelligence is likely to move through some similar stages, particularly the ability to shift from learning things (answering text questions) to analyzing things to "doing things," which is what AI agents promise. 

When Will AI's "iPhone Moment" Happen?

Where are the expected big winners from AI, over the longer term, beyond the immediate winners such as Nvidia in the graphics processor unit market? To put it another way, when will some firm, in some industry (existing or emerging) have an "iPhone moment" when the value of AI crystalizes in a really-big way?

In other words, even if most firms and people are eventually expected to profit from using AI, will there be new Googles, Facebooks, Amazons, and if, so, where will we find them?

Here's the gist of the problem: ultimate winners that fundamentally reimagined or created entire industries were not obvious. In the internet era, experts thought their ideas were impractical or impossible.

Amazon initially was viewed as just an online bookstore. 

When Larry Page and Sergey Brin first developed their search algorithm at Stanford, most people didn't understand how a search engine could become a multi-billion dollar company, as even the revenue model was unclear. 

Netflix was often mocked by traditional media and entertainment companies. Blockbuster (the video rental retailer) had an opportunity to buy Netflix for $50 million in 2000 and declined. Blockbuster is gone; Netflix leads the streaming video market. 

When first launched, Airbnb would have seemed to many a risky concept, especially for hosts renting space in their own lived-in homes. 

The idea of getting into a stranger's personal car as a transportation method might have seemed radical and unsafe when Uber first launched.

Many thought the concept of transferring money online seemed dangerous (PayPal). 

The point is that big winners are often hard to discern. And even when a field is considered promising, eventual winners often look just like all their other competitors, at first. 

Right now, most of us seem to agree that infrastructure (GPUs; GPU as a service; AI as a service; transport and data center capacity) is the place where significant gains are obvious.

Beyond that, there is much less certainty.

We might not experience anything “disruptive” or “revolutionary” for some time. Instead, we’ll see small improvements in most things we already do.

And then, at some point, we are likely to experience something really new, even if we cannot envision it, yet. 

Most of us are experientially used to the idea of “quantum change,”  a sudden, significant, and often transformative shift in a system, process, or state. Think of a tea kettle on a heated stove. As the temperature of the water rises, the water remains liquid. But at one point, the water changes state, and becomes steam.

Or think of water in an ice cube tray, being chilled in a freezer. For a long time, the water remains a liquid. But at some definable point, it changes state, and becomes a solid. 

That is probably how artificial intelligence will feature hundreds of evolutionary changes in apps and consumer experiences that will finally culminate in a qualitative change. 

In the history of computing, that “quantity becomes quality” process has been seen in part because new technologies reach a critical mass. Some might say these quantum-style changes result from “tipping points” where the value of some innovation triggers widespread usage. 

Early PCs in the 1970s and early 1980s were niche products, primarily for hobbyists, academics, and businesses. Not until user-friendly graphical interfaces were available did PCs seem to gain traction.

It might be hard to imagine, but GUIs that allow users to interact with devices using visual elements such as icons, buttons, windows, and menus, was a huge advance over command line interfaces. Pointing devices such as a  mouse, touchpad, or touch screen are far more intuitive for consumers than CLIs that require users to memorize and type commands.

In the early 1990s, the internet was mostly used by academics and technologists and was a text-based medium. The advent of the World Wide Web, graphical web browsers (such as  Netscape Navigator) and commercial internet service providers in the mid-1990s made the internet user-friendly and accessible to the general public.

Likewise, early smartphones (BlackBerry, PalmPilot) were primarily tools for business professionals, using keyboard interfaces and without easy internet access. The Apple iPhone, using a new “touch” interface, with full internet access, changed all that. 

The point is that what we are likely to see with AI implementations for mobile and other devices is an evolutionary accumulation of features with possibly one huge interface breakthrough or use case that adds so much value that most consumers will adopt it. 

What is less clear are the tipping point triggers. In the past, a valuable use case sometimes was the driver. In other cases it seems the intuitive interface was key. For smartphones it possibly was a combination of elegant interface; multiple-functions (internet access in the purse or pocket; camera replacement; watch replacement; PC replacement; plus voice and texting) 

The point is that it is hard to point to a single “tipping point” value that made smartphones a mass market product. While no single app universally drove adoption, several categories of apps--social media, messaging, navigation, games, utility and productivity-- all combined with an intuitive user interface, app stores and full internet access to make the smartphone a mass market product. 

Regarding consumer AI integrations across apps and devices, we might see a similar process. AI will be integrated in any evolutionary way across most consumer experiences. But then one particular crystallization event (use case, interface, form factor or something else) will be the trigger for mass adoption. 

For a long time, we’ll be aware of incremental changes in how AI is applied to devices and apps. The changes will be useful but evolutionary. 

But, eventually, some crystallization event will occur, producing a qualitative change, as all the various capabilities are combined in some new way. 

“AI,” by itself, is not likely to spark a huge qualitative shift in consumer behavior or demand. Instead, a gradual accumulation of changes including AI will set the stage for something quite new to emerge.

Users and consumers are unlikely to see disruptive new possibilities for some time, until ecosystems are more-fully built out and then some unexpected innovation finally creates a tipping point moment such as the “iPhone moment,” a transformative, game-changing event or innovation that disrupts an industry or fundamentally alters how people interact with technology, products, or services. 

It might be worth noting that such "iPhone moments" often involve combining pre-existing technologies in a novel way. The Tesla Model S, ChatGPT, Netflix, social media and search might be other examples. 

We’ll just have to keep watching.

AI Increases Data Center Energy, Water E-Waste Impact, But Perhaps Only by 10% to 12%

An argument can be made that artificial intelligence operations will consume vast quantities of electricity and water, as well as create lots of new e-waste. But higher volume of cloud computing operations--for conventional or AI purposes--is going to increase in any event.

And higher volume necessarily means more power and water consumption, and use of more servers. 

Some portion of the AI-specific investment would have been made in any case to support the growth of demand for cloud computing. 

So there is a “gross” versus “net” assessment to be made, for data center power, water and e-waste purposes resulting from AI operations. 

By some estimates, AI will increase all those metrics by 10 percent to 12 percent. It matters, but not as much as some might claim. 

By definition, all computing hardware will eventually become “e-waste.” So use of more computing hardware implies more e-waste, no matter whether the use case is “AI” or just “cloud computing.” And we will certainly see more of both. 

Also, “circular economy” measures will certainly be employed to reduce the gross amount of e-waste for all servers. So we face a dynamic problem: more servers, perhaps faster server replacement cycles, more data centers and capacity, offset by circular economy efficiencies and hardware and software improvements. 

Study Name	Date	Publishing Venue	Key Conclusions
The E-waste Challenges of Generative Artificial Intelligence	2023	ResearchGate	Quantifies server requirements and e-waste generation of generative AI. Finds that GAI will grow rapidly, with potential for 16 million tons of cumulative waste by 2030. Calls for early adoption of circular economy measures.
Circular Economy Could Tackle Big Tech Gen-AI E-Waste	2023	EM360	Introduces a computational framework to quantify and explore ways of managing e-waste generated by large language models (LLMs). Estimates annual e-waste production could increase from 2.6 thousand metric tons in 2023 to 2.5 million metric tons per year by 2030. Suggests circular economy strategies could reduce e-waste generation by 16-86%.
AI has a looming e-waste problem	2023	The Echo	Estimates generative AI technology could produce 1.2-5.0 million tonnes of e-waste by 2030 without changes to regulation. Suggests circular economy practices could reduce this waste by 16-86%.
E-waste from generative artificial intelligence"	2024	Nature Computational Science	Predicts AI could generate 1.2-5.0 million metric tons of e-waste by 2030; suggests circular economy strategies could reduce this by up to 86%1 2
"AI and Compute"	2023	OpenAI (blog)	Discusses exponential growth in computing power used for AI training, implying potential e-waste increase, but doesn't quantify net impact
"The carbon footprint of machine learning training will plateau, then shrink"	2024	MIT Technology Review	Focuses on energy use rather than e-waste, but suggests efficiency improvements may offset some hardware demand growth

The point is that the specific impact of AI on energy consumption, water and e-waste is significant. But the total data center operations footprint is not caused solely by AI operations. Computing cycles would have grown in any case. 

So we cannot simply point to higher energy, water and e-waste impact of data centers, and attribute all of that to AI operations. 

Measure	Total Data Center Impact	AI Workload Contribution	AI as % of Total Impact
Energy Consumption	~200-250 TWh/year globally	~20-30 TWh/year	~10-12%
Water Consumption	~600-700 billion liters/year	~60-90 billion liters/year	~10-13%
E-Waste Contribution	~3.5-4 million metric tons/year	~350-500 thousand tons/year	~10-12%