- 1: The Edge is expanding, increasing demand for AI apps that run on any device
- 2: Heavy-duty compute clusters in the cloud are available via APIs.
- 5. Digital builders who compose AI services and iterate quickly will win.
Python has been synonymous with AI and Machine Learning tasks for years and remains the primary language of choice. The Python ecosystem is rich with powerful open-source libraries, such as TensorFlow, Keras, Pandas, and Numpy, purpose-built for tackling complex data processing and AI tasks.
1: The Edge is expanding, increasing demand for AI apps that run on any device
The Edge comprises smartphones, robots, autonomous lawnmowers, swarms of drones, sensors, and the Internet of Things. As more devices and users come online, the globe-spanning network of data and intelligence expands, requiring new control panels and data visualization dashboards.
2: Heavy-duty compute clusters in the cloud are available via APIs.
Cloud data centers can train and host the most powerful AI models.
The models we know today that help us code, reason through complex tasks, and generate writing, images, and audio upon request, can do so because they've been trained, and are running on, large clusters of highly specialized supercomputers — at tremendous cost.
For the foreseeable future, the cloud will likely remain home to the machinery required to support the ongoing development and effective operation of the AI capabilities we know today.
For everyone except the most prominent players in the tech market, it makes more sense to call upon cloud-based services as needed to perform GPU-intensive tasks like fine-tuning models or performing predictions with existing models.
How does one tap into these powerful behemoth machines on an as-needed basis? Via the third technology trend: powerful application programming interfaces (APIs).
APIs are potent building blocks of functionality that developers can combine to produce rich experiences without being a deep expert in every computer science discipline that was necessary to build the APIs in the first place.
It’s this abstraction layer that makes APIs so powerful. A developer must only understand the usage of APIs to harness advanced storage, image recognition, semantic search, logical reasoning, data crunching, and high-speed retrieval capabilities without reading a single research paper. APIs empower builders to compose rich experiences very quickly.
The worldwide developer community has proliferated decades of tooling, best practices, books, talks, documentation, and patterns, making API creation and consumption a bread-and-butter skill for almost every web developer working today.
Since APIs are the lingua franca of the Internet and its builders, you can see a decision, such as OpenAI’s, to make their powerful models available via API for what it is: a disruptive empowerment of software developers and their end users.
5. Digital builders who compose AI services and iterate quickly will win.
Forthcoming standout applications will compose pipelines of AI services to perform feats of productivity and intelligence not previously possible. Imagine combining computer vision APIs that identify objects in a satellite feed with an advanced Large Language Model (LLM) that can reason about the scene and a vector database like Pinecone’s to retrieve context from long-term memory.
Apps could scan for deadly wildfires as they’re beginning, look up emergency response assets geographically closest to the start of the fire, and route them around traffic to help humans squash a devastating fire before it reaches full strength, all while issuing alerts to the smartphones of everyone who lives in the area so that they can get their families and pets to safety.
These advanced solutions, and others we can’t yet conceive of, will belong to the digital builders who can most rapidly compose AI services, test locally, iterate, and deploy globally to all form factors - from smartphones to drone panels to electric vehicle dashboards.
Developer-facing tooling continues to receive tremendous investment from the open-source community and critical sponsors, and given the language’s enduring popularity and steadily increasing capability, we see no signs of this investment slowing down.
Is Python going away? No. Projects such as Mojo will improve the developer and deployment experience for the Python community.