Nvidia Earnings: Stock Rallies As AI Giant Reports 600% Profit Explosion, 10-For-1 Stock Split — from forbes.com by Derek Saul

  • Nvidia reported $6.12 earnings per share and $26 billion of sales for the three-month period ending April 30, shattering mean analyst forecasts of $5.60 and $24.59 billion, according to FactSet.
  • Nvidia’s profits and revenues skyrocketed by 628% and 268% compared to 2023’s comparable period, respectively.
  • This was Nvidia’s most profitable and highest sales quarter ever, topping the quarter ending this January’s record $12.3 billion net income and $22.1 billion revenue.
  • Driving the numerous superlatives for Nvidia’s financial growth over the last year is unsurprisingly its AI-intensive datacenter division, which raked in $22.6 billion of revenue last quarter, a 427% year-over-year increase and a whopping 20 times higher than the $1.1 billion the segment brought in in 2020.

Per ChatPGT today:

NVIDIA is a prominent technology company known for its contributions to various fields, primarily focusing on graphics processing units (GPUs) and artificial intelligence (AI). Here’s an overview of NVIDIA’s main areas of activity:

1. **Graphics Processing Units (GPUs):**
– **Consumer GPUs:** NVIDIA is famous for its GeForce series of GPUs, which are widely used in gaming and personal computing for their high performance and visual capabilities.
– **Professional GPUs:** NVIDIA’s Quadro series is designed for professional applications like 3D modeling, CAD (Computer-Aided Design), and video editing.

2. **Artificial Intelligence (AI) and Machine Learning:**
– NVIDIA GPUs are extensively used in AI research and development. They provide the computational power needed for training deep learning models.
– The company offers specialized hardware for AI, such as the NVIDIA Tesla and A100 GPUs, which are used in data centers and supercomputing environments.

3. **Data Centers:**
– NVIDIA develops high-performance computing solutions for data centers, including GPU-accelerated servers and AI platforms. These products are essential for tasks like big data analytics, scientific simulations, and AI workloads.

4. **Autonomous Vehicles:**
– Through its DRIVE platform, NVIDIA provides hardware and software solutions for developing autonomous vehicles. This includes AI-based systems for perception, navigation, and decision-making.

5. **Edge Computing:**
– NVIDIA’s Jetson platform caters to edge computing, enabling AI-powered devices and applications to process data locally rather than relying on centralized data centers.

6. **Gaming and Entertainment:**
– Beyond GPUs, NVIDIA offers technologies like G-SYNC (for smoother gaming experiences) and NVIDIA GameWorks (a suite of tools for game developers).

7. **Healthcare:**
– NVIDIA’s Clara platform utilizes AI and GPU computing to advance medical imaging, genomics, and other healthcare applications.

8. **Omniverse:**
– NVIDIA Omniverse is a real-time graphics collaboration platform for 3D production pipelines. It’s designed for industries like animation, simulation, and visualization.

9. **Crypto Mining:**
– NVIDIA GPUs are also popular in the cryptocurrency mining community, although the company has developed specific products like the NVIDIA CMP (Cryptocurrency Mining Processor) to cater to this market without impacting the availability of GPUs for gamers and other users.

Overall, NVIDIA’s influence spans a broad range of industries, driven by its innovations in GPU technology and AI advancements.

 

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Grasp is the world’s first generative AI platform for finance professionals.

We build domain-specific AI systems that address the complex needs of investment bankers and management consultants.

By automating finance workflows, Grasp dramatically increases employee productivity and satisfaction.

 

Learning to Work, Or Working to Learn? — from insidehighered.com by Erin Crisp; via Melanie Booth, Ed.D. on LinkedIn
We need a systems approach to making work-to-learn models just as accessible as traditional learn-to-work pathways, Erin Crisp writes.

Over the past two years, I have had the unique experience of scaling support for a statewide registered teacher-apprenticeship program while also parenting three college-aged sons. The declining appeal of postsecondary education, especially among young men, is evident at my dinner table, in my office, and in my dreams (literally).

Scaling a statewide apprenticeship program for the preparation of teachers has meant that I am consistently hearing from four stakeholder groups—K-12 school district leaders, college and university leaders, aspiring young educators, and local workforce development leaders.

A theme has emerged from my professional life, one that echoes the dinner table conversations happening in my personal life: Society needs systematic work-to-learn pathways in addition to the current learn-to-work ecosystem. This is not an either/or. What we need is a systematic expansion of effort.

In a work-to-learn model, the traditional college sequence is flipped. Instead of starting with general education coursework or survey courses, the working learner is actively engaged in practicing the skills they are interested in acquiring. A workplace supervisor often helps him make connections between the coursework and the job. The learner’s attention is piqued. The learning is relevant. The learner gains confidence, and seeing their influence in the workplace (and paycheck) is satisfying. All of the ARCS model elements are easily achieved.

 


Microsoft’s new ChatGPT competitor… — from The Rundown AI

The Rundown: Microsoft is reportedly developing a massive 500B parameter in-house LLM called MAI-1, aiming to compete with top AI models from OpenAI, Anthropic, and Google.


2024 | The AI Founder Report | Business Impact, Use cases, & Tools — from Hampton; via The Neuron

Hampton runs a private community for high-growth tech founders and CEOs. We asked our community of founders and owners how AI has impacted their business and what tools they use

Here’s a sneak peek of what’s inside:

  • The budgets they set aside for AI research and development
  • The most common (and obscure) tools founders are using
  • Measurable business impacts founders have seen through using AI
  • Where they are purposefully not using AI and much more

2024 Work Trend Index Annual Report from Microsoft and LinkedIn
AI at Work Is Here. Now Comes the Hard Part Employees want AI, leaders are looking for a path forward.

Also relevant, see Microsoft’s web page on this effort:

To help leaders and organizations overcome AI inertia, Microsoft and LinkedIn looked at how AI will reshape work and the labor market broadly, surveying 31,000 people across 31 countries, identifying labor and hiring trends from LinkedIn, and analyzing trillions of Microsoft 365 productivity signals as well as research with Fortune 500 customers. The data points to insights every leader and professional needs to know—and actions they can take—when it comes to AI’s implications for work.

 

The Verge | What’s Next With AI | February 2024 | Consumer Survey

 

 

 

 

 

 




Microsoft AI creates talking deepfakes from single photo — from inavateonthenet.net


The Great Hall – where now with AI? It is not ‘Human Connection V Innovative Technology’ but ‘Human Connection + Innovative Technology’ — from donaldclarkplanb.blogspot.com by Donald Clark

The theme of the day was Human Connection V Innovative Technology. I see this a lot at conferences, setting up the human connection (social) against the machine (AI). I think this is ALL wrong. It is, and has always been a dialectic, human connection (social) PLUS the machine. Everyone had a smartphone, most use it for work, comms and social media. The binary between human and tech has long disappeared. 


Techno-Social Engineering: Why the Future May Not Be Human, TikTok’s Powerful ForYou Algorithm, & More — from by Misha Da Vinci

Things to consider as you dive into this edition:

  • As we increasingly depend on technology, how is it changing us?
  • In the interaction between humans and technology, who is adapting to whom?
  • Is the technology being built for humans, or are we being changed to fit into tech systems?
  • As time passes, will we become more like robots or the AI models we use?
  • Over the next 30 years, as we increasingly interact with technology, who or what will we become?

 

Forbes 2024 AI 50 List: Top Artificial Intelligence Startups  — from forbes.com by Kenrick Cai

The artificial intelligence sector has never been more competitive. Forbes received some 1,900 submissions this year, more than double last year’s count. Applicants do not pay a fee to be considered and are judged for their business promise and technical usage of AI through a quantitative algorithm and qualitative judging panels. Companies are encouraged to share data on diversity, and our list aims to promote a more equitable startup ecosystem. But disparities remain sharp in the industry. Only 12 companies have women cofounders, five of whom serve as CEO, the same count as last year. For more, see our full package of coverage, including a detailed explanation of the list methodology, videos and analyses on trends in AI.


Adobe Previews Breakthrough AI Innovations to Advance Professional Video Workflows Within Adobe Premiere Pro — from news.adobe.com

  • New Generative AI video tools coming to Premiere Pro this year will streamline workflows and unlock new creative possibilities, from extending a shot to adding or removing objects in a scene
  • Adobe is developing a video model for Firefly, which will power video and audio editing workflows in Premiere Pro and enable anyone to create and ideate
    Adobe previews early explorations of bringing third-party generative AI models from OpenAI, Pika Labs and Runway directly into Premiere Pro, making it easy for customers to draw on the strengths of different models within the powerful workflows they use every day
  • AI-powered audio workflows in Premiere Pro are now generally available, making audio editing faster, easier and more intuitive

Also relevant see:




 

Corporate Learning Is Boring — But It Doesn’t Have to Be — from hbr.org by Duncan Wardle; via GSV

Summary:
Most corporate learnings aren’t cutting it. Almost 60% of employees say they’re interested in upskilling and training, but 57% of workers also say they’re already pursuing training outside of work. The author, the former Head of Innovation and Creativity at Disney, argues that creativity is the missing piece to make upskilling engaging and effective. From his experience, he shares four strategies to unlock creativity in trainings: 1) Encourage “What if?”, 2) respond “How else?” to challenges, 3) give people time to think by encouraging playfulness, and 4) make training a game.

 

[Report] The Top 100 AI for Work – April 2024 — from flexos.work; with thanks to Daan van Rossum for this resource
AI is helping us work up to 41% more effectively, according to recent Bain research. We review the platforms to consider for ourselves and our teams.

Following our AI Top 150, we spent the past few weeks analyzing data on the top AI platforms for work. This report shares key insights, including the AI tools you should consider adopting to work smarter, not harder.

While there is understandable concern about AI in the work context, the platforms in this list paint a different picture. It shows a future of work where people can do what humans are best suited for while offloading repetitive, digital tasks to AI.

This will fuel the notion that it’s not AI that takes your job but a supercharged human with an army of AI tools and agents. This should be a call to action for every working person and business leader reading this.

 

Say Goodbye to Antiquated Performance Reviews — from td.org by Magdalena Nowicka Mook

Excerpt (emphasis DSC):

Most leaders understand the value of investing in an onboarding process for orientation, productivity, and retention, but few associate onboarding with strong performance over the employee’s full tenure with the organization. By contrast, everboarding is a newer approach that prioritizes ongoing learning and development rather than only an initial commitment. Insights from Deloitte indicate organizations that establish an ongoing learning culture are 52 percent more productive with engagement and achieve retention rates 30–50 percent higher than those that don’t.

When implemented effectively, everboarding embraces proven elements of a coaching culture that establish an ongoing commitment to skill development, deepens understanding of the organization, and supports real-time feedback to prevent stagnancy in high-potential employees brought in through strong hiring practices.

 

What Are AI Agents—And Who Profits From Them? — from every.to by Evan Armstrong
The newest wave of AI research is changing everything

I’ve spent months talking with founders, investors, and scientists, trying to understand what this technology is and who the players are. Today, I’m going to share my findings. I’ll cover:

  • What an AI agent is
  • The major players
  • The technical bets
  • The future

Agentic workflows are loops—they can run many times in a row without needing a human involved for each step in the task. A language model will make a plan based on your prompt, utilize tools like a web browser to execute on that plan, ask itself if that answer is right, and close the loop by getting back to you with that answer.

But agentic workflows are an architecture, not a product. It gets even more complicated when you incorporate agents into products that customers will buy.

Early reports of GPT-5 are that it is “materially better” and is being explicitly prepared for the use case of AI agents.

 

Conditions that trigger behaviour change — from peoplealchemy.com by Paul Matthews; via Learning Now TV

“Knowing is not enough; we must apply. Willing is not enough; we must do.”

Johann Wolfgang von Goethe

Learning Transfer’s ultimate outcome is behaviour change, so we must understand the conditions that trigger a behaviour to start.

According to Fogg, three specific elements must converge at the same moment for a specific behaviour to occur. Given that learning transfer is only successful when the learner starts behaving in the desired new ways, Fogg’s work is critical to understanding how to generate these new behaviours. The Fogg Behavioural Model [*1] states that B=MAP. That is, a specific behaviour will occur if at the same moment there is sufficient motivation, sufficient ability and sufficient prompt. If the behaviour does not occur, at least one of these three elements is missing or below the threshold required.

The prompt is, in effect, a call to action to do a specific behaviour. The prompt must be ‘loud’ enough for the target person to perceive it and be consciously aware of it. Once aware of a prompt, the target immediately, and largely unconsciously, assesses their ability to carry out the requested behaviour: how difficult would this be, how long will it take, who can help me, and so on. They base this on their perception of the difficulty of the requested behaviour, and their ability, as they see it, to achieve that behaviour.

 

From DSC:
I have had two instances recently where the phone-based systems (i.e., the Voice Response Units) haven’t worked…at all. They either wouldn’t let me do something as simple as updating my credit card number on file or checking on the status of a prescription. Human beings had to get involved to help me get the issues resolved. (Sounds a bit like the recent issues with the FAFSA forms, as I think about it.)

This is old hat, I know. This is common knowledge. But with AI, I’m increasingly concerned that the temptations are there for the MBAs/executives out there to lay off employees and boost their short-term profits (so that Wall Street will reward them and so that they can get their year-end bonuses).

The reminder/lesson for businesses and organizations of all types (including colleges and universities):

  • Unless you want to piss off and lose your customers, always allow your customers to stop using a VRU and go directly to a person that they can talk to.
  • Then empower those employees on the front lines as much as possible so that they can get the issues resolved for your customers.
  • Don’t think you are putting your MBA to good use by laying off your employees after you implement some new VRU system or AI-backed system. Don’t be too quick to think that you’re going to save all kinds of money by going with AI. This might be the case down the line, but I wouldn’t be too quick to get there yet. And even when you do get there, please allow us to talk to human beings.
 

Announcing the 2024 GSV 150: The Top Growth Companies in Digital Learning & Workforce Skills — from prnewswire.com with information provided by ASU+GSV Summit

“The world is adapting to seismic shifts from generative AI,” says Luben Pampoulov, Partner at GSV Ventures. “AI co-pilots, AI tutors, AI content generators—AI is ubiquitous, and differentiation is increasingly critical. This is an impressive group of EdTech companies that are leveraging AI and driving positive outcomes for learners and society.”

Workforce Learning comprises 34% of the list, K-12 29%, Higher Education 24%, Adult Consumer Learning 10%, and Early Childhood 3%. Additionally, 21% of the companies stretch across two or more “Pre-K to Gray” categories. A broader move towards profitability is also evident: the collective gross and EBITDA margin score of the 2024 cohort increased 5% compared to 2023.

See the list at https://www.asugsvsummit.com/gsv-150

Selected from 2,000+ companies around the world based on revenue scale, revenue growth, user reach, geographic diversification, and margin profile, this impressive group is reaching an estimated 3 billion people and generating an estimated $23 billion in revenue.

 

Skills-Based Hiring: The Long Road from Pronouncements to Practice— from burningglassinstitute.org

While headlines trumpet the demise of the college degree, our joint report with Harvard Business School Project on Managing the Future of Work reveals a different reality. Many companies have announced dropping degree requirements, but sustained hiring changes remain elusive for most. This report identifies where the reality of Skills-Based Hiring is lagging well-meaning ambitions, and shows which companies are getting it right.

 

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Per Donald Taylor this morning:

The results of this year’s L&D Global Sentiment Survey are now live online!

They are unlike anything else I’ve seen in the 11-history of the Survey.

Over 3,000 people from nearly 100 countries shared their views, and you can see my summary of them on LinkedIn:


 

 
© 2025 | Daniel Christian