Search Results for: red hat

Posted on by — Leave a comment

How Red Hat Runs

This past week at Red Hat Summit 2019 (May 7 – 9 2019) has been exhausting. It’s not an overstatement to say that they run analysts ragged at their events, but that’s not why the conference made me tired. It was the sheer energy of the show, the kind of energy that keeps you running with no sleep for three days straight. That energy came from two sources – excitement and fear.

Two announcements, in particular, generated joy amongst the devoted Red Hat fans. The first was the announcement of Red Hat Enterprise Linux version 8, better known as RHEL8. RHEL is the granddaddy of all major Linux distributions for the data center. RHEL8, however, doesn’t seem all that old. As well as all the typical enhancements to the kernel and other parts of the distro, Red Hat has added two killer features to RHEL.

The first, the web console, is a real winner. It provides a secure browser-based system to manage all the features of Linux that one typically needs a command line on the server to perform. Now, using Telnet or SSH to log in to a remote box and do a few adjustments is no big deal when you have a small number of machines, physical or virtual, in a data center. When there are thousands of machines to care for, this is too cumbersome. With web console plus Red Hat Satellite, the same type of system maintenance is much more efficient. It even has a terminal built in if the command line is the only option. I predict that the web console will be an especially useful asset to new sysadmins who have yet to learn the intricacies of the Linux command line (or just don’t want to).

The new image builder is also going to be a big help for DevOps teams. Image builder uses a point and click interface to build images of software stacks, based on RHEL of course, that can be instantiated over and over. Creating consistent environments for developers and testing is a major pain for DevOps teams. The ability to quickly and easily create and deploy images will take away a major impediment to smooth DevOps pipelines.

The second announcement that gained a lot of attention was the impending GA of OpenShift 4 represents a major change in the Red Hat container platform. It incorporates all the container automation goodness that Red Hat acquired from CoreOS, especially the operator framework. Operators are key to automating container clusters, something that is desperately needed for large scale production clusters. While Kubernetes has added a lot of features to help with some automation tasks, such as autoscaling, that’s not nearly enough for managing clusters at hyperscale or across hybrid clouds. Operators are a step in that direction, especially as Red Hat makes it easier to use Operators.

Speaking of OpenShift, Satya Nadella, CEO of Microsoft appeared on the mainstage to help announce Azure Red Hat OpenShift. This would have been considered a mortal sin at pre-Nadella Microsoft and highlights the acceptance of Linux and open source at the Windows farm. Azure Red Hat OpenShift is an implementation of OpenShift as a native Azure service. This matters a lot to those serious about multi-cloud deployments. Software that is not a native service for a cloud service provider do not have the integrations for billing, management, and especially set up that native services do. That makes them second class citizens in the cloud ecosystem. Azure Red Hat OpenShift elevates the platform to first-class status in the Azure environment.

Now for the fear. Although Red Hat went to considerable lengths to address the “blue elephant in the room”, to the point of bringing Ginny Rometty, IBM CEO on stage, the unease around the acquisition by IBM was palpable amongst Red Hat customers. Many that I spoke to were clearly afraid that IBM would ruin Red Hat. Rometty, of course, insisted that was not the case, going so far as to say that she “didn’t spend $34B on Red Hat to destroy them.”

That was cold comfort to Red Hat partners and customers who have seen tech mergers start with the best intentions and end in disaster. Many attendees I spoke drew parallels with the Oracle acquisition of Sun. Sun was, in fact, the Red Hat of its time – innovative, nimble, and with fierce loyalists amongst the technical staff. While products created by Sun still exist today, especially Java and MySQL, the essence of Sun was ruined in the acquisition. That is a giant cloud hanging over the IBM-Red Hat deal. For all the advantages that this deal brings to both companies and the open source community, the potential for a train wreck exists and that is a source of angst in the Red Hat and open source world.

In 2019, Red Hat is looking good and may have a great future. Or it is on the brink of disaster. The path they will take now depends on IBM. If IBM leaves them alone, it may turn out to be an amazing deal and the capstone of Rometty and Jim Whitehurst’s careers. If IBM allows internal bureaucracy and politics to change the current plan for Red Hat, it will be Sun version 2. Otherwise, it is expected that Red Hat will continue to make open source enterprise-friendly and drive open source communities. That would be very nice indeed.

Posted on by — Leave a comment

Red Hat Hybrid Cloud Management Gets Financial with Cloud Cost Management

Key Stakeholders: CIO, CFO, Accounting Directors and Managers, Procurement Directors and Managers, Telecom Expense Personnel, IT Asset Management Personnel, Cloud Service Managers, Enterprise Architects

Why It Matters: As enterprise cloud infrastructure continues to grown 30-40% per year and containerization becomes a top enterprise concern, IT must have tools and a strategy for managing the cost of storage and compute associated with both hybrid cloud and container spend. With Cloud Cost Management, Red Hat provides an option for its considerable customer base.

Key Takeaways: Red Hat OpenShift customers seeking to managing the computing costs associated with hybrid cloud and containers should starting trialing Cloud Cost Management when it becomes available in 2019. Effective cost management strategies and tools should be considered table stakes for all enterprise-grade technologies.

Amalgam Insights is a top analyst firm in the analysis of IT subscription cost management, as can be seen in our:

In this context, Red Hat’s intended development of multi-cloud cost management integrated with CloudForms is an exciting announcement for the cloud market. This product, scheduled to come out in early 2019, will allow enterprises supporting multiple cloud vendors to support workload-specific cost management, which Amalgam Insights considers to be a significant advancement in the cloud cost management market.

And this product comes at a time when cloud infrastructure cost management has seen significant investment including VMware’s $500 million purchase of Boston-based CloudHealth Technologies, the 2017 $50 million “Series A” investment in CloudCheckr, investments in this area by leading Telecom and Technology Expense Management vendors such as Tangoe and Calero, and recent acquisitions and launches in this area from the likes of Apptio, BMC, Microsoft, HPE, and Nutanix.

However, the vast majority of these tools are currently lacking in the granular management of cloud workloads that can be tracked at a service level and then appropriately cross-charged to a project, department, or location. This capability will be increasingly important as application workloads become increasingly nuanced and revenue-driven accounting of IT becomes increasingly important. Amalgam Insights believes that, despite the significant activity in cloud cost management, that this market is just starting to reach a basic level of maturity as enterprises continue to increase their cloud infrastructure spend by 40% per year or more and start using multiple cloud vendors to deal with a variety of storage, computing, machine learning, application, service, integration, and hybrid infrastructure needs.

Red Hat Screenshot of Hybrid Cloud Cost Management

As can be seen from the screenshot, Red Hat’s intended Hybrid Cloud Cost Management offering reflects both modern design and support for both cloud spend and container spend. Given the enterprise demand for third-party and hybrid cloud cost management solutions, it makes sense to have an OpenShift-focused cost management solution.

Amalgam Insights has constantly promoted the importance of formalized technology cost management initiatives and their ability in reducing IT cost categories by 30% or more. We believe that Red Hat’s foray into Hybrid Cloud Cost Management has an opportunity to compete with a crowded field of competitors in managing multi-cloud and hybrid cloud spend. Despite the competitive landscape already in play, Red Hat’s focus on the OpenShift platform as a starting point for cost management will be valuable for understanding cloud spend at container, workload, and microservices levels that are currently poorly understood by IT executives.

My colleague Tom Petrocelli has noted that “I would expect to see more and more development shift to open source until it is the dominant way to develop large scale infrastructure software.” As this shift takes place, the need to manage the financial and operational accounting of these large-scale projects will become a significant IT challenge. Red Hat is demonstrating its awareness of this challenge and has created a solution that should be considered by enterprises that are embracing both Open Source and the cloud as the foundations for their future IT development.

Recommendations

Companies already using OpenShift should look forward to trialling Cloud Cost Management when it comes out in early 2019. This product provides an opportunity to effectively track the storage and compute costs of OpenShift workloads across all relevant infrastructure. As hybrid and multi-cloud management becomes increasingly common, IT organizations will need a centralized capability to track their increasingly complex usage associated with the OpenShift Container Platform.

Cloud Service Management and Technology Expense Management solutions focused on tracking Infrastructure as a Service spend should consider integration with Red Hat’s Cloud Cost Management solution. Rather than rebuild the wheel, these vendors can take advantage of the work already done by RedHat to track container spend.

And for Red Hat, Amalgam Insights provides the suggestion that Cloud Cost Management become more integrated with CloudForms over time. The most effective expense management practices for complex IT spend categories always include a combination of contracts, inventory, invoices, usage, service orders, service commitments, vendor comparisons, and technology category comparisons. To gain this holistic view that optmizes infrastructure expenses, cloud procurement and expense specialists will increasingly demand this complete view across the entire lifecycle of services.

Although this Cloud Cost Management capability has room to grow, Amalgam Insights expects this tool to quickly become a mainstay, either as a standalone tool or as integrated inputs within an enterprise’s technology expense or cloud service management solution. As with all things Red Hat, Amalgam Insights expects rapid initial adoption within the Red Hat community in 2019-2020 which will drive down enterprise infrastructure total cost of ownership and increase visibility for enterprise architects, financial controllers, and accounting managers responsible for responsible IT cost management.

Posted on by — Leave a comment

Observations on the Future of Red Hat from Red Hat Analyst Day

On November 8th, 2018, Amalgam Insights analysts Tom Petrocelli and Hyoun Park attended the Red Hat Analyst Day in Boston, MA. We had the opportunity to visit Red Hat’s Boston office in the rapidly-growing Innovation District, which has become a key tech center for enterprise technology companies. In attending this event, my goal was to learn more about the Red Hat culture that is being acquired as well as to see how Red Hat was taking on the challenges of multi-cloud management.

Throughout Red Hat’s presentations throughout the day, there was a constant theme of effective cross-selling, growing deal sizes including a record 73 deals of over $1 million in the last quarter, over 600 accounts with over $1 million in business in the last year, and increased wallet share year-over-year for top clients with 24 out of 25 of the largest clients increasing spend by an average of 15%. The current health of Red Hat is undeniable, regardless of the foibles of the public market. And the consistency of Red Hat’s focus on Open Source was undeniable across infrastructure, integration, application development, IT automation, IT optimization, and partner solutions, which demonstrated how synchronized and focused the entire Red Hat executive team presenters were, including Continue reading Observations on the Future of Red Hat from Red Hat Analyst Day

Posted on by

Is IBM’s Acquisition of Red Hat the Biggest Acquihire of All Time?

Estimated Reading Time: 11 minutes

Internally, Amalgam Insights has been discussing why IBM chose to acquire Red Hat for $34 billion dollars fairly intensely. Our key questions included:

  • Why would IBM purchase Red Hat when they’re already partners?
  • Why purchase Red Hat when the code is Open Source?
  • Why did IBM offer a whopping $34 billion, $20 billion more than IBM currently has on hand?

As a starting point, we posit that IBM’s biggest challenge is not an inability to understand its business challenges, but a fundamental consulting mindset that starts with the top on down. By this, we mean that IBM is great at identifying and finding solutions on a project-specific basis. For instance, SoftLayer, Weather Company, Bluewolf, and Promontory Financial are all relatively recent acquisitions that made sense and were mostly applauded at the time. But even as IBM makes smart investments, IBM has either forgotten or not learned the modern rules for how to launch, develop, and maintain software businesses. At a time when software is eating everything, this is a fundamental problem that IBM needs to solve.

The real question for IBM is whether IBM can manage itself as a modern software company.

Continue reading Is IBM’s Acquisition of Red Hat the Biggest Acquihire of All Time?

Posted on by — 1 Comment

Tom Petrocelli Provides Context for IBM’s Acquisition of Red Hat

Tom Petrocelli, Amalgam Insights Research Fellow

In light of yesterday’s announcement that IBM is planning to acquire Red Hat for $34 billion, we’d like to share with you some of our recent coverage and mentions of Red Hat to provide context for this gargantuan acquisition.

To learn more about the state of Enterprise Free Open Source Software and the state of DevOps, make sure you continue to follow Tom Petrocelli on this website and his Twitter account.

Posted on by — 1 Comment

Market Milestone: Red Hat Acquires CoreOS Changing the Container Landscape

Red Hat Acquires CoreOS

We have just published a new document from Tom Petrocelli analyzing Red Hat’s $250 million acquisition of CoreOS and why it matters for DevOps and Systems Architecture managers.

This report is recommended for CIOs, System Architects, IT Managers, System Administrators, and Operations Managers who are evaluating CoreOS and Red Hat as container solutions to support their private and hybrid cloud solutions. In this document, Tom provides both the upside and concerns that your organization needs to consider in evaluating CoreOS.

This document includes:
A summary of Red Hat’s Acquisition of CoreOS
Why It Matters
Top Takeaways
Contextualizing CoreOS within Red Hat’s private and hybrid cloud portfolio
Alternatives to Red Hat CoreOS
Positive and negative aspects fcr current Red Hat and CoreOS customers

To download this report, please go to our Research section.

Posted on by

February 25: From BI to AI (Aporia, cnvrg.io, Decodable, Equalum, Grata, Hasura, Mage, nRoad, Redpanda, SeMI Technologies, thatDot)

If you would like your announcement to be included in Amalgam Insights’ weekly data and analytics roundups, please email lynne@amalgaminsights.com.

Funding

Aporia Raises $25 Million to Grow its Machine Learning Observability Platform

On February 22, Aporia, a machine learning observability platform, announced that it had raised $25M in a Series A funding round. Tiger Global Management led the round, with participation from existing investors TLV Partners and Vertex Ventures, and new investors Samsung NEXT and Tal Ventures. The funding will go towards hiring and global expansion.

Decodable Raises $20M Series A Funding Round For Its Realtime Data Platform

Decodable, a realtime data engineering platform, raised a $20M A round this week. Bain Capital Ventures and Venrock led the funding round, with additional participation from individual investors including former US Chief Data Scientist DJ Patil, DataDog CEO Olivier Pomel, Cockroach Labs CEO Spencer Kimball, and Redis CRO and President Jason Forget. Decodable also debuted the Decodable Real-Time Data Platform, which supports functions like event-driven micro services, data mesh deployment, realtime data integration and ML/AI pipelines, and data governance and regulatory compliance.

Grata Closes $25 Million A Round For Its Data Intelligence Engine

Grata, a data intelligence engine, announced February 22 that it had raised $25M in a Series A funding round led by Craft Ventures. Existing investors Accomplice, Bling, and Touchdown Ventures also participated, along with new investors Altai Ventures, Eigen Ventures, and Teamworthy Ventures. The funding will go towards further product development. Grata uses proprietary machine learning and natural language processing models to process unstructured data from websites into insights on private companies, made available in a search-based interface.

GraphQL Engine Provider Hasura Announces $100M in Series C Funding

Hasura, a GraphQL engine provider, has raised a $100M Series C funding round. Greenoaks led the round, with participation from existing investors Lightspeed Venture Partners, Nexus Venture Partners, and Vertex Ventures. Hasura will use the funding for R+D and global expansion of their go-to-market strategy.

Streaming Data Platform Redpanda Raises $50M Series B

Redpanda, a data streaming platform, announced February 23 that they had raised a $50M Series B Funding Round led by GV. Haystack VC also participated, as did Lightspeed Venture Partners (busy week for Lightspeed, also participating in the Hasura C round!). The funding will go towards hiring for their engineering and go-to-market teams.

SeMI Technologies Raises $16M Series A Round For AI-Based Search Database

SeMI Technologies, providers of open source vector search engine Weaviate, announced a $16M Series A funding round February 22. Cortical Ventures and New Enterprise Associates co-led the round. The funding will go towards hiring, community development, and product improvement including increasing potential use cases and creating and improving the ML models Weaviate is based on.

Launches and Updates

cnvrg.io Announces AI Blueprints, Customizable ML Pipelines

On February 22, Cnvrg.io, an AI/ML platform provider, debuted cnvrg.io AI Blueprints. AI Blueprints is a curated open-source library of machine learning model APIs and customizable pipelines, allowing companies to quickly piece together models to analyze their data. Availability of cnvrg.io AI Blueprints is planned for the first half of 2022.

Equalum Releases v3.0 of their Continuous Data Integration Platform 3.0

Equalum released version 3.0 of their “continuous” data integration platform this week. New features include expanded support for cloud targets across AWS, Azure, and GCP; enhanced binary parsers for Oracle logs and SQL replication; improvements to replication groups to allow for extensive data migrations and cross-platform data warehousing; and no-code data integration capabilities for streaming ETL and ELT data, as well as batch ETL and change data capture.

Mage Debuts Low Code AI Ranking Model Tool for Product Developers

On February 24, Mage announced the general availability of its low code AI tool. Mage is targeted towards product developers needing to build AI ranking models to increase user engagement and retention.

nRoad Launches Unstructured Data Processing Platform Convus

nRoad, an NLP startup, introduced its Convus platform February 23. Convus provides machine learning models for financial services to extract insights from unstructured data. This allows FinTech businesses to avoid manual data extraction and entry while incorporating information in documents into business processes.

thatDot Releases Complex Event Processing Engine Quine Streaming Graph

thatDot, complex event processing software providers, debuted Quine Streaming Graph, an open source event processing engine based on streaming graph data. Developers can use Quine to quickly build complex event processing workflows to apply to streaming graph data using “recipes.” Recipes currently available include blockchain realtime tag propagation, CDN cache efficiency analysis, and Apache server log observability.

Posted on by — 4 Comments

5 Predictions That Will Transform Corporate Training in 2018

At Amalgam Insights, we have been focused on the key 2018 trends that will change our ability to manage technology at scale. Tom Petrocelli provided his key Developer Operations and enterprise collaboration predictions for 2018 in mid-December. To continue that trend, Todd Maddox provides 5 key predictions that will shape enterprise learning in 2018 as markets reach new heights, corporate training embraces new scientific principles, and retention replaces compliance as a key training driver.

  1. VR/AR Enterprise Application Budget to Surpass $1 Billion in 2018
  2. eLearning (Computer-Based Training) Market to Approach $180 billion in 2018
  3. Commercial Training Sector to Embrace Neuroscience of Optimized Learning
  4. Continued Exponential Growth of Artificial Intelligence (AI) as a Driving Force Behind the User Interface (UI)
  5. Training for Retention: The Rule, Not the Exception in 2018

Continue reading 5 Predictions That Will Transform Corporate Training in 2018

Posted on by

8 Keys to Managing the Linguistic Copycats that are Large Language Models

Over the past year, Generative AI has taken the world by storm as a variety of large language models (LLMs) appeared to solve a wide variety of challenges based on basic language prompts and questions.

A partial list of market-leading LLMs currently available include:

Amazon Titan
Anthropic Claude
Cohere
Databricks Dolly
Google Bard, based on PaLM2
IBM Watsonx
Meta Llama
OpenAI’s GPT

The biggest question regarding all of these models is simple: how to get the most value out of them. And most users fail because they are unused to the most basic concept of a large language model: they are designed to be linguistic copycats.

As Andrej Karpathy of OpenAI stated earlier this year,

"The hottest new programming language is English."

And we all laughed at the concept for being clever as we started using tools like ChatGPT, but most of us did not take this seriously. If English really is being used as a programming language, what does this mean for the prompts that we use to request content and formatting?

I think we haven’t fully thought out what it means for English to be a programming language either in terms of how to “prompt” or ask the model how to do things correctly or how to think about the assumptions that an LLM has as a massive block of text that is otherwise disconnected from the real world and lacks the sensory input or broad-based access to new data that can allow it to “know” current language trends.

Here are 8 core language-based concepts to keep in mind when using LLMs or considering the use of LLMs to support business processes, automation, and relevant insights.

1) Language and linguistics tools are the relationships that define the quality of output: grammar, semantics, semiotics, taxonomies, and rhetorical flourishes. There is a big difference between asking for “write 200 words on Shakespeare” vs. “elucidate 200 words on the value of Shakespeare as a playwright, as a poet, and as a philosopher based on the perspective on Edmund Malone and the English traditions associated with blank verse and iambic pentameter as a preamble to introducing the Shakespeare Theatre Association.”

I have been a critic of the quality that LLMs provide from an output perspective, most recently in my perspective “Instant Mediocrity: A Business Guide to ChatGPT in the Enterprise.” https://amalgaminsights.com/2023/06/06/instant-mediocrity-a-business-guide-to-chatgpt-in-the-enterprise/. But I readily acknowledge that the outputs one can get from LLMs will improve. Expert context will provide better results than prompts that lack subject matter knowledge

2) Linguistic copycats are limited by the rules of language that are defined within their model. Asking linguistic copycats to provide language formats or usage that are not commonly used online or in formal writing will be a challenge. Poetic structures or textual formats referenced must reside within the knowledge of the texts that the model has seen. However, since Wikipedia is a source for most of these LLMs, a contextual foundation exists to reference many frequently used frameworks.

3) Linguistic copycats are limited by the frequency of vocabulary usage that they are trained on. It is challenging to get an LLM to use expert-level vocabulary or jargon to answer prompts because the LLM will typically settle for the most commonly used language associated with a topic rather than elevated or specific terms.

This propensity to choose the most common language associated with a topic makes it difficult for LLM-based content to sound unique or have specific rhetorical flourishes without significant work from the prompt writer.

4) Take a deep breath and work on this. Linguistic copycats respond to the scope, tone, and role mentioned in a prompt. A recent study found that, across a variety of LLM’s, the prompt that provided the best answer for solving a math problem and providing instructions was not a straightforward request such as “Let’s think step by step,” but “Take a deep breath and work on this problem step-by-step.”

Using a language-based perspective, this makes sense. The explanations of mathematical problems that include some language about relaxing or not stressing would likely be designed to be more thorough and make sure the reader was not being left behind at any step. The language used in a prompt should represent the type of response that the user is seeking.

5) Linguistic copycats only respond to the prompt and the associated prompt engineering, custom instructions, and retrieval data that they can access. It is easy to get carried away with the rapid creation of text that LLM’s provide and mistake this for something resembling consciousness, but the response being created is a combination of grammatical logic and the computational ability to take billions of parameters into account across possibly a million or more different documents. This ability to access relationships across 500 or more gigabytes of information is where LLMs do truly have an advantage over human beings.

6) Linguistic robots can only respond based on their underlying attention mechanisms that define their autocompletion and content creation responses. In other words, linguistic robots make judgment calls on which words are more important to focus on in a sentence or question and use that as the base of the reply.

For instance, in the sentence “The cat, who happens to be blue, sits in my shoe,” linguistic robots will focus on the subject “cat” as the most important part of this sentence. The cat “happens to be,” implies that this isn’t the most important trait. The cat is blue. The cat sits. The cat is in my shoe. The words include an internal rhyme and are fairly nonsensical. And then the next stage of this process is to autocomplete a response based on the context provided in the prompt.

7) Linguistic robots are limited by a token limit for inputs and outputs. Typically, a token is about four characters while the average English content word is about 6.5 characters (https://core.ac.uk/download/pdf/82753461.pdf). So, when an LLM talks about supporting 2048 tokens, that can be seen as about 1260 words, or about four pages of text, for concepts that require a lot of content. In general, think of a page of content as being about 500 tokens and a minute of discussion typically being around 200 tokens when one is trying to judge how much content is either being created or entered into an LLM.

8) Every language is dynamic and evolves over time. LLMs that provide good results today may provide significantly better or worse results tomorrow simply because language usage has changed or because there are significant changes in the sentiment of a word. For instance, the English language word “trump” in 2015 has a variety of political relationships and emotional associations that are now standard to language usage in 2023. Be aware of these changes across languages and time periods in making requests, as seemingly innocuous and commonly used words can quickly gain new meanings that may not be obvious, especially to non-native speakers.

Conclusion

The most important takeaway of the now-famous Karpathy quote is to take it seriously not only in terms of using English as a programming language to access structures and conceptual frameworks, but also to understand that there are many varied nuances built into the usage of the English language. LLM’s often incorporate these nuances even if those nuances haven’t been directly built into models, simply based on the repetition of linguistic, rhetorical, and symbolic language usage associated with specific topics.

From a practical perspective, this means that the more context and expertise provided in asking an LLM for information and expected outputs, the better the answer that will typically be provided. As one writes prompts for LLMs and seek the best possible response, Amalgam Insights recommends providing the following details in any prompt:

Tone, role, and format: This should include a sentence that shows, by example, the type of tone you want. It should explain who you are or who you are writing for. And it should provide a form or structure for the output (essay, poem, set of instructions, etc…). For example, “OK, let’s go slow and figure this out. I’m a data analyst with a lot of experience in SQL, but very little understanding of Python. Walk me through this so that I can explain this to a third grader.”

Topic, output, and length: Most prompts start with the topic or only include the topic. But it is important to also include perspective on the size of the output. Example, “I would like a step by step description of how to extract specific sections from a text file into a separate file. Each instruction should be relatively short and comprehensible to someone without formal coding experience.”

Frameworks and concepts to incorporate: This can include any commonly known process or structure that is documented, such as an Eisenhower Diagram, Porter’s Five Forces, or the Overton Window. As a simpe example, one could ask, “In describing each step, compare each step to the creation of a pizza, wherever possible.”

Combining these three sections together into a prompt should provide a response that is encouraging, relatively easy to understand, and compares the code to creating a pizza.

In adapting business processes based on LLMs to make information more readily available for employees and other stakeholders, be aware of these biases, foibles, and characteristics associated with prompts as your company explores this novel user interface and user experience.

Posted on by

Instant Mediocrity: a Business Guide to ChatGPT in the Enterprise

In June of 2023, we are firmly in the midst of the highest of hype levels for Generative AI as ChatGPT has taken over the combined hype of the Metaverse, cryptocurrency, and cloud computing. We now face a deluge of experts who all claim to be “prompt engineering” experts and can provide guidance on the AI tools that will make your life easier to live. At the same time, we are running into a cohort of technologists who warn that AI is only one step away from achieving full sentience and taking over the world as an apocalyptic force.

In light of this extreme set of hype drivers, the rest of us do face some genuine business concerns associated with generative AI. But our issues are not in worshipping our new robot overloads or in the next generation of “digital transformation” focused on the AI-driven evolution of our businesses that lay off half the staff. Rather, we face more prosaic concerns regarding how to actually use Generative AI in a business environment and take advantage of the productivity gains that are possible with ChatGPT and other AI tools.

Anybody who has used ChatGPT in their areas of expertise has quickly learned that ChatGPT has a lot of holes in its “knowledge” of a subject that prevent it from providing complete answers, timely answers, or productive outputs that can truly replace expert advice. Although Generative AI provides rapid answers with a response rate, cadence, and confidence that mimics human speech, it often is missing either the context or the facts to provide the level of feedback that a colleague would. Rather, what we get is “instant mediocrity,” an answer that matches what a mediocre colleague would provide if given a half-day, full-day, or week to reply. If you’re a writer, you will quickly notice that the essays and poems that ChatGPT writes are often structurally accurate, but lack the insight and skill needed to write a university-level assignment.

And the truth is that instant mediocrity is often a useful level of skill. If one is trying to answer a question that has one of three or four answers, a technology that is mediocre at that skill will probably give you the right answer. If you want to provide a standard answer for structuring a project or setting up a spreadsheet to support a process, a mediocre response is good enough. If you want to remember all of the standard marketing tools used in a business, a mediocre answer is just fine. As long as you don’t need inspired answers, mediocrity can provide a lot of value.

A few things for you to consider as your organization starts using ChatGPT. Just like when the iPhone launched 16 years ago, you don’t really have a choice on whether your company is using ChatGPT or not. All you can do is figure out how to manage and govern the use. Our recommendations typically take one of three major categories: Strategy, Productivity, and Cost. Given the relatively low price of ChatGPT both as a consumer-grade tool and as an API where current pricing is typically a fraction of the cost of doing similar tasks without AI, the focus here will be on strategy and productivity

Strategy – Every software company now has a ChatGPT roadmap. And even mid-sized companies typically have hundreds of apps under management. So, now there will 200, 500, or however many potential ways for employees to use ChatGPT over the next 12 months. Figure out how GPT is being integrated into the software and whether GPT is being directly used to process data or indirectly to help query, index, or augment data.

Strategy – Identify the value of mediocrity. The average large enterprise getting mediocrity from a query-writing or indexing perspective is often a much higher standard than the mediocrity of text autocompletion. Seek mediocrity in tasks where the average online standard is already higher than the average skill within your organization.

Strategy – How will you keep proprietary data out of Gen AI? – Most famously, Samsung recently had a scare when it saw how AI tools were echoing and using proprietary information. How are companies both ensuring that they have not put new proprietary data into generative AI tools for potential public use and that their existing proprietary data was not used to train generative AI models? This governance will require greater visibility from AI providers to provide detail on the data sources that were used to build and train the models we are using today.

Strategy – On a related note, how will you keep commercially used intellectual property from being used by Gen AI? Most intellectual property covered by copyright or patent does not allow for commercial reuse without some form of license. Do models need to figure out some way of licensing data that is used to train commercial models? Or can models verify that they have not used any copyrighted data? Even if users have relinquished copyright for the specific social networks and websites that they initially wrote for this does not automatically give OpenAI and other AI providers the same license to use the same data for training. And can AIs own copyright? Once large content providers such as music publishers, book publishers, and entertainment studios realize the extent to which their intellectual property is at risk with AI and somebody starts making millions with AI-enabled content that strongly resembles any existing IP, massive lawsuits will ensue. If an original provider, be ready to defend IP. If using AI, be wary of actively commercializing or claiming ownership of AI-enabled work for anything other than parody or stock work that can be easily replaced.

Productivity – Is code enterprise-grade: secure, compliant, and free of private corporate metadata? One of the most interesting new use cases for generative AI is the ability to create working code without having prior knowledge of the programming language. Although generative AI currently cannot create entire applications without significant developer engagement, it can quickly provide specifically defined snippets, functions, and calls that may have been a challenge to explicitly search for or to find on a Stack Overflow or in git libraries. As this use case continues to proliferate, coders need to understand their auto-generated code well enough to check for security issues, performance challenges, appropriate metadata and documentation, and reusability based on corporate service and workload management policies. But this will increasingly allow developers to shift from directly coding every line to editing and proofing the quality of code. In doing so, we may see a renaissance of cleaner, more optimized, and more reusable code for internal applications as the standard for code now becomes “instantly mediocre.”

Productivity – Quality, not Quantity. There are hundreds of AI-enabled tools out there to provide chat, search-based outputs, generative text and graphics, and other AI capabilities. Measure twice and cut once in choosing the tools that you use to help you. It’s better to find the five tools that matter than the 150 tools that don’t maximize the mediocre output that you receive.

Productivity – Are employees trained on fact-checking and proofing their Gen AI outputs? Whether employees are creating text, getting sample code, or prompting new graphics and video, the outputs need to be verified against a fact-based source to ensure that the generative AI has not “hallucinated” or autocompleted details that are incorrect. Generative AI seeks to provide the next best word or the next best pixel that is most associated with the prompt that it has been given, but there are no guarantees that this autocompletion will be factual just because it is related to the prompt at hand. Although there is a lot of work being done to make general models more factual, this is an area where enterprises will likely have to build their own, more personalized models over time that are industry, language, and culturally specific. Ideally, ChatGPT and other Generative AI tools are a learning and teaching experience, not just a quick cheat.

Productivity – How will Generative AI help accelerate your process and workflow automation? Currently, automation tends to be a rules-driven set of processes that lead to the execution of a specific action. But generative AI can do a mediocre job of translating intention into a set of directions or a set of tasks that need to be completed. While generative AI may get the order of actions wrong or make other text-based errors that need to be fact-checked by a human, the AI can accelerate the initial discovery and staging of steps needed to complete business processes. Over time, this natural language generation-based approach to process mapping is going to become the standard starting point for process automation. Process automation engineers, workflow engineers, and process miners will all need to learn how prompts can be optimized to quickly define processes.

Cost – What will you need to do to build your own AI models? Although the majority of ChatGPT announcements focus on some level of integration between an existing platform or application and some form of GPT or other generative AI tool, there are exceptions. BloombergGPT provides a model based on all of the financial data that it has collected to help support financial research efforts. Both Stanford University Alpaca and Databricks Dolly have provided tools for building custom large language models. At some point, businesses are going to want to use their own proprietary documents, data, jargon, and processes to build their own custom AI assistants and models. When it comes time for businesses to build their own billion-parameter, billion-word models, will they be ready with the combination of metadata definitions, comprehensive data lake, role definitions, compute and storage resources, and data science operationalization capabilities to support these custom models? And how will companies justify the model creation cost compared to using existing models? Amalgam Insights has some ideas that we’ll share in a future blog. But for now, let’s just say that the real challenge here is not in defining better results, but in making the right data investments now that will empower the organization to move forward and take the steps that thought leaders like Bloomberg have already pursued in digitizing and algorithmically opening up their data with generative AI.

Although we somewhat jokingly call ChatGPT “instant mediocrity,” this phrase should be taken seriously both in acknowledging the cadence and response quality that is created. Mediocrity can actually be a high level of performance by some standards. Getting to a response that an average 1x’er employee can provide immediately is valuable as long as it is seen for what it is rather than unnecessarily glorified or exaggerated. Treat it as an intern or student-level output that requires professional review rather than an independent assistant and it will greatly improve your professional output. Treat it as an expert and you may end up needing legal assistance. (But maybe not from this lawyer. )