Category Archives: Ai

In early 2016, streaming giantNetflix, Inc. (NASDAQ: NFLX) announced that it had rolled out its service to 190 countries around the world. As the top provider of streaming content in the U.S., one of the biggest questions regarding the company's ability to succeed elsewhere was the issue of bandwidth. With slower internet speeds in many countries, would streaming performance suffer as a result?

With worldwide growth at stake, this was a question that the company needed to answer.

Turns out Netflix has a variety of tools it uses to navigate markets with underdeveloped bandwidth. Netflix CEO Reed Hastings was a keynote speaker at the 2017 Mobile World Congress in Barcelona, and in an interview with BBC broadcaster Francine Stock, he revealed some of the ways the company is addressing the issue.

Netflix AI tackles bandwidth issues. Image source: Pixabay.

The biggest revelation was regarding the use of artificial intelligence (AI). Netflix uses AI algorithms to review each frame of a video and compress it only to the degree necessary without degrading the image quality. This differs from previous technology that compressed the entire stream, but could cause fuzzy, pixelated or unclear images. This new method, which Netflix calls the Dynamic Optimizer, was developed to address bandwidth issues in emerging markets.

This not only improves streaming quality over slower speeds, but also tailors content for customers that view Netflix on tablets and phones, as is the case in countries like India, South Korea, and Japan. Providing video streaming at the same quality, but requiring lower bandwidth, also addresses the issue of data caps imposed locally by mobile providers.

Netflix collaborated with the University of Southern California and the University of Nantes in France to train the system, using hundreds of viewers and hundreds of thousands of scenes. By rating each scene individually on a variety of quality metrics, the AI system learned to determine image quality. Hastings described the technological advancement like this:

What we've done is invest in the codex, the video encoders, so that at a half a megabit, you get incredible picture quality on a 4- and 5-inch screen. Now, we're down in some cases to 300 kilobits and we're hoping someday to be able to get to 200 kilobits for an amazing picture. So we're getting more and more efficient at using operators' bandwidth.

Innovative solutions to technical problems. Image source: Netflix, Inc.

Hastings explained that the company was investing in many other ways to make buffering a thing of the past. He stated that the company was working on interconnect agreements with internet service providers (ISPs) across the globe, which provide increased speeds as the result of a more direct connection. Netflix has developed its own content delivery network in a program it calls Open Connect. It provides specialized Netflix servers directly to the ISPs, whose sole function is to deliver Netflix content to local subscribers.

Netflix has been working on tailored solutions for video encoding for a number of years. In a Netflix Tech Blogentry from 2015, the company described a complex method for analyzing each title and assigning it a specific encoding rate based on the genre and complexity of the scenery. An action movie might have significant motion, fast-moving objects, rapid scene changes, explosions, and water splashes versus an animated title that produces significantly less distortion at the same bandwidth. These variables were considered across the company's vast library of TV shows and movies and each title was assigned its own compression rate, thereby providing the best quality at the lowest bandwidth.

As competition increases and Netflix continues its expansion into less-developed markets, technological innovations such as these will provide a key competitive advantage. Rather than relying on local market forces and providers to dictate terms, the company is using novel solutions to address its challenges.

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Netflix Is Using AI to Conquer the World... and Bandwidth Issues - Bloomington Pantagraph

From voice translation to self-driving automobile, AI's impact in everyday life will become more and more apparent this year. The AI and deep learning market will experience even more rapid technological advancement, very rapid growth and adoption, and increasing competition for both hardware and software platforms. While AI fears will remain, the public will become more cognisant and comfortable with social media AI applications.

Deep learning training lends itself to what we call "High Density Processing". High density processing applies when algorithms are computationally intensive, having higher ratios of compute operations per byte of memory bandwidth.

In such cases dense clusters of multicore CPUs hosting accelerator technology can provide highly favourable cost-performance and performance per watt. GPUs, because of their ability to provide high density processing, have enabled deep learning computations and have dominated recently.

In 2017, we will start seeing a move from the near monopoly of GPUs for training to hosting on a rather wide variety of multicore and accelerator technologies. These will include Knights Landing/Knights Mill chips and AI accelerators implemented as FPGAs or ASICs. But the GPU will still be widely used.

Short fixed-point arithmetic can offer order of magnitude performance advantages over floating point (see here). These low-power solutions with special purpose architectures can demonstrate better price/performance than even GPUs.

Cloudy options abound with Amazon, Baidu and Microsoft having augmented their GPU-based cloud offerings with FPGA options for AI applications, and Google "supercharging" their Cloud AI with ASICs known as Tensor Processing Units employing short arithmetic.

Intel will also be a leader in bringing accelerators to market, and the combination of Knights Landing plus the Nervana Engine technology to be unveiled later this year looks particularly intriguing.

So in 2017, GPU dominance will be eroded. We think it is premature to talk about a "post-GPU" era, and expect GPUs to maintain a very comfortable lead, but we do expect a much richer mix of technologies to emerge.

Deep learning isn't just about the hardware; software libraries that enable algorithms that take advantage of said hardware and that put the technology into more hands are maybe even more important. We're seeing several libraries battle for dominance in the AI arena. Google's TensorFlow has leapt to the forefront on GitHub and Intel recently responded with their BigDL deep learning framework for Spark. Theano, Microsoft's CNTK and many others the vast majority of which have CUDA support will compete eagerly for developer mindshare. It's too early to call the race, but our prediction is that Microsoft and Intel are the most likely companies to give Google a run for the money.

What's in it for Microsoft is promotion of their software ecosystem, especially around Big Data and IoT. Intel wants increased hardware sales, not surprisingly. And Google appears to be most interested in growing their developer ecosystem to gather new applications that they can then monetise in areas such as self-driving automobiles.

Voice translation will be one of the biggest breakout application segments. International travellers will begin to use it regularly on their mobile phones for short conversations, including ordering food and coffee, buying train tickets, and other shopping.

Text translation in messaging apps has become routine, especially for certain language pairs, facilitating communication between lovers, family and friends, and international project team members.

The many "Lost In Translation" occurrences lead to abundant laughter, frustration and misunderstandings, and even breakups. Despite the limitations of machine translation, the appeal will be irresistible. Usage in personal social interaction will initially be much greater than for business. Could this be the killer app for consumer AI?

AI fears in some respects will ease as the public becomes more cognisant and comfortable with AI applications that are accessed from, or that support applications running on, their mobile-based social media platforms (Google, Facebook, and Twitter in particular).

But concerns around governments' electronic monitoring of social media content and face recognition in public spaces will remain. Facebook, Twitter and others will struggle with the appropriate level of tuning of AI solutions to filter out fake news, offensive videos, and hate speech. Their complicity with nondemocratic government requests for censorship will grow at the expense of freedom of expression. In addition, concerns around middle-class job losses to automated machine learning systems will continue to grow as the globalisation backlash continues.

A raft of AI applications in healthcare including for diagnosis, patient monitoring, and even clinical trials will make steady progress, but there will be no major breakthroughs.

Patient interest will grow significantly as AI healthcare case studies become more numerous and positive outcomes recorded. AI will be seen in a very positive light for medical imaging evaluation and diagnosis, and will begin to lead to significant cost savings. Trial usage for laboratory tests (blood, urine) will grow, but lag usage for imaging applications significantly.

Although patients might be concerned about robots replacing doctors, it will be enhancement, not replacement that is relevant. Generally the patient will not know when doctors and nurses are using AI to support healthcare decisions.

As one example, IBM's Watson technology reached the same diagnosis as oncologists in 99 per cent of cancer cases examined, yet it was also able to explore a wider range of options, since it can extremely rapidly explore the medical literature. Second opinions will be arrived at in realtime, which will save everyone time and money.

Self-driving automobile ("auto-automobile") technology will advance, but will experience speed bumps and citizen backlash as a growing number of trials leads to more accidents, including fatalities, even as statistics point to a significant reduction in accidents. Local and national government restrictions will tighten, and trials will increasingly be focused on lower-risk driving scenarios.

In the high-risk arena, military interest in self-driving ground-based vehicles will become very evident, due to the potential savings of lives and money, and the prospects for using such vehicles to confuse the enemy since soldier casualties will be removed from the equation.

So, to sum up: You're not going to exclusively use GPUs as your AI engine forever, and you're going to have a wide range of choices when it comes to AI libraries. You'll be using AI language translators for pick-up lines on your next international business trip, making you more comfortable with AI applications, but you'll still be afraid of what the government might do with the same technology, and that an AI might take your job.

You'll be healthier because AI medical care applications will speedily diagnose and recommend treatments for your injuries and ills. And, finally, there's a slightly better chance you'll need this enhanced medical care since self-driving cars will be tested in greater numbers. Phew.

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What 2017 holds for AI: Will you fear or embrace our machine overlords? - The Register

Jessica Pothering

Jessica is a business and finance writer, focusing on impact investing, social entrepreneurship and economic development. She previously reported for financial publications covering the global private equity, real estate and insurance markets.

AI see, AI do.

Among the risks in using data-driven AI in low-resource or at-risk communities is that the algorithms will magnify systemic biases.

Care must be taken to prevent AI systems from reproducing discriminatory behavior, such as machine learning that identifies people through illegal racial indicators, write researchers from the Stanford One Hundred Study on Artificial Intelligence.

This week, ImpactAlpha is extracting nuggets from Stanfords century-long effort to understand AIs long-term possibilities and dangers. Theres already an update to yesterdays #2030 segment on self-driving cars: Ford recently announced a $1 billion investment in software for autonomous fleets.

The researchers found AI could be a money-saving lifeline for budget-strapped local and state governments.

Illinois Department of Human Services, for example, is using predictive data modelling to improve prenatal care to high-risk pregnant women.

Cincinnati is using AI to identify and inspect properties that arent up to code.

AI also has potential for developing low-cost community health campaigns, which are otherwise difficult to target and expensive to implement.

This post originally appeared in ImpactAlphas daily newsletter.Get TheBrief.

Photo credit: Scienceofsingularity.com

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Underserved communities: Leveraging AI to improve health and social services in low-resource areas - ImpactAlpha (registration)

Atul Kumar, Chief Product Officer at Mintigo suggests marketers what they can achieve realistically using AI in 2017

2016 was a tremendous year for MarTech. According to Forrester Tech Radar for B2B Marketing Technology and SD16 (SiriusDecisions conference 2016), two of the hottest trends last year were ABM (Account-Based Marketing) and Predictive Marketing. Im sure you all are now busy with deploying your ABM initiatives. Many of you have embraced or thinking of Predictive Marketing.

AI is the red hot topic that is being discussed at c-level in all organizations. We all are beneficiaries of AI in our daily lives; from Alexa and Siri to TacoBot, we are reaping the rewards of AI. Googles AI (AlphaGo) beat the world master in the game GO. In the later half of 2016, Salesforce.com announced Salesforce Einstein while Oracle promised to do better with applications built with Adaptive Intelligence. And self-driving cars are coming soon! Its clear that AI powered business and consumer applications have arrived and you need to be ready to have an intelligent conversation with your boss and peers.

Im sure youve heard of many different terms such as AI, predictive analytics, machine learning, neural networks, deep learning etc. all used interchangeably by the industry. AI is an umbrella term, a branch of computer science whereas machine learning, deep learning etc. are some of the methods and systems of enabling AI. For example, the virtual assistants or Bots, excellent examples of AI, actually use NLP/G (natural language processing/generation) to understand and respond to human requests. There is no need to panic when you hear different terms or some vendor try to tell you that we do AI and others dont! Whats more important that you understand what AI can do for you.

Here is what you can achieve realistically using AI in 2017:

1. Account and lead selection - AI can help you select your best accounts and leads for your inbound, outbound and ABM initiatives. AI platforms such as the one my company (Mintigo) offers, help you to build predictive models for any business scenario, from cross/up-sell to new product launches. You can then explore your total available market using discovery tools offered by these platforms. You can also get new lead names (look-alikes) for your campaigns as needed based on predictive insights.

2. Personalized 1:1 Nurtures - One of the key outputs of predictive analytics is a set of attributes that represents the model. These attributes, often referred to as ideal customer DNA or profile, defines the why or the reasoning of a predictive score. Why John Smith @IBM is more likely to buy your product or services as compared to Andy Cheng @HP. By comparing these sets of attributes for the two, you can easily understand the reasoning. Some of the AI platforms have the ability to provide you detailed attributes into your MAP & CRM systems in real-time. This allows you to nurture an inbound inquiry or send an outbound message in a very personalized manner; for example, if your ideal customer DNA includes modern marketers who spend more than average on digital marketing and use one of the marketing automation systems, you can engage your prospects by sending them offers and messages that are relevant to their needs. AI eliminates the generic messages, and engage your prospects with the right (and relevant) message at the right time.

3. Automated Campaigns - This is an advanced application where an AI application automatically builds the customer journey to accelerate time to sales. This is accomplished by engaging the right prospects with the right offers using the right channels at the right time. Simply put, it is the martech nirvana that only AI can enable. A good percentage of MAP users still use a single step email nurture (nice way to say batch and blast!). Lack of adoption of multi-step nurture can be attributed to lack of resources and time it takes to create one. Those who do run multi-step nurtures, are doing so with limited data and utilizing the art of marketing. The Automated AI driven campaigns solve this issue. However, to take advantage of this you need to organize your content and track each LP uniquely (channel, offer, offer type) using utm codes or equivalent. The target and response data must be available or use a system that offers test and learn abilities.

4. Sales Engagement - Helping sales to intelligently engage with the precious leads and accounts is crucial for the success of your business. AI applications are changing the conventional B2B sales in many different ways; automated conversational Bots (such as Conversica) and other task management bots are good early examples. Other AI applications, such as the one from Mintigo (Mintigos Predictive Sales Coach), are enabling sales to engage their prospects intelligently. These applications use artificial intelligence (AI) to identify the Who, What, Why, and How? of the sales process. To be more specific, Who will buy from your company, What will they buy from you, Why they need it, and How you should engage them in a meaningful conversation. Selling is a tough business and require tremendous amount of research despite enormous efforts put in enabling sales. Many enterprises hire fresh grads to dial for the dollars; the churn could be high if they were not continuously enabled. Marketing creates content that is often buried behind corporate firewalls in content stores. What is needed is always-on intelligence and messaging/content that enables sales to easily find the right prospect and then quickly understand his/her needs and has messages and content at their fingertips to have a meaningful conversation. AI applications are here to help you not only build trust relationship with your counterparts in sales but impact revenue directly.

This list is by no means complete or even close. Numerous AI driven applications are being born everyday. This provides a starting point that will help you navigate the world of AI for marketing. And finally, dont forget to watch ex machina to explore the art of possibility.

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AI for B2B Marketers What to Expect in 2017? - MarTech Advisor

Is there a uniform set of moral laws, and if so, can we teach artificial intelligence those laws to keep it from harming us? This is the question explored in an original short film recently released by The Guardian.

In the film, the creators of an AI with general intelligence call in a moral philosopher to help them establish a set of moral guidelines for the AI to learn and followwhich proves to be no easy task.

Complex moral dilemmas often dont have a clear-cut answer, and humans havent yet been able to translate ethics into a set of unambiguous rules. Its questionable whether such a set of rules can even exist, as ethical problems often involve weighing factors against one another and seeing the situation from different angles.

So how are we going to teach the rules of ethics to artificial intelligence, and by doing so, avoid having AI ultimately do us great harm or even destroy us? This may seem like a theme from science fiction, yet its become a matter of mainstream debate in recent years.

OpenAI, for example, was funded with a billion dollars in late 2015 to learn how to build safe and beneficial AI. And earlier this year, AI experts convened in Asilomar, California to debate best practices for building beneficial AI.

Concerns have been voiced about AI being racist or sexist, reflecting human bias in a way we didnt intend it tobut it can only learn from the data available, which in many cases is very human.

As much as the engineers in the film insist ethics can be solved and there must be a definitive set of moral laws, the philosopher argues that such a set of laws is impossible, because ethics requires interpretation.

Theres a sense of urgency to the conversation, and with good reasonall the while, the AI is listening and adjusting its algorithm. One of the most difficult to comprehendyet most crucialfeatures of computing and AI is the speed at which its improving, and the sense that progress will continue to accelerate. As one of the engineers in the film puts it, The intelligence explosion will be faster than we can imagine.

Futurists like Ray Kurzweil predict this intelligence explosion will lead to the singularitya moment when computers, advancing their own intelligence in an accelerating cycle of improvements, far surpass all human intelligence. The questions both in the film and among leading AI experts are what that moment will look like for humanity, and what we can do to ensure artificial superintelligence benefits rather than harms us.

The engineers and philosopher in the film are mortified when the AI offers to act just like humans have always acted. The AIs idea to instead learn only from historys religious leaders is met with even more anxiety. If artificial intelligence is going to become smarter than us, we also want it to be morally better than us.Or as the philosopher in the film so concisely puts it: "We can't rely on humanity to provide a model for humanity. That goes without saying."

If were unable to teach ethics to an AI, it will end up teaching itself, and what will happen then? It just may decide we humans cant handle the awesome power weve bestowed on it, and it will take offor take over.

Image Credit:The Guardian/YouTube

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This Is What Happens When We Debate Ethics in Front of Superintelligent AI - Singularity Hub

This article originally appeared on The Motley Fool.

Amazon.com, Inc. has been making quite a push into the field ofartificial intelligence(AI). Its most public example of this effort, Alexa, its voice-activateddigital assistant, controls the Echo smart speaker and Echo Dot, which were top sellers on Amazon's website over the holidays.

Those familiar with Amazon Web Services (AWS), an industry leader in cloud computing, may also be aware of the AI-based tools the company has recently made available to AWS customers: Rekognition for building image recognition apps; Polly for translating text to speech; and Lex, to build conversational bots.

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Jeff Bezos, founder of Amazon. Joshua Roberts/Reuters

Amazon also is adding cyber-security to its AI resume. TechCrunch isreportingthat Amazon has acquired AI-based cyber-security company Harvest.ai. According to itswebsite, Harvest.ai uses AI-based algorithms to identify the most important documents and intellectual property of a business, then combines user behavior analytics with data loss prevention techniques to protect them from cyber attacks. Harvest.ai already had ties to Amazon, as a customer who was featured in an AWS Startup Spotlight article, which focuses on innovative and disruptive young companies. Harvest.ai boasts former members of the National Security Agency (NSA), Federal Bureau of Investigation (FBI), Department of Defense (DoD), as well as former employees of Websense and FireEye, Inc .

Harvest.ai's flagship product, MACIE, monitors a company's network in near real-time to identify when a suspicious user accesses unauthorized documents.Its target market was "Fortune 1000 organizations that were migrating to cloud-based platforms." Amazon has a Who's Who of big name companies as customers, so it seems like a natural fit for the company. If it decides to deploy MACIE to its cloud, it adds to the suite of hosting products available for its customers.Amazon already offers its Amazon Inspector, which it defines as an "automated security assessment service to help improve the security and compliance of applications deployed on AWS."Harvest.ai would take that to the next level.

The use of AI in cybersecurity isn't new. MIT has been experimenting with a novel approach to application. By pairing a system with a human counterpart and applying supervised learning, the system was able to detect 85 percentof threats. Over time, that success rate is sure to improve. Last year,IBMannounced an initiative to train itsAI-based Watsonin security protocols, in what was to be a year-long research project. By the end of the year, the company expanded the beta program with the inclusion of 40 clients across a variety of industries. Earlier this month, IBM announced that Watson for Cyber Security would be available to customers.

The task of cyber security seems ideally suited to AI applications. The ability to digest a magnitude of data in a short time and match real-time situations against a set of specified criteria seems tailor made for the platform. Add to this AI's ability to learn over time and it seems inevitable that there would be a merging of these technologies.

These acquisitions combined with Amazon's own research makes it one of several companies on the cutting edge of AI. Amazon has been applying the knowledge it gains across a wide swath of its business from consumer facing products to its business-centric applications.

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Amazon Applies Its AI Tools to Cyber Security - Newsweek

Googles director of engineering Ray Kurzweil has said the AI singularity will happen in the year 2029, and just a few years later humans will merge with machines.

The AI singularity is the point where machines match human-level intelligence.

Speaking at the SXSW Conference in Austin, Texas, Mr Kurzweil said: "By 2029, computers will have human-level intelligence.

He added that the process has already begun.

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The Google employee said: "That leads to computers having human intelligence, our putting them inside our brains, connecting them to the cloud, expanding who we are.

Today, that's not just a future scenario.

"It's here, in part, and it's going to accelerate.

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Mr Kurzweil continued by stating that predictions that AI will enslave humans is not realistic, adding that it is already ubiquitous.

He said: "We don't have one or two AIs in the world. Today we have billions.

What he envisions is actually a world where AIs purpose is to benefit humanity, rather than exceed it, before predicting that we will one day finally merge with machines which, he believes, will massively improve us as beings.

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Asus

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Asus Zenbo: This adorable little bot can move around and assist you at home, express emotions, and learn and adapt to your preferences with proactive artificial intelligence.

The 69-year old computer scientist said: "What's actually happening is [machines] are powering all of us.

"They're making us smarter.

They may not yet be inside our bodies, but, by the 2030s, we will connect our neocortex, the part of our brain where we do our thinking, to the cloud.

"We're going to get more neocortex, we're going to be funnier, we're going to be better at music. We're going to be sexier.

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"We're really going to exemplify all the things that we value in humans to a greater degree.

"Ultimately, it will affect everything.

"We're going to be able to meet the physical needs of all humans.

We're going to expand our minds and exemplify these artistic qualities that we value."

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AI will be smarter than HUMANS by 2029 before we MERGE with ... - Express.co.uk

The road to human-level artificial intelligence is long and wildly uncertain. Most AI programs today are one-trick ponies. They can recognize faces, the sound of your voice, translate foreign languages, trade stocks and play chess. They may well have got the trick down pat, but one-trick ponies they remain. Google's DeepMind program, AlphaGo, can beat the best human players at Go, but it hasn't a clue how to play tiddlywinks, shove ha'penny, or tell one end of a horse from the other.

Humans, on the other hand, are not specialists. Our forte is versatility. What other animal comes close as the jack of all trades? Put humans in a situation where a problem must be solved and, if they can leave their smartphones alone for a moment, they will draw on experience to work out a solution.

The skill, already evident in preschool children, is the ultimate goal of artificial intelligence. If it can be distilled and encoded in software, then thinking machines will finally deserve the name.

DeepMind's latest AI has cleared one of the important hurdles on the way to human-level AGI -- artificial general intelligence. Most AIs can perform only one trick because to learn a second, they must forget the first. The problem, known as "catastrophic forgetting," occurs because the neural network at the heart of the AI overwrites old lessons with new ones.

DeepMind solved the problem by mirroring how the human brain works. When we learn to ride a bike, we consolidate the skill. We can go off and learn the violin, the capitals of the world and the finer rules of gaga ball, and still cycle home for tea. This program's AI mimics the process by making the important lessons of the past hard to overwrite in the future. Instead of forgetting old tricks, it draws on them to learn new ones.

Because it retains past skills, the new AI can learn one task after another. When it was set to work on the Atari classics -- Space Invaders, Breakout, Defender and the rest -- it learned to play seven out of 10 as well as a human can. But it did not score as well as an AI devoted to each game would have done. Like us, the new AI is more the jack of all trades, the master of none.

There is no doubt that thinking machines, if they ever truly emerge, would be powerful and valuable. Researchers talk of pointing them at the world's greatest problems: poverty, inequality, climate change and disease.

They could also be a danger. Serious AI researchers, and plenty of prominent figures who know less of the art, have raised worries about the moment when computers surpass human intelligence. Looming on the horizon is the Singularity, a time when super-AIs improve at exponential speed, causing such technological disruption that poor, unenhanced humans are left in the dust. These superintelligent computers needn't hate us to destroy us. As the Oxford philosopher Nick Bostrom has pointed out, a superintelligence might dispose of us simply because it is too devoted to making paper clips to look out for human welfare.

In January the Future of Life Institute held a conference on Beneficial AI in Asilomar, California. When it came to discussing threats to humanity, researchers pondered what might be the AI equivalents of nuclear control rods, the sort that are plunged into nuclear reactors to rein in runaway reactions. At the end of the meeting, the organizers released a set of guiding principles for the safe development of AI.

While the latest work on DeepMind edges scientists towards AGI, it does not bring it, or the Singularity, meaningfully closer. There is far more to the general intelligence that humans possess than the ability to learn continually. The DeepMind AI can draw on skills it learned on one game to play another. But it cannot generalize from one learned skill to another. It cannot ponder a new task, reflect on its capabilities, and work out how best to apply them.

The futurist Ray Kurzweil sees the Singularity rolling in 30 years from now. But for other scientists, human-level AI is not inevitable. It is still a matter of if, not when. Emulating human intelligence is a mammoth task. What scientists need are good ideas, and no one can predict when inspiration will strike.

2017 Guardian Web syndicated under contract with NewsEdge/Acquire Media. All rights reserved.

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AI Is Getting Brainier: Will Machines Leave Us in the Dust? - Top Tech News

Im going to let you in on a little secret: Ive broken the terms of use for SaaS software and shared a license before.

Surprised? My guess would be no because youve probably done it too.

In general, per-seat licensing has been a great way for SaaS companies to charge a subscription and collect reliable revenue. Its helped companies like Salesforce, Zoom, and Box grow into large, successful organizations. But theres also no question that this success and revenue reliability comes at a cost, where pricing is not tied directly to how much a customer uses a service.

In short, seat-based subscription models have lots of problems but have been good enough for a long time. However, as more SaaS services leverage AI to augment human work, it will make less and less sense to charge per human seat and more sense to charge for what is actually being used to get work done: the compute power needed to run increasingly intelligent and useful AI-enhanced services.

This shift from human to AI-based productivity is going to fundamentally alter how SaaS companies sell their services. If SaaS companies dont start thinking about this inevitability, and pricing it into their models, AI may cannibalize their revenue over time.

For service models in which AI can provide value, such as in customer service or CRM, the AI itself is going to actively reduce human work over time. What does this mean in practice? In the customer service sphere, for example, bots will work alongside humans, so humans will operate with greater productivity. But SaaS companies that integrate AI while continuing to charge on a per-seat basis will actually be dis-incentivized from making users more efficient. Think about it: companies will lose revenue as they increase AI, because each person (each seat they sell) will be able to do more, and fewer people will be needed to do the same job. So this pushes vendors to drag their heels on innovation.

On top of all of that, it gets pretty darn expensive to do the research for developing good AI and to run the system 24/7. Compute power can easily take a solid chunk of revenue. So, SaaS companies with AI integration will start to sell fewer seats while their system becomes more expensive to develop and run.

Given these trends, the calculus for the vast majority of SaaS companies needs to change both for the customer and for their own long-term viability. Otherwise, in five or 10 years, many of these companies will be in for a rude surprise as AI cannibalizes their revenue.

Expect to see SaaS companies start charging based on usage. That might mean charging for AI work because it costs compute cycles. The more efficiency a customer wants, and the more they rely on the AI, the more they will end up paying for service, but the less they will pay for staff.

Usage-based pricing isnt a novel idea. Amazon has been the obvious pioneer behind pay-as-you-go SaaS pricing. It was no surprise for AWS to introduce a pay-as-you-go model, because the service provided with AWS is not based on human users or account management time. Instead, customers are charged for the type of computing unit being consumed. For example, EC2 charges in cloud compute units. Getting even more granular, Lambda charges by the execution second, while S3 charges by the gigabyte of used disk space.

Usage-based pricing opens the door to a more granular experience in which the customer only pays for what they use. Its the equivalent to buying a ticket to a single football game, versus being forced to buy a season pass, even if you can only make it to that one game. But usage-based models also have other positive byproducts. They take away the ability for customers to cheat by sharing accounts, and they remove the incentive for the SaaS provider to push customers to overbuy licenses in order to plan for growth.

Just like Amazons services, AI-enhanced SaaS companies that charge based on usage will introduce greater elasticity, better user experience, and more efficiency into their systems, leading to less churn and more long-term revenue stability.

Fred Hsu is CEO of Agent.ai.

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AI is going to kill seat-based SaaS models - VentureBeat

Forbes

Why You Don't Need To Worry About AI Forbes Any great sci-fi movie has artificial intelligence (AI), but to be entertaining, a movie needs drama. So in the real world, advances in AI are less about robot overlords and more about Siri, take me home. Below, a few members of Forbes Technology ...

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Why You Don't Need To Worry About AI - Forbes