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CrowdStrike outage likely to hit cybersecurity's platformization pitch

CrowdStrike outage likely to hit cybersecurity's platformization pitch

Global IT outages on Friday were blamed on a faulty update from CrowdStrike and the impact hit banks, airlines and a host of other companies. But the real hit--beyond CrowdStrike's stock price--was to the platformization play being pitched by cybersecurity vendors.

First, the news. Reddit threads, X posts and various corporate accounts are showing the Blue Screen of Death (BSOD), CrowdStrike support messages and unhappy IT admins. The outage is so bad that CrowdStrike CEO George Kurtz had to play IT admin.

In a post on X, Kurtz said:

"CrowdStrike is actively working with customers impacted by a defect found in a single content update for Windows hosts. Mac and Linux hosts are not impacted. This is not a security incident or cyberattack. The issue has been identified, isolated and a fix has been deployed. We refer customers to the support portal for the latest updates and will continue to provide complete and continuous updates on our website. We further recommend organizations ensure they’re communicating with CrowdStrike representatives through official channels. Our team is fully mobilized to ensure the security and stability of CrowdStrike customers."

By the way, the replies are a gem.

Once the dust settles and IT admin tears dry, the real pain is going to be to the cybersecurity platformization argument. You know the one. Cybersecurity vendors tell CIOs they need to be on one platform to consolidate budget, discounts are handed out and we all go on our secure way with AI-driven solutions.

Palo Alto Networks started this platformization play to battle with CrowdStrike, which is taking budget and a platform.

The problem with consolidated platforms is that they also serve as single points of failure. The fact that CrowdStrike can push a Friday (!!) update and bring down transportation and financial systems is a bit alarming. Monoculture was seen as a threat to security and now you can put platformization in the mix.

Here's the good news: This CrowdStrike fiasco wasn't a cyberattack. Rest assured; cybercriminals are taking notes.

Constellation Research’s take

Constellation Research analyst Chirag Mehta recapped the fallout from the CrowdStrike outage.

Response from CrowdStrike

CrowdStrike’s response to the recent disruption reads more like a message from an IT administrator than a CEO. George neither explicitly took full responsibility nor apologized for the lapse in care. This lack of accountability is likely to result in angry customers and potential lawsuits, as the disruption has caused significant business continuity challenges.

Platformization and Single Point of Failure

The platformization strategy, where an organization controls the platform and confines others to operate within it, has its risks. It can create a single point of failure (SPOF). Palo Alto’s recent push towards platformization may attract more scrutiny, as customers are wary of being locked into a single platform that could become a SPOF.

EDR/MDR Vendors are Now Spooked

Other EDR and MDR vendors are fortunate that they were not affected this time. They now can evaluate the depth of their integration with operating systems, the methods of air-gapping their updates, and their deployment processes. Overconfidence can be dangerous.

Microsoft Can’t Catch a Break

Although this incident is not technically Microsoft's fault from a Windows or Defender perspective, the company is now entangled in it. Microsoft has been feverishly working to improve its security reputation, but this situation highlights inherent flaws in Windows' architecture and the level of kernel access required by third-party products like CrowdStrike. It serves as a reminder of the large attack surface of Windows, especially older, unpatched versions. It may be time for OS vendors to rethink their core architectures.

Handling Single Points of Failure

Firewall vendors are well aware of the critical importance of their software. A faulty update or poorly managed DDoS attack can shut down entire networks. Cloud providers excel at managing updates and securing environments against DDoS attacks. They often emphasize the security of their distributed cloud infrastructure, which is harder to bring down. Cloud providers, responsible for their own environments, have developed sophisticated practices for testing updates, deploying quickly, isolating incidents, and rolling back changes. I would expect them to talk up their security, especially Google who has growth ambitions in cybersecurity. This will also make it clear why Google is after Wiz who has an agentless approach for cloud security.

Agentless Approach Gets Spotlight

This incident highlights the potential of an agentless approach in various cybersecurity domains, where modifications to the underlying operating system are avoided. This is particularly relevant in OT security within sectors like healthcare, transportation, and aviation, where proprietary devices should not be altered. For instance, the FDA prohibits third-party agents on most healthcare devices to ensure their integrity and functionality.

CrowdStrike Again?

CrowdStrike seems to gain attention for all the wrong reasons. The last time the average person heard about CrowdStrike was during the 2016 presidential campaign when Trump mentioned it in the context of Hillary Clinton’s emails. Despite building a substantial clientele among Fortune 500 companies, CrowdStrike learned today that this prominence can be a double-edged sword.

 

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Infosys, Persistent see trickle down demand as financial services ramp genAI projects

Infosys, Persistent see trickle down demand as financial services ramp genAI projects

Infosys is seeing stronger spending from financial services firms as that industry begins to spend more on scaling generative AI. That demand recovery also aligns with what financial services executives have been saying on earnings calls.

Financial services firms have been among the most aggressive with generative AI implementations because their data strategies are solid due to being regulated. Simply put, financial services firms have a lighter lift when it comes to making the jump to AI-driven projects.

The financial services spending recovery cited by Infosys was also evident in results from Persistent. If this early recovery sticks it'll highlight how generative AI spending is moving beyond infrastructure. 

Infosys reported fiscal first quarter revenue of $4.71 billion with net income of $764 million. Persistent reported first quarter revenue of $328.2 million, up 16% from a year ago.

Speaking on a conference call, Infosys CEO Salil Parekh said the company saw 7.9% sequential revenue growth in financial services. Compared to a year ago, financial services revenue was flat, but Infosys is seeing green shoots in the US. Persistent saw financial services growth of 7.6%.

Parekh said:

"Along with our overall robust performance in Q1 and strong opportunity pipeline, we are seeing early signs of improvement in financial services vertical in the US. While discretionary spends continues to be under pressure, a highly differentiated offering around driving efficiencies at scale and transformation capabilities around generative AI have positioned us well in the market."

Parekh said financial services firms are doing the following:

  • Discretionary spending is still under pressure.
  • There are a lot of ongoing discussions about generative AI.
  • Firms are focusing on efficiency, consolidation and automation.
  • Mortgage companies, capital markets and credit card processing are seeing more volume and early signs of recovery.
  • Infosys is working on actual genAI projects as they've graduated beyond proofs of concept.

Constellation Research analyst Chirag Mehta said:

"Engineering, Research, and Development (ER&D) services are gaining significant traction, fueled by notable acquisitions in the industry. Infosys' acquisition of a service provider in-tech, focused on German automotive sector, and Cognizant's acquisition of a service provider Belcan, focused on U.S. aerospace and defense service provider, have invigorated the ER&D sector, which is poised to benefit from AI innovations. The manufacturing industry, particularly heavy industries, stands to gain immensely from these AI-driven advancements.

Additionally, Persistent's intent to acquire Starfish Associates, an AI-powered contact center, and its strategic partnership with Google Cloud to develop industry-specific GenAI solutions, highlight a robust commitment to AI adoption. This aggressive move is aimed at capturing a significant share of the growing customer spending on AI solutions."

The general tone among the financial services, banks and insurance companies has been upbeat about genAI. At a recent AWS event in New York, a bevy of customers outlined various use cases.

JPMorgan Chase at its investor day outlined how AI has moved beyond a central group to being part of every business unit.

"It's not understanding AI. It's understanding how it works," said Jamie Dimon, CEO of JPMorgan Chase. By the end of the year, Dimon estimated that JPMorgan Chase will have about 800 AI use cases across the company as management teams become better at deploying AI. He added:

"We use it for prospect, marketing, offers, travel, notetaking, idea generation, hedging, equity hedging, and the equity trading floors, anticipating when people call in what they're calling it for, answering customer, just on the wholesale side, but answering customer requests. And then we have – and we're going to be building agents that not just answers the question, it takes action sometimes. And this is just going to blow people's mind. It will affect every job, every application, every database and it will make people more efficient."

Daniel Pinto, Chief Operating Officer and President at JPMorgan Chase, said the company has moved to transform its data so it's usable for AI and analytics. There will also be a central platform to leverage that data across business units. "AI and particularly large language models, will be transformational here," said Pinto.

Goldman Sachs' CEO David Solomon has talked up the company's AI initiatives before. He said the company has leveraged AI for multiple use cases ranging from coding to equity research and content during the company's second quarter earnings call.

"We are focused on how you can create use cases that increase your productivity," said Solomon. "If you look and you think across the scale of our business, I think you can think of lots of places where the capacity to use these tools to take work that's always been more manual and allow the very smart people to do that work to focus their attention on clients."

Solomon said the AI spending boom is real and can drive productivity and revenue gains. Solomon said:

"I am particularly encouraged by the ongoing advancements in artificial intelligence. Recently, our Board of Directors spent a week in Silicon Valley where we spoke with the CEOs of many of the leading institutions at the cutting-edge of technology and AI. We all left with a sense of optimism about the application of AI tools and the accelerating innovation in technology more broadly. The proliferation of AI in the corporate world will bring with it significant demand-related infrastructure and financing needs, which should fuel activity across our broad franchise."

 

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TSMC's Q2: Six takeaways on genAI, AI demand

TSMC's Q2: Six takeaways on genAI, AI demand

TSMC’s second quarter earnings were better-than-expected and the chip manufacturer is becoming a barometer for generative AI infrastructure demand. High-performance computing (HPC) is now more than half of TSMC's revenue.

In addition, TSMC’s ability to be bleeding edge has made it the go-to partner for every generative AI infrastructure player including Nvidia and AMD, two companies moving toward an annual GPU cadence. 

TSMC's second quarter revenue was $20.92 billion, up 32.8% from a year ago, with earnings of $1.48 per American Depository Receipt unit. TSMC is projecting third quarter revenue to be between $22.4 billion and $23.2 billion.

Here are the takeaways from TSMC's second quarter earnings call.

Demand for AI processors is still well ahead of supply. TSMC CEO C.C. Wei said the company is working hard to meet customer demand, but supplies of AI chips will remain tight. "We are working very hard to get enough capacity to support customers from now to next year and 2026," said Wei. "The supply continues to be very tight, all the way through probably 2025 and I hope it can be eased in 2026. That's today's situation."

TSMC is disciplined about adding capacity. "Our capital investment decisions are based on four disciplines, that is, technology leadership, flexible and responsive manufacturing, retaining customers' trust, and earning a sustainable and healthy return. To ensure a proper return from our investment, both pricing and cost are important. TSMC's pricing strategy is strategic, not opportunistic to reflect the value that we provide," said Wei.

Wei said TSMC won't repeat the same mistakes made in 2021 and 2022 where demand was driven by pandemic and shortages and not sustainable.

AI demand is more durable than previous boom-bust cycles. "AI demand is more real than two or three years ago," said Wei. "AI will be a very useful tool for human to improve productivity. Everything will need AI."

Wei noted that TSMC is also in line to buy its customers' products because it is leveraging AI to be more productive. Nevertheless, TSMC wants to make sure customers are realistic about future demand for AI chips. "We have a top-down bottom-up approach and discuss with our customers and ask them to be more realistic. I don't want to repeat the same kind of mistake two or three years ago, and that's what we are doing right now," said Wei.

AI will drive PC and smartphone processor demand. Wei noted that customers are all looking to put AI functionality into edge devices, but don't have a handle on demand and replacement cycles yet. He said that AI is likely to drive edge processor demand in about two years.

TSMC will raise prices, but each customer and category are different. Wei noted that doubling capacity isn't cheap--$30 billion to $32 billion in capex for 2024--and TSMC hasn't been able to meaningfully boost the gross margins. TSMC executives repeatedly said the company will "sell the value" so that customer wins also filter down to it.

Wei said: "This is an ongoing and continuous process, and we are continuing to sell our value. And by the way, my customers are doing very well also. You knew that. So, we should do well also."

"When TSMC wants to expand the capacity, we need the land, we need the electricity and we need the talented people," said Wei. "All of my customers are looking for leading-edge capacity for the next few years and we are working with them to support them, both in pricing and in capacity."

The company is ready for an annual GPU product cadence. Wei said typically every processor takes 1.5 years to 2 years to move from design to production. Nvidia gave TSMC the heads up a while ago. "We have been prepared. And not just from June when they announced it, but much earlier," said Wei.

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CR CX Convos: Is Looking Beyond Body Language Critical for Commerce?

CR CX Convos: Is Looking Beyond Body Language Critical for Commerce?

Check out the latest CR Convo with Liz Miller.

On CR Conversations <iframe width="560" height="315" src="https://www.youtube.com/embed/p3lkleO2SjM?si=NgciFgcU_UGR1APF" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>

OpenAI, Mistral AI aim for models that can show their work, tackle mathematical problems

OpenAI, Mistral AI aim for models that can show their work, tackle mathematical problems

The large language model (LLM) industry is racing to rid themselves of glaring weaknesses that hamper broader use cases.

OpenAI released a paper designed to improve legibility and verification so humans can better evaluate them. In a nutshell, OpenAI researchers pitted models against each other via a "Prover-Verifier" game that enhanced the ability for LLMs to show their work.

In a blog post, OpenAI said:

"We found that when we optimize the problem-solving process of strong models solely for getting the correct answer, the resulting solutions can become harder to understand. In fact, when we asked human evaluators with limited time to assess these highly optimized solutions, they made nearly twice as many errors compared to when they evaluated less optimized solutions. This finding highlights the importance of not just correctness, but also clarity and ease of verification in AI-generated text."

OpenAI then deployed the prover-verifier games that involve one model that generates a solution and another verifier that checks for accuracy. The goal was to ensure answers were right and make them easier to verify by humans and other models.

According to OpenAI, the ability to verify answers will improve trust and pave the way for autonomous AI systems. The methodology from OpenAI is just a first step and models still require grounding.

Mistral AI also released Mathstral, a model that's designed to "bolster efforts in advanced mathematical problems requiring complex, multi-step logical reasoning."

In a blog post, Mistral AI said Mathstral is built on Mistral 7B but specializes in STEM subjects. The company added that Mathstral highlights the benefits of building models for specific purposes.

Mathstral, which is released under an Apache 2.0 license, outscored most models on math reasoning.

With developments moving quickly it's clear that LLM creators are quickly looking to enable more complex problem solving that'll benefit enterprises.

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F1 Technology, Analyst Hazing, Digital Body Language | ConstellationTV Episode 84

F1 Technology, Analyst Hazing, Digital Body Language | ConstellationTV Episode 84

This week on ConstellationTV episode 84, hear co-hosts Liz Miller and Holger Mueller analyze the latest enterprise #technology news and events (Formula1 #technology, #aws Summit, Cancelled #hubspot acquisition).

Then watch an entertaining Salon 50 interview to learn more about Constellation's newest analyst, Martin Scheider and hear from Liz Miller why digital body language is important for #commerce.

0:00 - Introduction: Meet the Hosts
01:32 - Enterprise #technology news coverage
18:04 - Salon50 with Martin Schneider
29:47 - Is Looking Beyond Body Language Critical for Commerce
42:44 - Bloopers!

ConstellationTV is a bi-weekly Web series hosted by Constellation analysts, tune in live at 9:00 a.m. PT/ 12:00 p.m. ET every other Wednesday!

On ConstellationTV <iframe width="560" height="315" src="https://www.youtube.com/embed/r4gysr8jjJU?si=gy5jwBV31E6Lnbv6" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>

Top 5 Takeaways from Qualcomm AI Day | With Analyst Andy Thurai

Top 5 Takeaways from Qualcomm AI Day | With Analyst Andy Thurai

Constellation VP & analyst Andy Thurai gives his top 5 takeaways after recently attending Qualcomm AI Day 2024. Watch the full recap to learn more about #qualcomm #innovation.

📌 Takeaway 1 - Smaller models and more efficient architecture will rule. 📌 Takeaway 2 - Edge is faster & scalable.
📌 Takeaway 3 - On-Device is private & faster.
📌 Takeaway 4 - Qualcomm is more than chips with their #AI hub.
📌 Takeaway 5 - Qualcomm AI research

On <iframe width="560" height="315" src="https://www.youtube.com/embed/HpDMm_Yi2Lk?si=KMTIDSToyG2u8wGr" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>

SugarCRM Adds Gen AI Tools with Latest Release

SugarCRM Adds Gen AI Tools with Latest Release

SugarCRM has announced two new generative AI features with its latest release, focused on driving user productivity and making text data generated by customer interactions more useful to sellers and customer service agents. These new Gen AI capabilities build upon existing predictive AI tools previously released.

The new features focus on summarization of interaction data and text from sales opportunities and support cases:

  • Opportunity Summarization provides a snapshot of strengths, weaknesses, and vulnerabilities to support improved sales outcomes. It includes expected outcomes, key contacts, and perceived risks. It also suggests next-best actions and helps create sales copy.
  • Case Summarization identifies potential blockers, issues, or risks. It suggests next-best actions and helps agents understand the context for more personalized service.

For both features, language translation is available. For global businesses this is not only a solid addition for managing multi-language business units but can also increase the pace of issue resolution for single location support teams serving a global customer base.

These new capabilities are powered by a native integration with OpenAI. As with most other Gen AI tools embedded into CRM systems, SugarCRM has done the work to ensure all outputs are grounded to reduce toxicity and hallucinations as well as masking sensitive information.  

SugarCRM has also published pricing for the new feature set. Adding the Gen AI capabilities is a $20 per user/per month surcharge on top of its core Sugar Sell and Sugar Serve sales and support product offerings. The base pricing includes 500,000 tokens, which given the use cases and SugarCRM’s midmarket customer focus, most customers would probably remain under the usage limits of the base pricing. (Some info on how OpenAI meters tokens here.) SugarCRM also notes that they cache all the results from all summaries in the system, so any subsequent views of the summary in Sugar Sell or Serve will not affect token usage, unless a change is made to that summary.

For users of Sugar, this is an easy path to add some useful tools without any heavy lifting. Embedded in the CRM, this allows for more contextual, grounded and “safe” usage of Ai tools in the workflow of deal and case management. However, from a cost perspective, it is a significant additional fee, as other CRM providers in the SMB and midmarket space seem to be adding a lot of these features with no explicit additional fees (at least at this time). If users of Sugar do not see the need to augment their Gen AI outside of the offered use cases, this is not a huge cost addition, but if firms are also adding proprietary ChatGPT licenses for marketing teams, other AI tools for RevOps, etc. – costs can creep. It is best to take a wider view and analyze the cost/benefits and wider use cases when selecting AI tools as either add-on features or as point SaaS products on their own.

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SpreadsheetLLM may yet decipher, democratize spreadsheets for rest of us

SpreadsheetLLM may yet decipher, democratize spreadsheets for rest of us

Microsoft researchers have cooked up SpreadsheetLLM, a method that encodes and optimizes large language models (LLMs) so they can understand spreadsheets.

Now there are only a few companies that would care about this use case--Microsoft and Google--but the impact of Spreadsheet LLM could be huge. Why? Despite analysts and every enterprise software company telling you that you shouldn't run your business on a bunch of spreadsheets flung around the office, you all still do it. You know who you are.

The interesting thing about SpreadsheetLLM is that it's a great lifehack to the reality that you'll have your damn Excel spreadsheets anyway and the rest of us need to understand what's going on.

Enter SpreadsheetLLM. In a paper outlining the project, researchers said they also created SheetCompressor, which is an encoding framework that compresses spreadsheets for LLMs. Researchers said:

"Spreadsheets pose unique challenges for LLMs due to their expansive grids that usually exceed the token limitations of popular LLMs, as well as their inherent two-dimensional layouts and structures, which are poorly suited to linear and sequential input. Furthermore, LLMs often struggle with spreadsheet-specific features such as cell addresses and formats, complicating their ability to effectively parse and utilize spreadsheet data."

Ultimately, SpreadsheetLLM can make understanding and analyzing spreadsheet data more accessible. The paper is a bit wonky, but SpreadsheetLLM appears to be an advance in processing and understanding spreadsheet data via various LLMs. SpreadsheetLLM could also be handy for data cleansing and all those functions we always Google for how-to videos (yes folks, Excel tips are a cottage industry).

Holger Mueller, Constellation Research analyst, said:

"Right after transactional databases, spreadsheets run the world. And the platform is Excel, so it's key for Microsoft to be at the forefront of using AI for better use of spreadsheets. Microsoft has done exactly that with SpreadsheetLLM, which could fundamentally change its UX and user reach. Verbal access to spreadsheets has massive value both for creating and analyzing spreadsheets. If Microsoft nails this, it not only secured the future of Excel but also change the future of work."

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EY: CxOs ramping genAI budgets, skimp on data foundation

EY: CxOs ramping genAI budgets, skimp on data foundation

CxOs are racing to invest in generative AI but skimping on the data foundation needed to execute, according to an Ernst & Young (EY) survey.

EY's AI Pulse Survey, based on 500 US senior leader respondents, highlights the generative AI enterprise conundrum. Here's a look at some of the moving parts.

Among companies already investing in AI, the number of companies investing $10 million will roughly double from 16% to 30% in the next year.

  • But 36% of senior leaders say they're investing in data infrastructure at scale.
  • And 34% said they are building on an AI governance framework.
  • 37% of senior leaders said their organizations are training and upskilling employees on AI at scale.

These moving parts highlight how some enterprises may be headed to a crap in, crap out wall if they jump into AI without a strong data game.

 

GenAI’s prioritization phase: Enterprises wading through thousands of use cases

Nevertheless, EY found that CxOs are doubling down on generative AI. Three-quarters of executives said their firms are seeing positive returns across business functions. Indeed, 77% said they were seeing operational efficiencies, 74% saw employee productivity gains and 72% saw improved customer satisfaction.

Other items from the survey:

  • Companies investing 5% or more of total budgets on AI are seeing more returns.
  • 50% of senior leaders say they will dedicate 25% or more of total budgets toward AI investments.
  • 54% of CxOs investing in AI said their organizations are investing in ethical AI over the next year.

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